Étienne Fournier
email: etienne.fournier@univ-grenoble-alpes.fr

Dorilys Kilgus
email: dorilys.kilgus@orange.fr

Aurélie Landry
email: aurelie.landry@univ-grenoble-alpes.fr

Belal Hmedan
email: belal.hmedan@univ-grenoble-alpes.fr

Damien Pellier
email: damien.pellier@imag.fr

Humbert Fiorino
email: humbert.fiorino@imag.fr

Christine Jeoffrion
email: christine.jeoffrion@univ-grenoble-alpes.fr

The impacts of human-cobot collaboration on perceived cognitive load and usability during an industrial task: an exploratory experiment

Keywords: collaborative robot, UX, Industry 4.0, human-machine interface, efficiency, cognitive load, usability

Since cobots (collaborative robots) are increasingly being introduced in industrial environments, being aware of their potential positive and negative impacts on human collaborators is essential. This study guides occupational health workers by identifying the potential gains (reduced perceived time demand, number of gestures and number of errors) and concerns (the cobot takes a long time to perceive its environment, which leads to an increased completion time) associated with working with cobots. In our study, the collaboration between human and cobot during an assembly task did not negatively impact perceived cognitive load, increased completion time (but decreased perceived time demand), and decreased the number of gestures performed by participants and the number of errors made. Thus, performing the task in collaboration with a cobot improved the user's experience and performance, except for completion time, which increased.

This study opens up avenues to investigate how to improve cobots to ensure the usability of the human-machine system at work.

Introduction

Collaborative robots (cobots) are increasingly popular in industrial workplaces because they are perceived as a means to enhance performance, particularly in future industrial workflows that routinely integrate new technologies into production chains (INRS, 2018). The term "cobot", created by Colgate & collaborators (1996), refers to "a robotic device which manipulates objects in collaboration with a human operator" (p. 1). The development of artificial intelligence offers cobots adaptive qualities: adaptations to production dynamics, to technical variabilities [START_REF] Swamidass | Manufacturing in variability[END_REF], and to the operator. Yet the artificial algorithms remain at an experimental level and need to be improved and evaluated before implanting cobots with adaptive capabilities into industry. Human-cobot interactions can take on several forms. In this report, we will only discuss direct collaboration (working simultaneously on a task). To accept this collaboration, it is essential to study the impacts of the introduction of collaborative robots on operators before implementation [START_REF] Kildal | Potential users' key concerns and expectations for the adoption of cobots[END_REF].

In many studies, the human-robot interaction experience is referred to as "user experience" or "UX" [START_REF] Hassenzahl | User experience (UX): Towards an experiential perspective on product quality[END_REF]. This experience can be studied through the concept of usability [START_REF] Bevan | What is the difference between the purpose of usability and user experience evaluation methods[END_REF][START_REF] Davis | Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology[END_REF][START_REF] Dubey | Analytical roadmap to usability definitions and decompositions[END_REF]INRS, 2018;[START_REF] Nielsen | Usability Engineering[END_REF]. Indeed, studying a system's usability is a way of guiding its design [START_REF] Chaniaud | Effect of Prior Health Knowledge on the Usability of Two Home Medical Devices: Usability Study[END_REF]. According to ISO 9241-11, a standard reference defining human-machine interaction [START_REF] Bevan | ISO 9241-11 Revised: What Have We Learnt About Usability Since[END_REF], usability is defined as "the extent to which a product can be used by specified users to accomplish specific goals with effectiveness, efficiency and satisfaction in a particular context of use" (ISO, 2018). Effectiveness is the accuracy and degree of completion with which users will accomplish set goals (ISO, 2018) and is measured through the quality of the results, the quantity produced, and the percentage of goals accomplished. In this study, we use the number of errors to measure effectiveness. Indeed, this gives a behavioral measure of cognitive load, since [START_REF] Verhulst | Contribution de l'étude de l'interaction en environnement virtuel: Intérêt de la charge mentale[END_REF] defined cognitive load as the amount of cognitive resources expended at a given time on a task.

