Lucas Rizzo

Pierpaolo Dondio

Sarah Jane Delany

Luca Longo
email: luca.longo@dit.ie

Modeling mental workload via rule-based expert system: a comparison with NASA-TLX & Workload Profile

Keywords: Rule-based expert system, mental workload, heuristics

In the last few decades several fields have made use of the construct of human mental workload (MWL) for system and task design as well as for assessing human performance. Despite this interest, MWL remains a nebulous concept with multiple definitions and measurement techniques. State-of-the-art models of MWL are usually ad-hoc, considering different pools of pieces of evidence aggregated with different inference strategies. In this paper the aim is to deploy a rule-based expert system as a more structured approach to model and infer MWL. This expert system is built upon a knowledge-base of an expert and translates into computable rules. Different heuristics for aggregating these rules are proposed and they are elicited using inputs gathered in an user study involving humans performing web-based tasks. The inferential capacity of the expert system, using the proposed heuristics, is compared against the one of two ad-hoc models, commonly used in psychology: the NASA-Task Load Index and the Workload Profile assessment technique. In detail, the inferential capacity is assessed by a quantification of two properties commonly used in psychological measurement: sensitivity and validity. Results show how some of the designed heuristics can over perform the baseline instruments suggesting that MWL modelling using expert system is a promising avenue worthy of further investigation.

Introduction

Mental workload (MWL) is a multi-faceted phenomenon with no clear and widely accepted definition. Intuitively, it can be described as the amount of cognitive work expended to a certain task during a given period of time. However, this is a simplistic definition and other factors such as stress, time pressure and mental effort can all influence MWL [START_REF] Longo | Formalising Human Mental Workload as a Defeasible Computational Concept[END_REF]. The principal reason for measuring MWL is to quantify the mental cost of performing a task in order to predict operator and system performance [START_REF] Cain | A review of the mental workload literature[END_REF]. It is an important construct, mainly used in the fields of psychology and ergonomics, mainly with application in aviation and automobile industries [START_REF] Gartner | Pilot workload and fatigue: A critical survey of concepts and assessment techniques[END_REF][START_REF] Paxion | Mental workload and driving[END_REF] and in interface and web design [START_REF] Tracy | Measuring cognitive load to test the usability of web sites[END_REF][START_REF] Longo | The importance of human mental workload in web-design[END_REF][START_REF] Longo | On the relationship between perception of usability and subjective mental workload of web interfaces[END_REF]. According to Young and Stanton, underload and overload can weaken performance [START_REF] Young | Attention and automation: new perspectives on mental underload and performance[END_REF]. However, optimal workload has a positive impact on user satisfaction, system success, productivity and safety [START_REF] Longo | A defeasible reasoning framework for human mental workload representation and assessment[END_REF]. Often the information necessary for modelling the construct of MWL is uncertain, vague and contradictory [START_REF] Longo | A computational analysis of cognitive effort[END_REF]. State-of-the-art measurement techniques do not take into consideration the inconsistency of data used in the modelling phase, which might lead to contradictions and loss of information. For example, if the time spent on a certain task is low it can be derived that the overall MWL is also low, however, if the effort invested in the task is extremely high, then the contrary can be inferred. The aim of this study is to investigate the use of rule-based expert systems for the modelling and inference of MWL. An expert system is a computable program designed to model the problem-solving ability of a human expert [START_REF] Durkin | Expert systems: design and development[END_REF]. This human expert has to provide a knowledge base, then in turn is translated into computable rules. These rules are used by an inference engine aimed at inferring a numerical index of MWL. Since there is no ground truth indicating if such index is fully correct, the inferential capacity of the defined expert system needs to be investigated in order to gauge its quality. To solve this, the proposal is to adopt some of the most commonly used criteria used in psychometrics such as validity and sensitivity [START_REF] Eggemeier | Properties of workload assessment techniques[END_REF][START_REF] Tsang | Diagnosticity and multidimensional subjective workload ratings[END_REF][START_REF] Rubio | Evaluation of subjective mental workload: A comparison of swat, nasa-tlx, and workload profile methods[END_REF]. In simple terms, these criteria are aimed at assessing whether a technique is measuring the construct under investigation and whether it is capable of differentiating variations in workload. From this, the following research question can be defined: can implementations of rule-based expert systems, compared to state-of-the-art MWL inference techniques, enhance the modelling of mental workload according to sensitivity and validity?

The remainder of this paper is organised as follows: section 2 describes related works on MWL, its assessment techniques and provides a general view on rule-based expert systems. Section 3 presents the design of an experiment, the methodology adopted. Findings are discussed in section 4 while section 5 concludes our contribution and introduces future work.

Related work

Mental workload assessment techniques

As stated by several authors, there is no simple and agreed definition of mental workload [START_REF] Young | State of science: mental workload in ergonomics[END_REF][START_REF] Hart | Nasa-task load index (nasa-tlx); 20 years later[END_REF][START_REF] Paxion | Mental workload and driving[END_REF]. It is thought to be multidimensional and multifaceted, resulting from the aggregation of many different factors thus difficult to be uniquely defined [START_REF] Cain | A review of the mental workload literature[END_REF]. The basic intuition is that mental workload is the necessary amount of cognitive work for a person to accomplish a task over a period of time. Nevertheless, a large number of measures have been developed [29,[START_REF] Hart | Development of nasa-tlx (task load index): Results of empirical and theoretical research[END_REF] and practitioners have found measuring MWL to be useful [START_REF] Tsang | Mental workload measurement and analysis[END_REF]. Most empirical classification assessment procedures can be divided in three major categories [START_REF] O'donnell | Workload assessment methodology[END_REF]:

-Subjective measures: operators are required to evaluate their own MWL according to different rating scales or a set of questionnaires.

-Performance-based measures: these infer an index of MWL from objective notions of performance on the primary task, such as number of errors, completion time or reaction time to respond to secondary tasks. -Physiological measures: these infer a value of MWL according to some physiological response from the operator such as pupillary reflex or muscle activity.

Further details for each category can be found in [START_REF] Meshkati | Techniques in mental workload assessment[END_REF][START_REF] Gartner | Pilot workload and fatigue: A critical survey of concepts and assessment techniques[END_REF]. This study makes use of two of the subjective measures of MWL that have been largely employed for the last four decades [START_REF] Reid | The subjective workload assessment technique: A scaling procedure for measuring mental workload[END_REF][START_REF] Hart | Development of nasa-tlx (task load index): Results of empirical and theoretical research[END_REF][START_REF] Tsang | Diagnosticity and multidimensional subjective workload ratings[END_REF]. These are used as base-lines and are: NASA-Task Load Index (TLX) [START_REF] Hart | Development of nasa-tlx (task load index): Results of empirical and theoretical research[END_REF] and Workload Profile (WP) [START_REF] Tsang | Diagnosticity and multidimensional subjective workload ratings[END_REF].

The NASA-TLX is a multidimensional scale, initially developed for the use in the aviation industry. Its application has been spread across several different areas, such as automobile drivers, medical profession, users of computers and military cockpits. Also, it has achieved great importance and is considered a reference point for the development of new measures and models [START_REF] Hart | Nasa-task load index (nasa-tlx); 20 years later[END_REF]. NASA-TLX consists of six sub scales: mental demand, physical demand, temporal demand, frustration, effort and performance (Table 4, in the Appendix, questions 1-5 plus physical demand). The computation of an overall MWL index is made through a weighted average of these six dimensions d i quantified using a questionnaire. The weights w i are provided by the operator according to a comparison of each possible pair of the six dimensions, for example "which contributed more for the MWL: mental demand or effort?", "which contributed more for the MWL: performance or frustration?", giving a total of 15 preferences. The number of times each dimension is chosen defines its weight (equation 1).

The Workload Profile is another MWL assessment technique based on the Multiple Resource Theory (MRT) [START_REF] Wickens | Processing resources and attention[END_REF]. In contrast to the NASA-TLX, it is built upon 8 dimensions: perceptual/central processing, response processing, spatial processing, verbal processing, visual processing, auditory processing, manual responses and speech responses (Table 4, question 6-13). The operator is asked to rate the proportion of attentional resources, in the range 0 to 1, for each dimension, then summed. For comparison purpose, this sum is averaged (eq. 2).

TLX MWL = 6 i=1 d i × w i 1 15 (1) WP MWL = 8 i=1 d i (2)
According to [START_REF] Rubio | Evaluation of subjective mental workload: A comparison of swat, nasa-tlx, and workload profile methods[END_REF] WP is preferred to NASA-TLX if the goal is to compare the MWL of two or more tasks with different levels of difficulty, while NASA-TLX is preferred if the goal is to predict the performance of a particular individual in a single task. Several criteria have been proposed for the selection and development of measurement techniques [START_REF] O'donnell | Workload assessment methodology[END_REF]. In this study the focus is on two of them:

validity: to determine whether the MWL measurement instrument is actually measuring MWL. Two variations of validity are usually employed in psychology: concurrent and convergent. The former aims at determining to what extent a technique can explain objective performance measures, such as task execution time. The second indicates whether different MWL techniques correlate to each other [START_REF] Tsang | Diagnosticity and multidimensional subjective workload ratings[END_REF]. In literature, concurrent and convergent validity are calculated adopting statistical correlation coefficients [START_REF] Rubio | Evaluation of subjective mental workload: A comparison of swat, nasa-tlx, and workload profile methods[END_REF][START_REF] Longo | A defeasible reasoning framework for human mental workload representation and assessment[END_REF]. sensitivity: the capability of a technique to discriminate significant variations in MWL and changes in resource demand or task difficulty [START_REF] O'donnell | Workload assessment methodology[END_REF]. Formally, sensitivity has been assessed in two different ways: multiple regression [START_REF] Tsang | Diagnosticity and multidimensional subjective workload ratings[END_REF] and ANOVA [START_REF] Rubio | Evaluation of subjective mental workload: A comparison of swat, nasa-tlx, and workload profile methods[END_REF][START_REF] Longo | A defeasible reasoning framework for human mental workload representation and assessment[END_REF]. The aim was to identify statistically significant differences of the MWL indexes associated to each task under examination.

Mental workload and rule-based expert system

An expert system is a computer program created in order to emulate an expert in a given field [START_REF] Durkin | Expert systems: design and development[END_REF]. The goal is to imitate the experts capability of solving different tasks in its area. Unlike usual procedural algorithms, an expert system normally has two modules: a knowledge base and an inference engine. The knowledge base is provided by the expert and translated into a set of rules, which will be utilised by an inference engine. A typical rule is of the form "IF ... THEN ..." and the engine will elicit and aggregate all the rules in order to infer a conclusion. In [START_REF] Liao | Expert system methodologies and applicationsa decade review from 1995 to 2004[END_REF], a literature review of many areas in which expert systems have been applied is provided, while [START_REF] Hatzilygeroudis | Integrating (rules, neural networks) and cases for knowledge representation and reasoning in expert systems[END_REF][START_REF] Mitra | Knowledge representation in mickey: An expert system for designing microprocessor-based systems. Systems, Man and Cybernetics, Part A: Systems and Humans[END_REF] are examples of works in the more general field of knowledge representation. To the best of our knowledge, the only study that attempted to model MWL employing inference rules by Longo [START_REF] Longo | Formalising human mental workload as non-monotonic concept for adaptive and personalised web-design[END_REF]. Here, modelling MWL has been proposed as a defeasible reasoning process, which is a kind of reasoning built upon inference rules that are defeasible. Defeasible reasoning does not produce a final representation of MWL, but rather a dynamic representation that might change in the light of new evidence and rules. Following this approach, rule-based expert systems might be suitable complements because of their capacity to imitate the problem-solving ability of an expert and facilitate the justification of the inferred conclusion.

Design and methodology

In order to answer the research question an experiment is designed as it follows:

1. acquisition of a knowledge base (KB) related to MWL from an expert; 2. KB translation into different types of rule (forecast, undercutting, rebutting) 3. construction of models (e 1 -e 4 , f r 1 -f r 4 ) based on two variations of KB, each employing different types of rules and heuristics (H 1 , ..., H 4 ); 4. comparison of the inferential capacity of each model against selected baseline instruments (NASA-TLX and WP) according to validity and sensitivity:

validity is measured to investigate if the implemented rule-based expert system is capable of inferring MWL as well as the baseline instruments.

sensitivity is measured to determine the quality of the inference made by the implemented expert system.

Knowledge base (KB)

Research studies performed by Longo et al. have developed a knowledge base for the inference of MWL in the field of human computer interaction [START_REF] Longo | Formalising Human Mental Workload as a Defeasible Computational Concept[END_REF][START_REF] Longo | A defeasible reasoning framework for human mental workload representation and assessment[END_REF][START_REF] Longo | The importance of human mental workload in web-design[END_REF]. The goal was to investigate the impact of structural changes of web interfaces on the imposed mental workload on end-users after interacting with them. The knowledge base developed comprises by 21 attributes (Table 4), containing a set of features believed to be useful for modelling MWL, each of them quantified, through a subjective question, in the range [0, 100] ∈ R. The MWL has four possible levels, as per definition 1.

Definition 1 (Mental workload level) Four MWL levels are defined: underload (U), fitting -(F -), fitting + (F + ) and overload (O).

The set of rules built from the knowledge-base of the expert [START_REF] Longo | Formalising Human Mental Workload as a Defeasible Computational Concept[END_REF] can be seen in the Appendix and a formal definition follows.

Definition 2 (Rules) Three types of rules are defined.

-Forecast rule (FR): takes a value α of an attribute X and infers a MWL level

β if α is in a predefined range [x 1 , x 2 ] with x 1 , x 2 ∈ N and x 2 > x 1 . F R : IF α ∈ [x 1 ,
Y if α 1 ∈ [x 1 1 , x 1 2 ], • • • , α n ∈ [x n 1 , x n 2 ]
. In this case it is said that rule Y is discarded, d(Y), and will not be considered for future inferences of MWL.

U R : IF α 1 ∈ [x 1 1 , x 1 2 ] and • • • and α n ∈ [x n 1 , x n 2 ] THEN d(Y ) - Rebutting

Inference engine

Having defined the set of rules, the next step for inferring MWL is to implement an inference engine. Our inference engine starts with the activation of rules in the set of FR. These will be called activated rules. This activation is based on the inputs provided by the user. Afterwards, rules from the set of UR and RR might discard activated rules, solving some part of the contradictory information. This step is not compulsory. The implementation of rule-based expert systems without UR and RR is also provided. Activated rules that are not discarded are called surviving rules. After defining the set of surviving rules, there still might be some inconsistent inferences. Surviving rules will likely be inferring different MWL levels, even with the application of UR and RR. The expert system, therefore, must be able to aggregate the surviving rules and produce a final inference of MWL. Next an example follows:

Example 2 Following rules from Example 1 and given a numerical input it is possible to define the set of activated rules and the set of surviving rules. Example 2 illustrates a set of surviving rules inferring underload MWL (MD1, TD1) and overload MWL (PK1) at the same time. At this stage, a typical set of conflict resolutions strategies for expert systems include: deciding a priority for each rule, firing all possible lines of reasoning or choosing the first rule addressed. However, none of these strategies is applicable in our experiment, since there is no preference among rules, order of evaluation or possibility to compute more than one output. The knowledge base does not provide sufficient information for performing this computation and because of that four heuristics are defined to accomplish the aggregation of the surviving rules. The strategies are developed in order to extract different pieces of information from the surviving rules, which are aggregated or not in different fashions. The final MWL will be a value in the range [0, 100] ∈ R. Before presenting such heuristics it is necessary to define the value of a surviving rule (definition 3). Definition 3 (Surviving rule value) The value of a surviving rule r ∈ F R, with input 0 ≤ α ≤ 100 related to attribute X, is given by the function

f (r) = α, if X ∝ M W L 100 -α, if X ∝ 1 M W L with X ∝ M W L a direct relationship, X ∝ 1 M W L an inverse relationship 1 .
Given Definition 3 the following heuristics are designed:

-h 1 : the average of the surviving rules of the MWL level with the largest cardinality of surviving rules. In case of two or more levels with equal cardinality, it computes the mean of the averages. The idea is to give importance to the largest point of view (largest set of surviving rules) to infer MWL. -h 2 : the highest average value of the surviving rules for each MWL level. This is a pessimistic point of view, and infers the highest MWL according to the different sets of surviving rules of each MWL level. -h 3 : average value of all surviving rules. This is to give equal importance to all surviving rules, regardless of which level of MWL they were supporting. -h 4 : average of average of surviving rules of each MWL level. This is to give equal importance to all sets of MWL levels.

Data collection, elicitation of models and evaluation

Nine information seeking web-based tasks of varying difficulty and demand (Table 3), were performed by participants over three websites: Google, Wikipedia and Youtube. Two alterations of the interface of each web-site were proposed, having overall (9x2=18) configurations. 40 volunteers performed 9 tasks (on a random alteration) and after each, they answered each question of Table 4 using a paper-based scale in the range [0..100] ∈ ℵ, partitioned in 3 regions delimited at 33 and 66. Due to loss of data or partial completion of questionnaires, 406 instances were valid. Collected answers, for each instance, were used to elicit the rules of each model (section 3), aggregated with their heuristic, that in turn, produced an index of MWL, in the scale [0..100] ∈ . The outputs formed a distribution of MWL indexes, one for each model, and these were compared against the ones of the baseline models according to validity and sensitivity (fig. 1).

Baseline instruments

N ASA -T LX W P Mental workload inference

e1 e2 e3 e4 heuristics f r1 f r2 f r3 f r4
Expert system validity, sensitivity Fig. 1. Evaluation strategy schema

Validity

In line with other studies [START_REF] Rubio | Evaluation of subjective mental workload: A comparison of swat, nasa-tlx, and workload profile methods[END_REF][START_REF] Longo | A defeasible reasoning framework for human mental workload representation and assessment[END_REF], validity was assessed using correlation coefficients. In order to select the most suitable statistic, a test of the normality of the distributions of the MWL indexes, produced by each model, was performed using the Shapiro-Wilk test. This test did not achieve a significance greater than 0.05 for most of the models, underlying the non normality of data. As a consequence, the Spearman's rank-order correlation was selected.

Convergent validity: aimed at determining to what extent a model correlate with other model of MWL. As it can be seen from figure 2, the baseline instruments (NASA-TLX and WP) achieved a correlation of .538 (dashed reference line) with each other. When correlated with NASA-TLX, e 3 and f r 3 obtained a higher correlation than this. These two models both apply the heuristic h 3 , which is the average of all surviving rules, a similar computational method used by the baseline instruments. Just in two other cases (e 1 , f r 1 ) a good correlation (close to the reference line) with WP was obtained. These 2 models implement heuristic h 1 , which is the average of the surviving rules of the MWL level (set of rules) with the largest cardinality. The above 4 cases demonstrate how models can be built using rule-based expert system showing similar validity than other baseline MWL assessment instruments believed to shape the construct of MWL.