Moreover, there are various measures for measuring cognitive load: subjective and objective (Cain, 2007b;[START_REF] Kramer | Physiological Metrics of Mental Workload: A Review of Recent Progress[END_REF]. Objective measures can be obtained by studying individual performance on the task [START_REF] Paas | Cognitive Load Measurement as a Means to Advance Cognitive Load Theory[END_REF]: if a task is too onerous in cognitive resources, the individual performing this task will no longer be able to provide all the necessary resources and consequently their performance will decrease. Efficiency is the degree to which the user performs the task easily with a minimum of resources (ISO, 2018). We use task completion time and the number of gestures to measure efficiency in our study. Finally, satisfaction is defined as "the extent to which the user's physical, cognitive and emotional responses resulting from using a system meets the user's needs and expectations" (ISO, 2018). The perceived cognitive load, an indicator of the user's cognitive response as a result of using a system, is used as a measure of the satisfaction dimension of usability. Subjective measures of cognitive load can be obtained through questionnaires where participants are asked questions directly. In this study, we use the NASA-TLX [START_REF] Hart | Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research[END_REF], which measures workload as a whole but also more specifically cognitive load, physical load, temporal demands frustration, effort, and performance [START_REF] Hart | NASA-task load index[END_REF]. Also, we consider that usability and cognitive load are two theoretical fields that could be linked to measure UX as a whole.

We hypothesized that a human-robot collaboration will reduce the cognitive load required to perform a task, despite the potential increase of cognitive load from introducing collaboration into the task [START_REF] Kildal | Empowering assembly workers with cognitive disabilities by working with collaborative robots: A study to capture design requirements[END_REF]. We assumed that collaboration will allow better performance: increased effectiveness, efficiency, and satisfaction. Our operational hypotheses were:

-H1 = perceived cognitive load will be lower for an assembly task in a condition of collaboration with the cobot than in a control condition.

-H2 = completion time will be lower for an assembly task in a condition of collaboration with the cobot than in a control condition.

-H3 = the number of errors will be lower for an assembly task in a condition of collaboration with the cobot than in a control condition.

-H4 = the number of gestures will be lower for an assembly task in a condition of collaboration with the cobot than in a control condition.

Material and methods

54 participants (mean age = 21.4 years, SD = 3.9; 42 women and 12 men) consented to participate in the experiment at the University of Grenoble-Alpes.

They did not receive any financial compensation for their participation; however, psychology students could request extra credits on a set of classes. They had no vision impairment (colorblindness, etc.). Our study took place during the Covid semi-lockdown and we obtained the approval of the director of the inter-university laboratory of psychology of Grenoble to run the experiment with human participants. Participants were given explanations on the experiment itself and how their data will be handled. After this introduction, they were given a consent form to sign in order to continue the study. We developed a two-condition betweensubject design. Participants were randomly assigned to a group. In the control condition, 32 participants performed the task alone, whereas in the experimental condition, 22 participants performed the task in collaboration with a cobot (Figure 1). The cobot used for the task is a YuMi (short for You and Me) created by ABB Robotics enterprise (ASEA Brown Boveri). It was used for three reasons: first, it is an industrial cobot that is already implemented in many industrial factories, second, it is the most "collaborative" robot currently existing, and third, it has high safety included in the design (rubber padded joints, curved angles, etc.) to work "safely" in close proximity with human operators. Recruitment for the control condition took place in April because the cobot was not ready yet. Recruitment for the experimental condition took place in May, during the covid restriction. The two groups were uneven for this reason. The cobot was programmed on different levels through a modular robotic architecture. First, the vision module perceives the environment and the presence of the participant's hand during the perception phase to ensure a true description of the environment that is not occluded by the participant's hand. This stage was performed using Python programming language, and the OpenCV computer vision library. Second, the environment description obtained from the perception module is used to infer the progress of the collaborative task carried out by the robot and the participant. This information is used to drive a cognitive conceptual model, which in turn makes use of Automated Planning techniques to generate adaptive intelligent behavior that corresponds to the variability of the assembly task. The intelligent behavior is a plan: a sequence of actions that composes the pick/place task (opening the gripper, moving the robot arm from one place to another, closing the gripper, etc.). Third, the sequence of actions generated to accommodate task variability in an intelligent manner is executed on YuMi using the low-level assembly language RAPID provided by ABB (the manufacturer of the robot).

Finally, the plan (intelligent behavior composed of a sequence of actions) and the robot motion are projected on a screen through Graphical User Interface (GUI) to explain the intelligent behavior to the participant by making them aware of what will happen next.