Concurrent validity: aimed at determining the extent to which a model correlate with task completion time (objective performance measure)2 . From figure 3, it is possible to note that even the baseline instruments do not have a high correlation with task completion time. The first dashed line represents the correlation of 0.178 between NASA-TLX and Time while the second represents the correlation of 0.119 between WP and Time. Similarly to convergent validity, the models applying heuristic h 3 (e 3 , and f r 3 ) plus the model e 2 were the ones that better correlated with task completion time, figure 3, over performing the NASA-TLX. Almost all the models over performed also the WP baseline. These findings suggest that computational models of MWL can be built as rule-based expert systems, and these are capable of enhancing the concurrent validity of the assessments when compared with state-of-the-art models.

Sensitivity

In line with other studies [START_REF] Rubio | Evaluation of subjective mental workload: A comparison of swat, nasa-tlx, and workload profile methods[END_REF][START_REF] Longo | A defeasible reasoning framework for human mental workload representation and assessment[END_REF], sensitivity was assessed by analysis of variance.

In particular, the non-parametric Kruskal-Wallis H test was performed over the MWL distributions generated by each model, and this was selected because some of the assumptions behind the equivalent of one-way ANOVA were not met. Only model e 4 was not capable of rejecting the null hypothesis of same distribution of MWL indexes across tasks (p < 0.01). This means that, for the other models, statistical significant differences exist. The Kruskal-Wallis H test, however, does not tell exactly which pairs of tasks are different from each other. As a consequence, post hoc analysis was performed and the Games-Howell test was chosen because of unequal variances of the distributions under analysis. Table 2 depicts how many pairs of tasks each model was capable of differentiating at different significance levels (p < 0.05 and p < 0.01). As is can be observed, models applying heuristic h 3 (f r 3 and e 3 ) outperformed the WP but underperformed the NASA-TLX. This result is a confirmation that sensitive mental workload rule-based expert systems can be successfully built and compete with existing benchmarks in the field.

Summary of findings

Quantifications of the validity and the sensitivity of developed models suggest that rule-based expert systems can be successfully built for mental workload modelling and assessment because their inferential capacity lies between the inferential capacity of two state-of-the-art assessment instruments, namely the Nasa Task Load Index and the Workload profile. However, here it is argued that these systems are more appealing and dynamic than selected state-of-the-art approaches. Firstly, they use rules built with terms that are closer to the way humans reason and that imitate experts problem-solving ability. Secondly, they embed heuristics for aggregating rules in a more dynamic way, with a better capacity of handling uncertainty and conflicting pieces of information compared to fixed formulas of state-of-the-art models. Thirdly, they allow the comparison of knowledge-bases and beliefs of different MWL designers thus increasing the understanding of the construct of Mental Workload itself.

Conclusion and future work

This research presents a new way of modelling and assessing the construct of Mental Workload (MWL) by means of rule-based expert systems. A knowledge base of a MWL designer was elicited and translated into computational rules of various typology. Different heuristics for aggregating these rules were designed aimed at inferring MWL as a numerical index. Inferred indexes were systematically compared with those generated by two state-of-the-art MWL assessment techniques: the NASA Task Load Index and the Workload Profile. This comparison included the quantification of two properties of each distribution of MWL indexes, namely sensitivity and validity, commonly employed in the literature. Findings suggest that rule-based expert systems are promising not only because they can approximate the inferential capacity of selected state-of-the-art MWL assessment techniques. They also offer a flexible approach for translating different knowledge-bases and beliefs of MWL designers into computational rules supporting the creation of models that can be replicated, extended and falsified, thus enhancing the understanding of the construct of mental workload itself. Future works will be focused on the replication of the approach adopted in this study using other knowledge bases elicited from other MWL experts. Additionally, this approach will be extended incorporating fuzzy representation of rules and acceptability semantics, borrowed from argumentation theory [START_REF] Dung | On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games[END_REF][START_REF] Longo | Defeasible reasoning and argument-based medical systems: an informal overview[END_REF], with the aim of improving conflict resolution of rules and building models expected to have an even higher sensitivity and validity.

2 ) 1

 21 rule (RR): is a relationship between two forecast rules, Y 1 and Y 2 , that can not coexist. RR : IF Y 1 and Y 2 THEN d(Y 1 ) and d(Y Example An example of possible rules are: -Forecast rules EF1: [IF effort ∈ [0, 32] THEN U] EF4: [IF effort ∈ [67, 100] THEN O] MD1: [IF mental demand ∈ [0, 32] THEN U] PK1: [IF past knowledge ∈ [0, 32] THEN O] -Undercutting rule DS1: [IF task difficulty ∈ [67, 100] and skills ∈ [67, 100] THEN d(EF4)] -Rebutting rule -r5: [IF PK1 and EF1 THEN d(PK1) and d(EF1)]

-

  Inputs: [effort = 80, past knowledge = 15, task difficulty = 90, mental demand = 20, skills = 70, temporal demand = 10] -Rules: Activated: [EF4, PK1, MD1, TD1, DS1] Discarded: [EF4] Surviving: [PK1, MD1, TD1]

Example 3 Following 2 = 15 , h 2 :

 32152 Example 2, the value of the surviving rules is given by f (P K1) = 85, f (M D1) = 20 and f (T D1) = 10. Finally, the overall MWL computed by each heuristic is: h 1 : 20+10 max

Fig. 2 .Fig. 3 .

 23 Fig. 2. Convergent validity: p < 0.05.

Table 1 .

 1 Experiments set up: types of rules employed by two variations of the same knowledge base (left) and name of each model, variation used, heuristic adopted (right).

	Types of rules	Knowledge base variations	Model	KB variation 1 2	Heuristics h1 h2 h3 h4
			e1
	Forecast		e2 e3
		1	e4
	Undercutting Rebutting	2	f r1 f r2 f r3 f r4

Table 2 .

 2 Sensitivity of MWL models with Games-Howell post hoc analysis. The maximum pairwise comparisons of 9 tasks is 9 2 = 36).

	Model	p < 0.05	p < 0.01	Model	p < 0.05	p < 0.01
	NASA-TLX	18	12	NASA-TLX	18	12
	WP	9	4	WP	9	4
	e1	2	1	f r1	2	0
	e2	5	3	f r2	4	1
	e3	13	10	f r3	17	10
	e4	0	0	f r4	4	1

Only the attributes past knowledge, skills and performance of Table4have an inverse relationship with MWL (the higher the answer the lower the MWL level) while the others have a direct relationship.

Due to measurement errors, only 281 instances have an associated time.

Acknowledgments

Lucas Middeldorf Rizzo would like to thank CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) for his Science Without Borders scholarship, proc n. 232822/2014-0.

Appendix

Knowledge base

For the attribute mental demand the forecast rules are: The same principle applies to the attributes temporal demand, physical demand, solving and deciding, selection of response, task and space, verbal material, visual resources, auditory resources, manual response, speech response, effort, parallelism, and context bias, forming 52 other rules. For psychological stress, motivation, past knowledge, skills and performance the forecast rules are:

The undercutting rules and rebutting rules are: Selection of response How much attention was required for selecting the proper response channel and its execution? (manual -keyboard/mouse, or speech -voice)

Task and space How much attention was required for spatial processing (spatially pay attention around you)?

Verbal material How much attention was required for verbal material (eg. reading or processing linguistic material or listening to verbal conversations)?

Visual resources How much attention was required for executing the task based on the information visually received (through eyes)?

Auditory resources How much attention was required for executing the task based on the information auditorily received (ears)?

Manual Response How much attention was required for manually respond to the task (eg. keyboard/mouse usage)?

Speech response How much attention was required for producing the speech response(eg. engaging in a conversation or talk or answering questions)?

Context bias

How often interruptions on the task occurred? Were distractions (mobile, questions, noise, etc.) not important (low context bias) or did they influence your task (high context bias)?

Past knowledge How much experience do you have in performing the task or similar tasks on the same website? The physical demand was considered 0 for all instances


Zohreh Akbari
email: zohreh.akbari@icb.uni-due.de

Rainer Unland
email: rainer.unland@icb.uni-due.de

Automated Determination of the Input Parameter of DBSCAN Based on Outlier Detection

Keywords: Clustering, DBSCAN, Empirical rule, Machine learning, Outlier detection, Parameter determination, Unsupervised learning

During the last two decades, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has been one of the most common clustering algorithms, that is also highly cited in the scientific literature. However, despite its strengths, DBSCAN has a shortcoming in parameter detection, which is done in interaction with the user, presenting some graphical representation of the data. This paper introduces a simple and effective method for automatically determining the input parameter of DBSCAN. The idea is based on a statistical technique for outlier detection, namely the empirical rule. This work also suggests a more accurate method for detecting the clusters that lie close to each other. Experimental results in comparison with the old method, together with the time complexity of the algorithm, which is the same as for the old algorithm, indicate that the proposed method is able to automatically determine the input parameter of DBSCAN quite reliably and efficiently.

Introduction

Machine Learning (ML) is one of the core fields of Artificial Intelligence (AI) and is concerned with the question of how to construct computer programs that automatically improve with experience [START_REF] Mitchell | Machine Learning[END_REF]. Depending on the nature of the learning data available to the learning system, machine learning methods are typically classified into three main categories [START_REF] Russell | Artificial Intelligence: A Modern Approach[END_REF][START_REF] Hertzmann | Machine Learning and Data Mining Lecture Notes[END_REF]: supervised, unsupervised and reinforcement learning. In supervised learning example inputs and their desired outputs are given and the goal is to learn a general rule that maps these inputs to their desired outputs. In unsupervised learning, on the other hand, no labels are given to the learning algorithm, leaving it on its own to find the hidden structure of the data, e.g. to look for the similarities between the data instances (i.e. clustering [START_REF] Shalev-Shwartz | Understanding Machine Learning: From Theory to Algorithm[END_REF]), or to discover the dependencies between the variables in large databases (i.e. association rule mining [START_REF] Ventura | Pattern Mining with Evolutionary Algorithms[END_REF]). In reinforcement learning the desired input/output pairs are again not presented, however, the algorithm is able to estimate the optimal actions by interacting with a dynamic environment and based on the outcomes of the more recent actions, while ignoring experiences from the past, that were not reinforced recently. This research focuses on the most common unsupervised learning method (i.e. cluster analysis [START_REF] Shalev-Shwartz | Understanding Machine Learning: From Theory to Algorithm[END_REF][START_REF] Murphy | Machine Learning: A Probabilistic Perspective[END_REF]), and more specifically on one of its successful algorithms the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [START_REF] Kriegel | A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[END_REF]. As mentioned above, in unsupervised learning, learner processes the input data with the goal of coming up with some summary or compressed version of the data [START_REF] Shalev-Shwartz | Understanding Machine Learning: From Theory to Algorithm[END_REF]. Clustering a dataset is a typical example of this type of learning. Clustering is the task of grouping a set of objects such that similar objects end up in the same group and dissimilar objects are diverted into different groups. Clearly, this description is quite imprecise and possibly ambiguous. However, quite surprisingly, it is not at all clear how to come up with a more rigorous definition [START_REF] Shalev-Shwartz | Understanding Machine Learning: From Theory to Algorithm[END_REF], and since no definition of cluster is widely accepted many algorithms have been developed to suit specific domains [START_REF] Estivill-Castro | A Fast and robust general purpose clustering algorithm[END_REF], each of which using a different induction principle [START_REF] Rokach | Chapter 15: Clustering Methods[END_REF].

Due to their diversity, clustering methods are classified into different categories in the scientific literature [START_REF] Rokach | Chapter 15: Clustering Methods[END_REF][START_REF] Berkhin | Survey of Clustering Data Mining Techniques[END_REF][START_REF] Han | Data Mining Concepts and Techniques[END_REF][START_REF] Everitt | Cluster Analysis[END_REF]. However, despite the slight differences between these classifications, they all mention the DBSCAN algorithm as one of the eminent methods available. DBSCAN owes its popularity to the group of capabilities it offers [START_REF] Kriegel | A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[END_REF]: (1) it does not require the specification of the number of clusters in the dataset beforehand, (2) it requires little domain knowledge to determine its input parameter, (3) it can find arbitrarily shaped clusters, ( 4) it has good efficiency on large datasets, ( 5) it has a notion of noise, and is robust to outliers, [START_REF] Murphy | Machine Learning: A Probabilistic Perspective[END_REF] it is designed in a way that it can be supported efficiently by spatial access methods such as R*-trees [START_REF] Beckmann | The R*-tree: An Efficient and Robust Access Method for Points and Rectangles[END_REF], and so on.

DBSCAN algorithm requires two input parameters, namely 𝐸𝑝𝑠 and 𝑀𝑖𝑛𝑃𝑡𝑠 , which are considered to be the density parameters of the thinnest cluster acceptable, specifying the lowest density which is not considered to be noise. These parameters are hence respectively the radius and the minimum number of data objects of the least dense cluster possible. The algorithm supports the user in determining the appropriate values for these parameters offering a heuristic method, which imposes the user interaction based on some graphical representation of the data (presented in section 2.2). However, since DBSCAN is sensitive to its input parameters and the parameters have significant influences on the clustering result, an automated and more precise method for the determination of the input parameters is needed. Some notable algorithms targeting this problem are: (1) GRPDBSCAN, which combines the grid partition technique and DBSCAN algorithm [START_REF] Darong | Grid-based DBSCAN Algorithm with Referential Parameters[END_REF], (2) DBSCAN-GM, that combines Gaussian-Means and DBSCAN algorithms [START_REF] Smiti | DBSCAN-GM: An improved clustering method based on Gaussian Means and DBSCAN techniques[END_REF], and (3) BDE-DBSCAN, which combines Differential Evolution and DBSCAN algorithms [START_REF] Karami | Choosing DBSCAN Parameters Automatically using Differential Evolution[END_REF]. Opposed to these methods, which all intend to solve the problem using some other techniques, this paper remains with the original idea of the DBSCAN algorithm and just tries to omit the user interaction needed, allowing the algorithm to detect the appropriate value itself. This is done using some basic statistical techniques for outlier detection. Two different approaches are mentioned in this paper, which apply the concept of standard deviation to the problem of outlier detection, namely the empirical rule for normal distributions and the Chebyshev's inequality for non-normal dis-tributions [START_REF] Black | Business Statistics: For Contemporary Decision Making[END_REF][START_REF] Ott | An Introduction to Statistical Methods and Data Analysis (7th Edition)[END_REF]. This work, however, focuses mainly on the application of the empirical rule to outlier detection in normal distributed data, and addresses the Chebyshev's inequality only as a possible solution for non-normal distributions.

The rest of the paper is organized as follows. Section 2 describes the DBSCAN algorithm and its supporting technique for the determination of its input parameters. In Section 3, the above mentioned statistical techniques for outlier detection are presented (i.e. the empirical rule and the Chebyshev's inequality). Section 4 describes the automated technique for the determination of the parameter 𝐸𝑝𝑠. Experimental results and the time complexity of the automated technique are then discussed in Section 5. Section 6 concludes with a summary and some directions for the feature researches.

DBSCAN: Density-Based Spatial Clustering of Applications with Noise

According to [START_REF] Kriegel | A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[END_REF], the key idea of DBSCAN algorithm is that for each point of the cluster the neighborhood of a given radius has to contain at least a minimum number of points, i.e. the density in the neighborhood has to exceed some threshold. The following definitions support the realization of this idea.

Definition 1: (𝐸𝑝𝑠 -𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟ℎ𝑜𝑜𝑑 of a point) The 𝐸𝑝𝑠 -𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟ℎ𝑜𝑜𝑑 of a point 𝑝, denoted by 𝑁 𝐸𝑝𝑠 (𝑝), is defined by 𝑁 𝐸𝑝𝑠 (𝑝) = {𝑞 ∈ 𝐷 | 𝑑𝑖𝑠𝑡(𝑝, 𝑞) ≤ 𝐸𝑝𝑠 }.
Definition 2: (directly density-reachable) A point 𝑝 is directly density-reachable from a point 𝑞, w.r.t. 𝐸𝑝𝑠 and 𝑀𝑖𝑛𝑃𝑡𝑠, if

𝑝 ∈ 𝑁 𝐸𝑝𝑠 (𝑞) and 2. |𝑁 𝐸𝑝𝑠 (𝑞) ≥ 𝑀𝑖𝑛𝑃𝑡𝑠|

The second condition is called core point condition (There are two kinds of points in a cluster, points inside of the cluster, called core points, and points on the border of the cluster, called border points). Definition 3: (density-reachable) A point 𝑝 is density-reachable from a point 𝑞, w.r.t. 𝐸𝑝𝑠 and 𝑀𝑖𝑛𝑃𝑡𝑠, if there is a chain of points 𝑝 1 , … , 𝑝 𝑛 , 𝑝 1 = 𝑞, 𝑝 𝑛 = 𝑝 such that 𝑝 𝑖+1 is directly density-reachable from 𝑝 𝑖 . Definition 4: (density-connected) A point 𝑝 is density-connected to a point 𝑞 , w.r.t. 𝐸𝑝𝑠 and 𝑀𝑖𝑛𝑃𝑡𝑠 , if there is a point 𝑜 such that both, 𝑝 and 𝑞 are densityreachable from 𝑜, w.r.t. 𝐸𝑝𝑠 and 𝑀𝑖𝑛𝑃𝑡𝑠. Definition 5: (cluster) Let 𝐷 be a database of points. A cluster 𝐶 , w.r.t. 𝐸𝑝𝑠 and 𝑀𝑖𝑛𝑃𝑡𝑠, is a non-empty subset of 𝐷 satisfying the following conditions:

1.  𝑝, 𝑞: 𝑖𝑓 𝑝  𝐶 and 𝑞 is density-reachable from 𝑝, w.r.t. 𝐸𝑝𝑠 and 𝑀𝑖𝑛𝑃𝑡𝑠 , then 𝑞  𝐶. (Maximality) 2.  𝑝, 𝑞  𝐶: 𝑝 is density-connected to 𝑞, w.r.t. 𝐸𝑃𝑆 and 𝑀𝑖𝑛𝑃𝑡𝑠. (Connectivity) Definition 6: (noise) Let 𝐶 1 , . . . , 𝐶 𝑘 be the clusters of the database 𝐷, w.r.t. parameters 𝐸𝑝𝑠 𝑖 and 𝑀𝑖𝑛𝑃𝑡𝑠 𝑖 , 𝑖 = 1, … , 𝑘. Then the noise is defined as the set of points in the database 𝐷 not belonging to any cluster 𝐶 𝑖 , i.e. 𝑛𝑜𝑖𝑠𝑒 = {𝑝 𝜖 𝐷 | 𝑖: 𝑝 𝐶 𝑖 ).

The following lemmata are important for validating the correctness of the algorithm. Intuitively, they state that having the parameters 𝐸𝑝𝑠 and 𝑀𝑖𝑛𝑃𝑡𝑠, a cluster can be discovered in a two-step approach. First, choose an arbitrary point from the database satisfying the core point condition as a seed. Second, retrieve all points that are density-reachable from the seed, obtaining the cluster containing the seed.