Our aim was to develop a type of task that could be performed both in collaboration with a cobot or alone, and that would support study of cognitive load while considering the technical constraints of the cobot. We chose to simulate a typical task in the industrial environment: an assembly of components. In our exploratory study, the task was carried out using Duplos (Lego). Each participant was required to replicate three models (Figure 2). The order of presentation of the models was randomized. Each participant had a stock of Duplos located on their left (Area 1 in Figure 3). They had to replicate the model in the "construction zone" (Area 2 in Figure 3). In the experimental condition, the cobot's stock was placed on the participant's right (Area 3 in Figure 3). In the experimental condition, the cobot collaborated with the human by placing pieces on the construction area at the same time as the participant, by indicating if an error had been made using the communication interface and by providing additional stock to the participant (between the green Duplos left of Area 2). The cobot alternated between actions (Table 1) and perception phases (during which it would stop and analyze the Duplos placement). Its instructions were the same as the human's: it had to complete the model. During most of the experiment, the pace was set by the participant and the cobot had to adapt. However, due to technical constraints, the participants could not act when the cobot was in perception phase. They had to wait and regain control of the pace once the perception phase ended. Table 1. An example of a human-machine process chart in the experimental were given special instructions to consult the screen of the cobot to know where it was going to place its next piece and when it was going into the perception phase.

All participants replicated the three models while being filmed. Video recordings were analyzed using The Observer software (version 15.0.1200, 2019).

We observed the completion time, number of errors (when a piece is placed, removed, and replaced), and number of gestures, the latter defined as movements of an upper limb (placing, removing a piece, managing the stock, thinking, verification, waiting, robot interaction, and prevented gestures).

Finally, participants completed a translated version [START_REF] Cegarra | Étude des propriétés de la version francophone du NASA-TLX. COOP'2000[END_REF] of the NASA-TLX [START_REF] Hart | Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research[END_REF] to assess the cognitive load they experienced (see Table 2). This test measures the experienced mental load through six subscales: mental demand, physical demand, time demand, frustration, effort. and performance. It is conducted in two stages [START_REF] Hart | Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research[END_REF].

First, we asked participants to rate each subscale on a scale from 0 to 100. Second, we asked them to rank the different subscales according to the impact of each one on their overall feeling. This ranking ranges from 1 to 6, with 1 representing the subscale with the lowest impact on the overall feeling and 6 representing the subscale with the highest impact on the feeling of the mental load. All of this information allowed us to obtain the weighted score of the cognitive load perceived by each participant (each subscale is multiplied by the number of participants).

Table 2. French version of the NASA-TLX used in the experiment.

Mental demand

Quelle quantité d'activité mentale et perceptive a été nécessaire pour réaliser la tâche (par ex. réfléchir, décider, chercher, etc.) ? La tâche vous a-t-elle paru simple, nécessitant peu d'attention, ou complexe, nécessitant beaucoup d'attention ?

The questionnaire was implemented using the QUALTRICS software (Qualtrics, 2005). Internal consistency of the questionnaire was confirmed: no item was correlated more than 0.70 with another, the KMO index was greater than 0.50 (0.659), and the Bartlett Test was significant (K=46.590(15), p<0.01). Study results

were calculated using RStudio, version 1.4.1106 (RStudio Team, 2021). The results presented below are from a dataset available on GitHub (Datacobot2021a),

and the specific open source code was applied (RcodeDatacobot2021a). Unpaired t tests were used to test the study hypotheses, with p < 0.05 considered statistically significant. Assumptions of normal distributions of equality of variances were tested, and the Wilcoxon test was used as relevant.

Results

Hypothesis 1: the effect of collaboration with a cobot on perceived cognitive load

In the control condition, participants rated their cognitive load with mean of 37.5 out of 100 (SD=12.9, min=15.2, max=59.1, N=32) and in the experimental

Physical demand

Quelle quantité d'activité physique a été nécessaire pour réaliser la tâche (par ex. pousser, déplacer, tourner, manipuler, etc.) ? La tâche vous a-t-elle paru facile, peu fatigante, calme ou pénible, fatigante, active ?