2.1

The Algorithm

The DBSCAN algorithm can be described as follows:

Table 1. Algorithm 1: Pseudo-code of the DBSCAN DBSCAN Algorithm (Input: 𝑫, 𝑬𝒑𝒔, 𝑴𝒊𝒏𝑷𝒕𝒔)

1. While (𝐷 has an unclassified1 point) 2. Select an arbitrary unclassified point 𝑝.

3. If 𝑝 does not satisfy the core point condition, mark it as a noise. 4. Else retrieve all the density-reachable points from 𝑁 𝐸𝑝𝑠 (𝑝) forming a cluster containing 𝑁 𝐸𝑝𝑠 (𝑝) and mark all the member of this cluster as classified. 5. End While

2.2

Determining the Parameters 𝑬𝒑𝒔 and 𝑴𝒊𝒏𝑷𝒕𝒔 DBSCAN offers a simple but effective heuristic method to determine the parameters 𝐸𝑝𝑠 and 𝑀𝑖𝑛𝑃𝑡𝑠 of the thinnest cluster in the dataset. For a given 𝑘 function 𝑘 -𝑑𝑖𝑠𝑡 is defined from the Database 𝐷 to the real numbers, mapping each point to the distance from its 𝑘 -𝑡ℎ nearest neighbor. When sorting the points of the dataset in descending order of their 𝑘 -𝑑𝑖𝑠𝑡 values, the graph of this function gives some hints concerning the density distribution in the dataset. This graph is called the sorted 𝑘 -𝑑𝑖𝑠𝑡 graph. It is clear that the first point in the first valley of the 𝑀𝑖𝑛𝑃𝑡𝑠 -𝑑𝑖𝑠𝑡 graph can be the threshold point with the maximal 𝑀𝑖𝑛𝑃𝑡𝑠 -𝑑𝑖𝑠𝑡 value in the thinnest cluster. All points with a larger 𝑀𝑖𝑛𝑃𝑡𝑠 -𝑑𝑖𝑠𝑡 value are considered to be noise, and all the other points are assigned to some clusters. DBSCAN states that according to experiments, the 𝑘 -𝑑𝑖𝑠𝑡 graphs for 𝑘 > 4 do not significantly differ from the 4 -𝑑𝑖𝑠𝑡 graph and, furthermore, they need considerably more computation. Therefore, it eliminates the parameter 𝑀𝑖𝑛𝑃𝑡𝑠 by setting it to 4 for all datasets (for 2-dimensional data). The parameter determination method also explains, that since in general, it is very difficult to detect the first valley of the 𝑘 -𝑑𝑖𝑠𝑡 graph automatically, but it is relatively simple for the user to see this valley in a graphical representation, it is suggested to follow an interactive approach for determining the threshold point.

Statistical Techniques for Outlier Detection

The term noise in DBSCAN algorithm is equivalent to an outlier in statistics, which is an observation that is far removed from the rest of the observations [START_REF] Maddala | Outliers[END_REF]. One of the basic statistical techniques for outlier detection is called the empirical rule. The empirical rule is an important rule of thumb, that is used to state the approximate percentage of values that lie within a given number of standard deviation from the 𝑚𝑒𝑎𝑛 of a set of data if the data are normally distributed. The empirical rule, also called the 68-95-99.7 rule or the three-sigma rule of thumb states that 68.27%, 95.45% and 99.73% of the values in a normal distribution lie within one, two and three standard deviations of the mean [START_REF] Black | Business Statistics: For Contemporary Decision Making[END_REF]. One of the practical usages of the empirical rule is as a definition of outliers as the data that fall more than three standard deviations from the norm in normal distributions [START_REF] Coolidge | Statistics: A Gentle Introduction[END_REF].

Fig. 1. The Empirical Rule [ 21]

If there are many points that fall more than three standard deviations from the norm, then the distribution is most likely non-normal. In this case, Chebyshev's inequality, which applies to non-normal distributions, is applicable. Chebyshev's inequality states that in any probability distribution, at least 1 - in 𝑘 standard deviations of the 𝑚𝑒𝑎𝑛 [START_REF] Black | Business Statistics: For Contemporary Decision Making[END_REF] (e.g. in non-normal distributions at least 99% of the values lie within 10 standard deviations of the 𝑚𝑒𝑎𝑛). Hence, using the Chebyshev's inequality, the outlier can also be defined as the data that fall outside an appropriate number of standard deviations from the mean [START_REF] Amidon | Data Outlier Detection using the Chebyshev Theorem[END_REF] 2 .

Automated Determination of the Parameter Eps

Setting the 𝑀𝑖𝑛𝑃𝑡𝑠 to 4, determining the parameter 𝐸𝑝𝑠, the algorithm is aiming a radius that covers the majority of the 4 -𝑑𝑖𝑠𝑡 values and stands well as a threshold for the specification of the noise values. As mentioned above, the term noise in DBSCAN algorithm is equivalent to an outlier in statistics, which is an observation that is far removed from the rest of the observations [START_REF] Maddala | Outliers[END_REF]. Thus, the idea here is to use statistical rules in order to find the threshold value between the accepted 4 -𝑑𝑖𝑠𝑡 values and the values considered for the noise points.

As mentioned above, one of the practical usages of the empirical rule is as a definition of outliers as the data that fall more than three standard deviations from the norm in normal distributions [START_REF] Coolidge | Statistics: A Gentle Introduction[END_REF]. Thus, considering the 4 -𝑑𝑖𝑠𝑡 values, the value of parameter 𝐸𝑝𝑠 can be set to their 𝑚𝑒𝑎𝑛 plus three standard deviations. This would cover even more than 99.73% of the calculated 4 -𝑑𝑖𝑠𝑡 values, since the 4 -𝑑𝑖𝑠𝑡 values smaller than 𝑚𝑒𝑎𝑛 -3 × 𝑆𝐷 are also covered here.

Border points and even in general, points closer to the border of the clusters usually have greater 𝑘 -𝑑𝑖𝑠𝑡 values, which lead to larger 𝐸𝑝𝑠 values and thus might cause two close clusters to be detected as one cluster (Since the parameter 𝑀𝑖𝑛𝑃𝑡𝑠 or 𝑘 is set to 4, this problem may be caused mostly by the border points). These relatively greater 𝑘 -𝑑𝑖𝑠𝑡 values, however, do not have any positive effect on the process of cluster detection, as the 𝑘 -𝑑𝑖𝑠𝑡 values of the core points are actually the ones forming the right clusters and at the same time covering the border points. Figure 2 shows a case in which the 4 -𝑑𝑖𝑠𝑡 value of border point 𝑝 is much larger than the 4 -𝑑𝑖𝑠𝑡 value of the core point 𝑞, which can actually cover 𝑝 in its 4 -𝑑𝑖𝑠𝑡 -𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟ℎ𝑜𝑜𝑑.

Fig. 2. 4 -𝑑𝑖𝑠𝑡 values for example core (𝑞) and border point (𝑝)

2 This work focuses solely on the empirical rule and the normal distributions. However, the possibilityofusingtheChebyshev'sinequalityisgivenhere,in order to show that the general idea of using outlier detection techniques for the reason of parameter determination in DBSCAN is not limited to the distribution of the data.

p q

In order to eliminate the negative effect of the 𝑘 -𝑑𝑖𝑠𝑡 values of the border points, the algorithm presented here considers any point with minimum 𝑘 -𝑑𝑖𝑠𝑡 value which covers the border point in its 𝑘 -𝑑𝑖𝑠𝑡 -𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟ℎ𝑜𝑜𝑑 and replaces the 𝑘 -𝑑𝑖𝑠𝑡 value of this border point with the 𝑘 -𝑑𝑖𝑠𝑡 value of this core point. Thus for a given 𝑘, function 𝑘 -𝑑𝑖𝑠𝑡𝑠ˊ is defined from the Dataset 𝐷 to the real numbers, mapping each point to the 𝑘 -𝑑𝑖𝑠𝑡 value of any core point, covering this point in its 𝑘 -𝑑𝑖𝑠𝑡 -𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟ℎ𝑜𝑜𝑑, with minimum 𝑘 -𝑑𝑖𝑠𝑡 value. Actually, following this technique, points are considered in ascending order of their 4 -𝑑𝑖𝑠𝑡 values, then taking each point 𝑝, if the 4 -𝑑𝑖𝑠𝑡ˊ value for any point in its four nearest neighbors is not set so far, this value will be set to the 4 -𝑑𝑖𝑠𝑡 value of point 𝑝. Using this technique for each point, the 𝑘 -𝑑𝑖𝑠𝑡 value of the smallest cluster, the point can join, would be considered. At the end the 𝑚𝑒𝑎𝑛 and the standard deviation of these 𝑘 -𝑑𝑖𝑠𝑡ˊ values which are saved for all points are calculated and the 𝐸𝑝𝑠ˊ value is set to 𝑚𝑒𝑎𝑛 + 3 × 𝑆𝐷. The following pseudo-code indicates this method. Table 2. Algorithm 2: Pseudo-code of the 𝐸𝑝𝑠𝐹𝑖𝑛𝑑𝑒𝑟 𝑬𝒑𝒔𝑭𝒊𝒏𝒅𝒆𝒓 (Input: 𝐷) 1. For each point 𝑝 find the four nearest neighbors. 2. Sort the points in ascending order of theirs 4 -𝑑𝑖𝑠𝑡 values. 3. Following the ascending order, take each point 𝑝 and if the 4 -𝑑𝑖𝑠𝑡ˊ value for any of its four nearest neighbors is not set so far, set this value to the 4 -𝑑𝑖𝑠𝑡 value of the point 𝑝. 4. Calculate the 𝑚𝑒𝑎𝑛 of the 4 -𝑑𝑖𝑠𝑡ˊ values: 𝑚𝑒𝑎𝑛 5. Calculate the standard deviation of the 4 -𝑑𝑖𝑠𝑡ˊ values: 𝑆𝐷 6. Set the 𝐸𝑝𝑠ˊ value to 𝑚𝑒𝑎𝑛 + 3 × 𝑆𝐷.

Experimental Results and Time Complexity

In this section the experimental results and the time complexity of the automated technique proposed in Section 4 (𝐸𝑝𝑠𝐹𝑖𝑛𝑑𝑒𝑟) are discussed.

Experimental Results and Discussions

In this section, the algorithm presented in Section 4 is applied to some datasets. This makes the comparison between the old method and the new automated method possible. All the experiments were performed on Intel(R) Celeron(R) CPU

Fig. 3. Sample datasets

In order to show the results of the clustering, each cluster is presented by a different shade of gray in Figure 4. Noise points are marked using black color. In order to illustrate the problem that may occur with the 𝑘 -𝑑𝑖𝑠𝑡 value of the border points (discussed in Section 4), dataset 5 is presented here (Figure 6). This dataset is defined in a way that nested and very close clusters are available in it.

4-distˊ points

It should be pointed out that even though the experiments presented here were all for 2-dimensional datasets, the idea can be applied to high-dimensional datasets as well. This is clearly possible, since the calculation of the distance between the points and the application of standard deviation remains the same for high-dimensional datasets. only point that must be considered is that, the DBSCAN has suggested 4 as the 𝑀𝑖𝑛𝑃𝑡𝑠 value just for 2-dimensional datasets. However, as mentioned before, 𝐸𝑝𝑠 and 𝑀𝑖𝑛𝑃𝑡𝑠 are the density parameters of the thinnest cluster; therefore it is always possible to determine the 𝐸𝑝𝑠 by keeping the 𝑀𝑖𝑛𝑃𝑡𝑠 parameter small enough (or even just by setting it to one). The diversity of the density may always be described with different radii containing a predefined number of points (𝑀𝑖𝑛𝑃𝑡𝑠).

Time Complexity

Since the algorithm needs to find the four nearest neighbors of each point in the dataset, the time complexity of the algorithm cannot be less than 𝑂(𝑛 2 ). Of course, since these points should have been also retrieved in the user interaction technique, and the only difference here is the calculation of the 𝑚𝑒𝑎𝑛 and the standard deviation, which can be done in 𝑂(𝑛), it is clear that the time complexity of the automated technique presented here, is the same as for the old method. Thus concerning the automated abilities of this technique, it is obvious that the application of this approach in the determination of the 𝐸𝑝𝑠 parameter is quite reasonable.

Conclusion

This paper proposes a simple and effective method to automatically determine the input parameter 𝐸𝑝𝑠 of DBSCAN. The work remains with the original idea of the DBSCAN algorithm and just tries to omit the user interaction needed, and allow the algorithm to detect the appropriate value itself. This is done using some basic statistical techniques for outlier detection. Two different approaches are mentioned here, which apply the concept of standard deviation to the problem of outlier detection, namely the empirical rule for normal distributions and Chebyshev's inequality for non-normal distributions. One of the practical usages of the empirical rule is as a definition of outliers as the data that fall more than three standard deviations from the norm in normal distributions. Thus, the value of parameter 𝐸𝑝𝑠 can be set to 𝑚𝑒𝑎𝑛 plus three standard deviations. This value would cover the majority of the 𝑘 -𝑑𝑖𝑠𝑡ˊ values and stands well as a threshold for the specification of the noise values. This work also mentioned the problem which occurs with the 𝑘 -𝑑𝑖𝑠𝑡 values of the border points, and suggests a more accurate method for the determination of the values, based on which 𝐸𝑝𝑠 is calculated (i.e. 𝑘 -𝑑𝑖𝑠𝑡ˊ values). Experimental results and the time complexity of the proposed algorithm suggest that the application of this technique in the determination of the 𝐸𝑝𝑠 parameter is quite reasonable. The concentration of this research was mainly on the application of the empirical rule to outlier detection in normal distributed data. The future works will have to consider the Chebyshev's inequality for possible non-normal distributions of 𝑘 -𝑑𝑖𝑠𝑡ˊ values.

Lemma 1 :

 1 Let 𝑝 be a point in 𝐷 and |𝑁 𝐸𝑝𝑠 (𝑝)| ≥ 𝑀𝑖𝑛𝑃𝑡𝑠 . Then the set 𝑂 = {𝑜 |𝑜  𝐷 𝑎𝑛𝑑 𝑜 𝑖𝑠 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 -𝑟𝑒𝑎𝑐ℎ𝑎𝑏𝑙𝑒 𝑓𝑟𝑜𝑚 𝑝, 𝑤. 𝑟. 𝑡. 𝐸𝑝𝑠 𝑎𝑛𝑑 𝑀𝑖𝑛𝑃𝑡𝑠} is a cluster, w.r.t. 𝐸𝑝𝑠 and 𝑀𝑖𝑛𝑃𝑡𝑠. Lemma 2: Let 𝐶 be a cluster, w.r.t. 𝐸𝑝𝑠 and 𝑀𝑖𝑛𝑃𝑡𝑠, and let 𝑝 be any point in 𝐶 with |𝑁 𝐸𝑝𝑠 (𝑝)| ≥ 𝑀𝑖𝑛𝑃𝑡𝑠 . Then 𝐶 equals to the set 𝑂 = {𝑜 |𝑜 𝑖𝑠 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 -𝑟𝑒𝑎𝑐ℎ𝑎𝑏𝑙𝑒 𝑓𝑟𝑜𝑚 𝑝, 𝑤. 𝑟. 𝑡. 𝐸𝑝𝑠 𝑎𝑛𝑑 𝑀𝑖𝑛𝑃𝑡𝑠}.

1 𝑘 2

 12 of the values are with-

Fig. 4 .Figure 5 Fig. 5 .

 455 Fig. 4. Detected clusters

5 Fig. 6 .

 56 Fig. 6. Dataset 5

Fig. 7 .

 7 Fig. 7. Different clustering results for dataset 5

Note that the term unclassified here indicates that it is not determined yet if the point is a noise or not.

Note that the larger difference between 𝐸𝑝𝑠 and 𝐸𝑝𝑠ˊ for Dataset 3 is caused by the larger difference between the

-𝑑𝑖𝑠𝑡ˊ values of those data instances considered as noise and the rest of the data instances. This difference has no effect on the clustering result, since 𝐸𝑝𝑠 and 𝐸𝑝𝑠ˊ are actually threshold values and since there are no data instances with 4 -𝑑𝑖𝑠𝑡ˊ values between 𝐸𝑝𝑠 and 𝐸𝑝𝑠ˊ, the clustering result would remain the same.


Fotis Kalaganis
email: kalaganis@csd.auth.grdadam@mus.auth.gr

Dimitrios A Adamos
email: d.adamos@ieee.org

Nikos Laskaris
email: laskaris@aiia.csd.auth.gr

A consumer BCI for Automated Music Evaluation within a popular on-demand Music Streaming Service ''Taking Listener's Brainwaves to Extremes''

Keywords: EEG, music evaluation, recommendation-systems, human machine interaction, Spotify

We investigated the possibility of a using a machine-learning scheme in conjunction with commercial wearable EEG-devices for translating listener's subjective experience of music into scores that can be used for the automated annotation of music in popular on-demand streaming services.

Based on the established -neuroscientifically sound-concepts of brainwave frequency bands, activation asymmetry index and cross-frequency-coupling (CFC), we introduce a Brain Computer Interface (BCI) system that automatically assigns a rating score to the listened song.

Our research operated in two distinct stages: i) a generic feature engineering stage, in which features from signal-analytics were ranked and selected based on their ability to associate music induced perturbations in brainwaves with listener's appraisal of music. ii) a personalization stage, during which the efficiency of extreme learning machines (ELMs) is exploited so as to translate the derived patterns into a listener's score. Encouraging experimental results, from a pragmatic use of the system, are presented.

Introduction

Until recently, electroencephalography (EEG) was exclusive to doctors' facilities and specialists' workplaces, where trained experts operated expensive devices. The vast majority of the research was dedicated to the diagnosis of epilepsy, sleep disorders, Alzheimer's disease as well as monitoring certain clinical procedures such as anesthesia. By the same token, the application of Brain-Computer Interfaces (BCIs) has so far been confined to neuroprosthetics and for building communication channels for the physically impaired people [START_REF] Niedermeyer | Electroencephalography: basic principles, clinical applications, and related fields[END_REF].

Recent advances in medical sensors offer commercial EEG headsets at affordable prices. The procedure of recording the electrical activity of the brain via electrodes on the human scalp is pretty simple nowadays and does not require any expertise [START_REF] Casson | Wearable Electroencephalography[END_REF]. The latter can lead to a wide variety of commercial applications in almost any aspect of everyday life. Meanwhile, many researchers are drawn into the exploration of brain patterns that are not related to healthcare [START_REF] Ariely | Neuromarketing: the hope and hype of neuroimaging in business[END_REF]. At the same time, the manufacturing of new portable neuroimaging devices favors innovation, as novel applications of noninvasive BCIs are anticipated within real-life environments [START_REF] He | Noninvasive brain-computer interfaces based on sensorimotor rhythms[END_REF].