Temporal demand

Quelle pression temporelle avez-vous ressentie durant l'exécution de la tâche ? Fallait-il gérer la réalisation de la tâche de manière lente ou de manière rapide ?

Performance

Comment estimez-vous votre performance en ce qui concerne la réalisation de la tâche ? (Quel est votre niveau de satisfaction concernant votre performance ?)

Effort

Quel effort (mental et physique) avez-vous dû fournir pour atteindre votre niveau de performance ? Frustration

Vous êtes-vous senti satisfait, content, relaxé ou plutôt ennuyé, irrité, stressé pendant la réalisation de la tâche ? condition at 35.5 out of 100 (SD=15.3, min=11.3, max=62.5, N=22). There was not significant effect of collaboration with the robot on evaluation of cognitive load (t=0.51(52), p=0.61).

Hypothesis 2: the effect of collaboration with a cobot on task completion time

The participants took an average of 54.8 seconds (SD=18.5,min=19.4,max=146.4,N=32) to reproduce a model in the control condition and 136.4 seconds (SD=30.9, min=80.9 max=213.7, N=22) in the experimental condition. There was a significant effect of collaboration with the robot on the completion time (t=-12.756(52), p=2,2*10 -12 ). Oddly, the NASA TLX answers indicate that the participants perceived less temporal demand in the collaboration condition (t=4.6(43.5), p=3,668*10 -5 ).

Hypothesis 3: the effect of collaboration with a cobot on the number of errors

Participants made an average of eight errors (SD=5.8, min=0, max=20, N=32) in the control condition and two errors (SD=1.8, min=0, max=6, N=22) in the experimental condition. There was a significant effect of collaboration with the robot on the number of errors (w=618.5, p=2,556*10 -6 ).

Hypothesis 4: the effect of collaboration with a cobot on the number of gestures performed

The participants performed an average of 74 gestures (SD=20, min=41, max=124, N=32) in the control condition and 51 (SD=9.8, min=34, max=72, N=22) in the experimental condition. There was a significant effect of collaboration with the robot on the number of gestures (t=4.9547(52), p=8,068*10 -6 ).

We tested the effect of the collaboration on all sub-sections of the NASA TLX test. T-tests revealed no differences on those sub-sections (including perceived frustration and effort), with the exception of a positive significant effect of the collaboration on the perceived time demand.

Discussion

The current results concerning the reduction of the number of errors and the number of gestures in the collaboration condition are in line with our initial hypothesis (H3 and H4). In our study, the human/robot collaboration reduced the number of errors and the number of gestures performed. This outcome could imply that in an non-experimental industrial work task, collaboration with a cobot could improve operator efficiency and the quality of their work. Similar results have been reported earlier. Indeed, [START_REF] Salunkhe | Assembly 4.0: Wheel Hub Nut Assembly Using a Cobot[END_REF] recently found that cobots could, in theory, identify problematic gestures, thus protecting human workers, while improving the quality of the work.

Since introducing the cobot increased the number of instructions (participants had to understand the task, and understand how the cobot works and how to interact with it), we feared that those additional instructions would adversely impact perceived cognitive load. However, this was not the case, since the perceived mental loads did not differ in the two conditions and H1 was not supported, though collaboration with the cobot had no negative effect on perceived cognitive load. [START_REF] Kildal | Empowering assembly workers with cognitive disabilities by working with collaborative robots: A study to capture design requirements[END_REF] found that the complexity of the task is not increased due to the introduction of a collaboration between human and cobot, even when the operator is cognitively impaired. Future studies need to measure the impact of collaborating with a cobot on cognitive load, as it is representative of the operator's wellbeing in their work [START_REF] Fruggiero | Cognitive Human Modeling in Collaborative Robotics[END_REF].

Finally, the results concerning completion time (H2) were against our expectations. Indeed, we found that completion time significantly increased in the condition of co-activity. This increase was due to the introduction of waiting time and technical problems that occurred during the experiment. In fact, another study, realized in an industrial context, showed no increase in completion time between collaboration with a cobot and no collaboration [START_REF] Fager | Kit Preparation with Cobotsupported Sorting in Mixed Model Assembly[END_REF].