Since the beginning of the twenty-first century, the digital revolution has radically affected the music industry and is continuously reforming the business model of music economy [START_REF]Business Innovation and Disruption in the Music Industry[END_REF]. Until now, previously-established channels of music distribution have been replaced and new industry stakeholders have emerged. Among them, on-demand music recommendation and streaming services emerge as the ''disruptive innovators'' [START_REF] Downes | Big Bang Disruption[END_REF] of the new digital music ecosystem.

In our previous work [START_REF] Adamos | Towards the bio-personalization of music recommendation systems: a single-sensor EEG biomarker of subjective music preference[END_REF], we presented our vision for the integration of bio-personalized features of musical aesthetic appreciation into modern music recommendation systems to enhance user's feedback and rating processes. With the current work, we attempted the first step toward implementation with the realization of an automated music evaluation process, that is performed in nearly real-time by decoding aesthetic brain responses during music listening and feeding them back to a contemporary music streaming service (i.e. Spotify) as listener's ratings. We aimed for a flexible BCI system that could easily adapt to new users and, after a brief training session, would reliably predict the listener's ratings about the listened songs.

To facilitate convenience and friendliness of the user's experience, the recording of brain activity was implemented using a modern commercial wireless EEG device. Thus, we first adapted our approach to the device capabilities, investigating which combinations of available brainwave descriptors and electrode sites are reliably and consistently reflecting the listeners' evaluation about the music being played. Then we synthesized these descriptors into a composite biomarker, common for all listeners. Finally, we examined different learning machines that could incorporate this biomarker and generalize efficiently from a very small set of training examples (paired biomarkerpatterns and ratings).

The proposed music-evaluation BCI relies on standard ELMs for translating a particular set of readily-computable signal descriptors, as extracted from our 4-channel wearable EEG device, into a single numbered score expressing the appraisal of music within the range [START_REF] Niedermeyer | Electroencephalography: basic principles, clinical applications, and related fields[END_REF][START_REF] Casson | Wearable Electroencephalography[END_REF][START_REF] Ariely | Neuromarketing: the hope and hype of neuroimaging in business[END_REF][START_REF] He | Noninvasive brain-computer interfaces based on sensorimotor rhythms[END_REF][START_REF]Business Innovation and Disruption in the Music Industry[END_REF]. It can be personalized with very limited amount of training and runs with negligible amount of delay. In all our experimentations the passive listening paradigm was followed, since this is closer to the real life situation where someone enjoys listening to music.

The preliminary results, reported in this paper, include evidence about the existence of a robust set of brain activity characteristics that reliably reflect a listener's appraisal. Moreover, the effectiveness of ELMs in the particular regression task is established, by comparing its performance with alternative learning machines. Overall, the outcomes of this work are very encouraging for conducting experiments about music perception in real-life situations and embedding brain signal analysis within the contemporary technological universe.

The remaining paper is structured as follows. Section 2 serves as an introduction to EEG and its role in describing and understanding the effects of music. Section 3 outlines the essential tools that were employed during data analysis. Section 4 describes the experimental setup and the adopted methodology for analyzing EEG data. Section 5 is devoted to the presentation of results, while the last section includes a short discussion about the limitations of this study and its future perspectives.

Electroencephalography and Music perception studies

Electroencephalogram is a recording of the electrical changes occurring in the brain, produced by placing electrodes on the scalp and monitoring the developed electrical fields. EEG reflects mainly the summation of excitatory and inhibitory postsynaptic potentials at the dendrites of ensembles of neurons with parallel geometric orientation. While the electrical field produced by distinct neurons is too weak to be recorded with surface EEG electrode, as neural action gets to be synchronous crosswise a huge number of neurons, the electrical fields created by individual neurons aggregate, resulting to effects measurable outside the skull [START_REF] Cohen | Analyzing Neural Time Series Data: Theory and Practice[END_REF].

The EEG brain signals, also known as brainwaves, are traditionally decomposed (by means of band-pass filtering or a suitable transform) and examined within particular frequency bands, which are denoted via greek letters and in order of increasing central frequency are defined as follows: δ (0.5-4)Hz, θ (4-8)Hz, α (8-13)Hz, β (13-30)Hz, γ(>30)Hz. EEG is widely recognized as an invaluable neuroimaging technique with high temporal resolution. Considering the dynamic nature of music, EEG appears as the ideal technique to study the interaction of music, as a continuously delivered auditory stimulus, with the brainwaves. For more than two decades neuroscientists study the relationship between listening to music and brain activity from the perspective of induced emotions [START_REF] Altenmüller | Cortical DC-potentials as electrophysiological correlates of hemispheric dominance of higher cognitive functions[END_REF][START_REF] Petsche | EEG Coherence and Musical Thinking[END_REF][START_REF] Birbaumer | Perception of music and dimensional complexity of brain activity[END_REF]. More recently, a few studies appeared which shared the goal of uncovering patterns, lurked in brainwaves, that correspond to subjective aesthetic pleasure caused by music [START_REF] Hadjidimitriou | Toward an EEG-Based Recognition of Music Liking Using Time-Frequency Analysis[END_REF][START_REF] Schmidt | Resting frontal EEG alpha-asymmetry predicts the evaluation of affective musical stimuli[END_REF].

Regarding music perception, the literature has reported a wide spectrum of changes in the ongoing brain activity. This includes a significant increase of power in β-band over posterior brain regions [START_REF] Nakamura | Analysis of music-brain interaction with simultaneous measurement of regional cerebral blood flow and electroencephalogram beta rhythm in human subjects[END_REF]. An increase in γ band, which was confined to subjects with musical training [START_REF] Bhattacharya | Musicians and the gamma band: a secret affair?[END_REF], an asymmetrical activation pattern reflecting induced emotions [START_REF] Schmidt | Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions[END_REF] and an increase of frontal midline θ power when contrasting pleasant with unpleasant musical sounds [START_REF] Sammler | Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music[END_REF].

Regarding the particular task of decoding the subjective evaluation of music from the recorded brain activity, the role of higher-frequency brainwaves has been identified [START_REF] Hadjidimitriou | EEG-Based Classification of Music Appraisal Responses Using Time-Frequency Analysis and Familiarity Ratings[END_REF], and in particular the importance of γ-band brainwaves recorded over forebrain has been reported [START_REF] Pan | Common frequency pattern for music preference identification using frontal EEG[END_REF]. More recently, a relevant CFC biomarker based on the concept of nested oscillations in the brain was introduced for the assessment of spontaneous aesthetic brain responses during music listening [START_REF] Adamos | Towards the bio-personalization of music recommendation systems: a single-sensor EEG biomarker of subjective music preference[END_REF]. The reported experimental results indicated that the interactions between β and γ oscillations, as reflected in the brainwaves recorded over the left prefrontal cortex, are crucial for estimating the subjective aesthetic appreciation of a piece of music.

Methods

This section presents briefly the methodological elements employed in the realization of the proposed framework. More specifically, instantaneous signal energy, activation asymmetry index and a CFC estimator were used to derive brainwave descriptors (i.e. features reflecting neural activity associated with music listening). The importance of each descriptor (or combination of descriptors) was evaluated using Distance Correlation. ELMs, an important class of artificial neural network (ANNs), were exploited to convert the derived patterns into scores that represented the listener's appraisal.

Signal Descriptors

Brainwaves are often characterized by their prominent frequency and their (signal) energy content. Here, we adopted a quasi-instantaneous parameterization of Brainwaves content, by means of Hilbert transform. The signal from each sensor x(t), was first filtered within the range corresponding to the frequency-band of a brain rhythm (like δ-rhythm) and the envelope of the filtered activity αrhythm(t) was considered as representing the momentary strength of the associated oscillatory activity. Apart from the amplitude of each brain rhythm, its relative contribution was also derived by normalizing with the total signal strength (summed from all brain rhythms).

In neuroscience research, activation refers to the change in EEG activity in response to a stimulus and is of great interest to investigate differences in the way the two hemispheres are activated [START_REF] Coan | Frontal EEG asymmetry as a moderator and mediator of emotion[END_REF]. To this end, an activation asymmetry index was formed by combining measurements of activation strength from two symmetrically located sensors, i.e. AI(t)= left αrhythm(t)-right αrhythm(t). The normalized version of this index was also employed as an additional alternative descriptor.

A third brainwaves' descriptor was based on the CFC concept, which refers to the functional interactions between distinct brain rhythms [START_REF] Canolty | The functional role of cross-frequency coupling[END_REF]. A particular estimator was employed [START_REF] Dimitriadis | A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses[END_REF] that quantified the dependence of amplitude variations of a high-frequency brain rhythm on the instantaneous phase of a lower-frequency rhythm (a phenomenon known as phase-amplitude coupling (PAC)). This estimator operated on each sensor separately and used to investigate all the possible PAC couplings among the defined brain rhythms.

It is important to notice here, that the included descriptors were selected so as to cover different neural mechanisms and share a common algorithmic framework. Their implementation -and mainly their integration-within a unifying system did not induce time delays unreasonable for the purposes of our real-time application.

Distance Correlation

In statistics and in probability theory, distance correlation is a measure of statistical dependence between two random variables or two random vectors of arbitrary, not necessarily equal, dimension. Distance Correlation, denoted by R, generalizes the idea of correlation and holds the important property that R(X,Y)=0 if and only if X and Y are independent. R index satisfies 0≤ R ≤1 and, contrary to Pearson's correlation coefficient, is suitable for revealing non-linear relationships [START_REF] Szekely | Measuring and testing dependence by correlation of distances[END_REF]. In this work, it was the core mechanism for identifying neural correlates of subjective music evaluation, by detecting associations between the results of signal descriptors (or combinations of them) and the listener's scores.

Learning Machines

Machine learning deals with the development and implementation of algorithms that can build models able to generalize a particular function from a set of given examples. Regression is a supervised learning task that machine learning can handle with efficiency of similar, or even higher, level than the standard statistical techniques, andmainly-without imposing hypotheses. In this work, the decoding of subjective music evaluation was cast as a (nonlinear) regression problem. A model was then sought (i.e. learned from the experimental data) that would perform the mapping of patterns derived from the brain activity descriptors to the subjective music evaluation.

ELMs appeared as a suitable choice due to their documented ability to handle efficiently difficult tasks without demanding extensive training sessions [START_REF] Huang | Extreme learning machine: Theory and applications[END_REF]. They are feedforward ANNs with a single layer of hidden nodes, where the weights connecting inputs to hidden nodes are randomly assigned and never updated [START_REF] Huang | What are Extreme Learning Machines? Filling the Gap between Frank Rosenblatt's Dream and John von Neumann's Puzzle[END_REF].

Implementation and Experiments

Our experimentations evolved in two different directions. First, a set of experiments were run so as to use the experimental data for establishing the brainwave pattern that would best reflect the music appraisal of an individual and train the learning machine from music pieces of known subjective evaluation. Next, additional experiments were run that implemented the real-time scoring by the trained ELM-machine so as to justify the proposed BCI in a more naturalistic setting. In both cases, the musical pieces were delivered through a popular on-demand music streaming service (Spotify) that facilitates the registration of the listener's feedback ('like'-'dislike') to adapt the musical content to his taste and make suggestions about new titles.

Participants

All 5 participants were healthy students of AUTH Informatics department. Their average age was 23 years and music listening was among their daily habits. They signed an informed consent after the experimental procedures had been explained to them.

Data Acquisition

Having in mind the user-friendliness of the proposed scheme, we adopted a modern commercial dry-sensor wireless device (i.e. Interaxon's Muse devicehttp://www.choosemuse.com) in our implementations. This "gadget" offers a 4-channel EEG signal, with a topological arrangement that can be seen in Fig1. The signals are digitized at the sampling frequency of 220 Hz. Data are transmitted under OSC, which is a protocol for communication among computers, sound synthesizers, and other multimedia devices that is optimized for modern networking technology [START_REF] Wright | Open Sound Control: state of the art[END_REF].

Experimental Procedure

Prior to placing the headset, subjects sat in a comfortable armchair and volume of speakers was set to a desirable level. They were advised to refrain from body and head movements and enjoy the music experience. Recording was divided into sessions of 30 minutes duration. The music streaming service was operated in radio mode, hence randomly selected songs (from the genre of their preference), were delivered to each participant while his/her brain activity was registered. Among the songs there were advertisements. That part of recordings was isolated from the rest. The recording procedure was integrated, in MATLAB, together with all necessary information from the streaming audio signal (i.e. song id, time stamps for the beginning and termination of each song). Participants evaluated each of the listened song, using as score one of the integers {1,2,3,4,5}, during a separate session just after the end of the recording. These scores, together with the associate patterns extracted from the recorded brain signals, comprised our training set. The overall procedure was repeated for every subject, in order to build an independent testing set.

Data Analysis

The preprocessing of signal included 50Hz component removal (by a built-in notch filter in MuseIO -the software that connects to and streams data from Muse), DC offset removal, and removal of the signal segments that corresponded to the 5 first second of each song (in order to avoid artificial transients).

The digital processing included i) band-pass filtering for deriving the brainwaves of standard brain rhythms, ii) Hilbert Transform for deriving their instantaneous amplitude and phase and iii) computation of the descriptors described in section 3.1. To increase frequency resolution, we divided the β rhythm into βlow [START_REF] Schmidt | Resting frontal EEG alpha-asymmetry predicts the evaluation of affective musical stimuli[END_REF][START_REF] Nakamura | Analysis of music-brain interaction with simultaneous measurement of regional cerebral blood flow and electroencephalogram beta rhythm in human subjects[END_REF][START_REF] Bhattacharya | Musicians and the gamma band: a secret affair?[END_REF][START_REF] Schmidt | Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions[END_REF][START_REF] Sammler | Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music[END_REF][START_REF] Hadjidimitriou | EEG-Based Classification of Music Appraisal Responses Using Time-Frequency Analysis and Familiarity Ratings[END_REF][START_REF] Pan | Common frequency pattern for music preference identification using frontal EEG[END_REF][START_REF] Coan | Frontal EEG asymmetry as a moderator and mediator of emotion[END_REF], and βhigh (20-30 Hz) sub-bands and derived descriptors separately. Similarly the γ rhythm was divided into γlow (30-49Hz) and γhigh (51-90Hz). The recorded brain activity was segmented into overlapping windows of fixed duration (that during off-line experimentation had been varied between 30 and 100 seconds). The overall set of descriptors associated with the segments of brain activity had been used for selecting the best combination in a training-phase (applied collectively to all participants). A particular subset of descriptors (treated as a composite pattern including all the selected features) was extracted from each segment and utilized in an additional training phase (applied individually), during which an ELM-model was tailored to the user. In the testing phase, the subject specific ELM-model was applied to streaming composite patterns (representing segments of brain signals), in order to predict the listener's evaluation.

Results

Selecting Features -The synthesized biomarker

Feature selection was based on the Distance Correlation scores R's of all signal descriptors as averaged across all participants (Fig. 2). The descriptors were ranked in descending order (regarding their music evaluation expressiveness) and a dynamic programming methodology was applied. Starting with the feature of highest R, we traversed systematically the ranked list for combinations that would eventually maximize the Distance Correlation. This procedure led to a particular combination of 5 features, the synthesized music appraisal biomarker, which included the normalized temporal asymmetry index in βlow band, relative energy of α band at temporal electrodes, γlowγhigh PAC at FP1 sensor and relative energy of θ band at TP9. The relevance of this descriptor to music evaluation showed a dependence on the length of segment based on which it had been evaluated (using the timecourses from all participants, the averaged trace of Fig. 3 was computed). It can be seen that the effectiveness of the biomarker constantly increases with the duration of the listened music. This kind of investigations can provide indications about how fast the automated music evaluation system could operate.

Designing the ELM

Using the common biomarker, an ELM was designed per listener. The number of neurons in the hidden player, the only parameter to be tuned in ELMs, was selected as the smallest number of neurons such that the training and testing error were converging at an acceptable level, lower than 0.01.

Evaluating the music-evaluation BCI

The proposed BCI was evaluated in two ways. During the first stage, the available data (the biomarker patterns of each of the five participants along with the associated ratings) were randomly partitioned in training and testing set, in a 60%-40% proportion for cross-validation. An ELM was trained using the former set and its performance was quantified using the latter set. The overall procedure was repeated 10 times, and averaged results are reported. The normalized root mean squared error (RMSE), as defined for regression tasks, was found to be 0.063±0.0093 (mean±std across participants). For comparison purposes, we also employed Support Vector Machines (SVMs), which performed slightly inferiorly. Although the difference was marginal, the very short training time was another factor in favor of employing ELMs.

During the second stage, a pilot online BCI was developed in order to realize the testing phase of the ELM in a realistic setting. Two of the previous subjects, participated in an additional recording session during which the already tailored ELMs were providing, based on segments of 90 seconds, a read-out of the subjective music evaluation. The predicted scores were registered and compared with the ones provided by the listeners just after the experimental session. The normalized RMSE was estimated to be 0.09 and 0.07.

Discussion

This paper constitutes the report from a pilot study, during which we attempted to associate the listener's brainwaves with the subjective aesthetic pleasure induced by music. The main novelty of the presented proof-of-concept, is that it was realized based on a modern consumer EEG device and in conjunction with a popular on-demand music streaming service (Spotify). Our results indicate that signals from a restricted number of sensors (located over frontal and temporal brain areas) can be combined in a computable biomarker reflecting the listener's subjective music evaluation. This brainwaves' derivative can be efficiently decoded by regression-ELM and therefore leads to a reliable readout from the listener. The main advantage of the approach is that complies with idea of employing EEG-wearables in daily activities and is readily embedded within the contemporary on-demand music streaming services (see Fig. 4). However, the problem of artifacts (noisy signals of biological origin) has not been addressed yet. For the presented results, the participants had been asked to limit body/head movements and facial expressions and as much as possible. Hence, before employing such a system to naturalistic recordings, methodologies for real-time artifact suppression (as in [START_REF] Akhtar | Recursive independent component analysis for online blind source separation[END_REF]) have to be incorporated.

Today, we live in the world of Internet of Things (IoT) where the interconnectedness among devices has already been anticipated and supportive technologies are now being realized [START_REF] Want | Enabling the internet of things[END_REF]. However, to achieve a seamless integration of technology in people's lives, there is still much room for improvement. In a world beyond IoT, technology would proactively facilitate people's expectations and wearable devices would transparently interface with systems and services. To enable such scenarios would require to move from the IoT to the Internet of People (IoP) [START_REF] Miranda | From the Internet of Things to the Internet of People[END_REF]. People would then participate as first-class citizens relishing the benefits of holistic human-friendly applications. As such, we present a pragmatic use of a consumer BCI that ideally fits in the anticipated forms of the digital music universe and demonstrate a first series of encouraging experimental results.

Fig. 1 .

 1 Fig. 1. Topological arrangement of Electrodes (A) and Interaxon's Muse headset (B).

Fig. 2 .

 2 Fig. 2. Distance Correlation R of all employed signal descriptors.

Fig. 3 .

 3 Fig.3. Distance Correlation R as a function of the segment length for the synthesized biomarker.

Fig. 4 .

 4 Fig. 4. Flow chart of the proposed music evaluation BCI