Nonetheless, the perceived time demand was significantly lower here in the experimental condition. Thus, even though collaboration with the cobot lasted longer, this was not reflected in the perceptions of the participants. This outcome could be a clue that perceived pleasure with a cobot is higher that the perceived pleasure of working alone [START_REF] El Makrini | Working with Walt: How a Cobot Was Developed and Inserted on an Auto Assembly Line[END_REF]. Overall, we concluded that collaboration improved the user experience on the following criteria: errors, gestures and perceived time demand (Table 3). This study provides several contributions regarding human-machine systems design. Indeed, we noticed that there are variabilities in human behavior during the realization of a laboratory-simulated assembly task. These variabilities emerged from organizational design (presence or absence of a cobot), technical variability (issues linked to the cobot on certain experimental runs), and interindividual variabilities (e.g. wide range of the number of gestures performed by participants).

These variabilities were managed within the collaboration, as was the case in other recent studies [START_REF] Chacón | On Cognitive Assistant Robots for Reducing Variability in Industrial Human-Robot Activities[END_REF][START_REF] Fager | Kit Preparation with Cobotsupported Sorting in Mixed Model Assembly[END_REF]. The cobot and its AI allowed the experimental task to be constrained in terms of how the assembly was done (what piece to put, the order of piece, etc.) only by the human participant and not by "the machine". Our work shows that it is possible to have an intelligent machine that regulates its actions to help the human, whatever strategy he adopts, which is an advantage for industry work [START_REF] Pauliková | Analysis of the Impact of Human-Cobot Collaborative Manufacturing Implementation on the Occupational Health and Safety and the Quality Requirements[END_REF]. Furthermore, the current work showed that this new type of collaboration could be beneficial in terms of performance and usability. Further studies in an industrial setting, though, are needed to confirm such benefits. Those studies need to address the impacts (negative and positive) that cobots have on the operators (well-being, health, satisfaction at work) and on the industry (performance, security), the protocol of implementation used to change the organization of work, how operators are trained and recruited to work with cobots, and the ethics behind those changes. There needs to be an emphasis on the study of social aspects of the human/cobot collaboration.

We have also verified that it is possible (in the context of a laboratory task)

to measure cognitive load through different behavioral measures and to use these same indicators to obtain information on usability criteria according to the ISO 9241-11. Investigating cognitive load needed to adapt cobots to the human is a new, challenging, and necessary field of research [START_REF] Fruggiero | Cognitive Human Modeling in Collaborative Robotics[END_REF]. It is, therefore, an original contribution to bring together these two concepts belonging to different disciplinary fields and by experimenting in a different domain of UX.

We offer an original experimental protocol that can be transposed to other types of tasks.

The cobot had technical problems that impacted some experimental runs.

By working on improving of the cobot's technical capacities, we will be able to reduce task completion time and thus improve the overall UX. Furthermore, it would be interesting to replicate this study in an industrial environment, which is more complex than laboratory-controlled situations. The task here was quite simple, thus limiting the generalizability of the study. However, this simplicity was necessary to improve the AI of the cobot, a first step to ensure that AI was able to adapt to the variabilities of the human.

Our work indicates that the adaptation of a robotic system to a human is possible and should receive priority consideration owing to its benefits: it requires very little training (since the human does not adapt to the cobot) and it is flexible to human variabilities. Also, no analysis was made regarding potential differences related to gender, as there were more women than men in our study (statistical analysis would have been biased). Future studies should address gender [START_REF] Perez | Invisible women: Data bias in a world designed for men[END_REF] as a potential source of interindividual variability as it is relevant to improve science and design [START_REF] Tannenbaum | Sex and gender analysis improves science and engineering[END_REF]).

An experiment with a within-subjects design, by randomizing the order of the two conditions, would allow us to assess the perception of difference between the two conditions. Indeed, participant behaviors were different across experimental conditions. For example, the participants were much more static in the experimental condition than in the control condition. This difference in behavior could be the result of a different conceptualization of the task.

Future research needs to be developed to evaluate the impact of the cobot collaboration on workload. For now, our study is in line with earlier literature, and it seems that using a cobot to collaborate with an operator could decrease perceived cognitive load. As [START_REF] Perre | Improving productivity and worker conditions in assembly: Part 1 -A collaborative architecture and task allocation[END_REF] suggested in their article, the cobot could "diminish an operator's workload and improve the working conditions" (p. 5). To analyze this impact, researchers can use self-reported scales such as the NASA-TLX and behavioral measurements such as the number of errors and the completion time.

In summary, we found that performing an assembly task in collaboration with a cobot increased effectiveness, maintained the level of satisfaction, and improved certain criteria of efficiency (number of errors, number of gestures) if the robot is programmed to adapt its decisions to the human's. In this laboratory context, the human/cobot collaboration facilitated better task performance and an overall improved UX. We also developed an experimental protocol linking the concepts of usability and cognitive load in a multidisciplinary experimental task, combining the technical constraints of programmers and the constraints of human behavior measurements. This protocol can be used in future studies to identify key points to consider when designing a cobot (perception of the cobot, infrastructure of the AI, etc.), thus facilitating broader and more effective implementation of cobots in Industry 4.0.

Figure 1 .

 1 Figure 1. The YuMi robot (ABB) used in the experimental condition.

Figure 2 .

 2 Figure 2. The three models that participants were required to replicate.

Figure 3 .

 3 Figure 3. Location of the construction zone (2), the participant's stock (1), and the

  condition, participants had a three-minute training session with a demonstration on how to complete a model. The experiment took place in a ventilated room with adequate lighting. In the experimental condition, participants had five minutes of training on how to complete a model and how to interact with the cobot. No participant demonstrated substantial mental or physical fatigue. They

Table 3 .

 3 Summary of the results of collaborating with a cobot for each independent variable.

	Outcome	Collaboration with cobot
	Perceived cognitive load	Equal to the control condition
	Completion time	Higher than in the control condition
	Number of errors	Lower than in the control condition
	Number of gestures	Lower than in the control condition
	Perceived time demand	Lower than in the control condition


Corentin Piozin

Gabriela Herrera Altamira

Catherine Simon

Brice Lavrard

Jean-Yves Audran

Florian Waszak

Selim Eskiizmirliler

Motion prediction for the sensorimotor control of hand prostheses with a brain-machine interface using EEG

Keywords: Brain-Machine Interface, Prosthesis, Feature Extraction, Motor Imagery

I. INTRODUCTION

Upper-limb amputations account for around 20% of the total number of amputations throughout the world, estimated to be about 65 million people [START_REF]ATscale Product Narrative: Prosthesis[END_REF]. From those 20%, the World Health Organization (WHO) consider that only 10% have access to prosthetic devices, mainly because of the high cost of the commercially available and certified devices and the lack of personnel and infrastructures [START_REF]WHO standards for prosthetics and orthotics[END_REF]. Nevertheless, in a survey conducted by Biddiss & Chau regarding the factors of prosthesis abandonment [START_REF] Biddiss | Upper-limb prosthetics: critical factors in device abandonment[END_REF] the team noted that 10 to 50% (depending on the age) of users of upper-limb prostheses have stopped to use their devices as it is considered inconvenient for daily-use, mainly because of the fatigue, the weight, the lack of comfort or because their also notice that the absence of sensory feedback in the prosthesis is a significant factor of rejection among users. It is indeed known that myoelectric prostheses -which are based on the use of surface ElectroMyoGraphy (EMG) electrodes to control movements on the device -come with some limitations, for example the stump on the arm of t78-1-6654-1337-4/22/$31.00 ©2022 European Union user is subject to constant stress while in use and the sweat from the prosthesis socket can also make it difficult to properly catch the muscular activity. Those disadvantages offer room for the development of a new system that would go beyond the traditional myoelectric control of the prostheses, in order to improve both the dexterity and the robustness of prostheses for activities of daily living.

Brain-Machine Interfaces (BMIs) -which allow a user to control various end effectors through the recording and decoding of its brain activity -possess what seems to be an ever growing popularity, evolving both in research and in the general population with great promises. Moreover, an important fraction of the research done on that matter focuses on medical applications of BMIs, particularly for neurorehabilitation in the case of motor impairment following a stroke [START_REF]Cantillo--computer interface coupled to a robotic hand orthosis for stroke patients' neurorehabilitation: a crossover feasibility s[END_REF][START_REF] Ramos | Murguialday -machine interface in chronic[END_REF][START_REF] Tam | Gao for motor imagery BCI to control an assistive device in chronic stroke subjects: a multi-IEEE transactions on neural systems and rehabilitation engineering[END_REF] or a spinal cord injury [START_REF]Wodlinger et -dimensional anthropomorphic arm control in a human brain-machine interface: difficulties, solutions[END_REF][START_REF] Benabid | brain-machine in a tetraplegic patient: a proof-of-concept The[END_REF].

The use of BMIs relies primarily on the good recording of the brain activity of a subject; it is commonly divided in three distinct categories: invasive, semi-invasive and non-invasive [START_REF] Eskiizmirliler | -machine interface (BMI) as a tool for understanding human-machine cooperation into human enhancement: interdisciplinary and international[END_REF]. Invasive BMIs use microelectrode arrays implanted directly in the region of interest of the cortex (e.g. the Motor cortex for movement-related applications) that record the activity of a population of neurons [START_REF] Collinger | -performance neuroprosthetic control by an[END_REF]. Similarly, semi-invasive recording techniques use a grid of electrodes (also known as ECoG, for ElectroCorticoGraphy) implanted subdurally over the cortex and measure cortical field potentials [START_REF] Babiloni | in somatosensory, motor, premotor and prefrontal cortical Clinical neurophysiology[END_REF]12]. While offering an excellent signal for BMI applications, those two techniques, as their name suggests, require an important surgical procedure with a decrease of performance over time due to the degradation of the signal coming from the electrodes [13][START_REF] Perge | day signal instabilities affect decoding[END_REF][START_REF] Jeong | ngineering for hard problems[END_REF]. Despite suffering from its own limitations (mainly a poor signal-tonoise ratio and low spatial resolution of the signal, as well as a long preparation time on a subject), the non-invasive recording of brain activity with ElectroEncephaloGraphy (EEG) is widely common in research, from behavioural research on cognition processes [START_REF] Darriba | through evidence accumulation over time[END_REF] to medical diagnoses [START_REF] Chen | A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG[END_REF][START_REF] Bosl | EEG analytics for early detection of autism spectrum disorder: a data-driven approach[END_REF] or neurofeedback treatments [START_REF] Guillard | A portable neurofeedback device for treating chronic subjective tinnitus: feasibility and results of a pilot study[END_REF][START_REF] Micoulaud-Franchi | EEG neurofeedback for anxiety disorders and post-traumatic stress disorders: a blueprint for a promising brain based therapy[END_REF] and even video games [START_REF] Congedo | Brain Invaders: a prototype of an opensource P300-based video game working with the OpenViBE platform[END_REF]. It is also probably the most exploited method for the development of Brain-Machine Interfaces applications thanks to its ease of use and good temporal resolution of the signal, which makes it a promising candidate for the control of prosthesis on a daily basis. However little has yet been done on that topic, as the current focus concerns the increase of EMG electrodes to recognize muscular patterns of activation when performing different movements [START_REF]mobility-based prosthesis control in transhumeral amputees without surgical reinnervation: a preliminary s[END_REF]. One can also note the use of the Targeted Muscle Reinnervation (TMR) procedure [START_REF] Mioton | Targeted muscle reinnervation and prosthetic rehabilitation after limb loss[END_REF][START_REF]France : la chirurgie au service des prothèses bioniques. Technique chirurgicale et résultats préliminaires à 1 an de la chirurgie[END_REF], which consists in using severed nerves of a patient by relocating them in a new muscular area (e.g. in the chest), giving more possibilities to control a device as well as reducing the pain coming from the injured peripheral nerves [25]. Nonetheless, despite the improvement in the control of prostheses this technique still requires a surgical procedure which reduce the possibilities of a large-scale implementation, leaving some space for the EEG to fill those gaps.

Up until recently, the work from the laboratory was centred around the studies of invasive BMIs for the kinematic and dynamic control of robotic hand & arms [26,[START_REF] Tagliabue | -digit grip type and force level by frequency decoding of motor cortex activity for a BMI[END_REF]. The latest study (submitted) reports a performance analysis of mimicking grasp movements by two different robotic hands controlled by BMIs using invasively and non-invasively recorded brain activities as inputs.

In this paper, we will present the preliminary data analysis and development results of our ongoing project conducted in collaboration with the international prosthesis manufacturer Otto Bock (Duderstadt, Germany) and the rehabilitation center Institut RMA, Valenton, France) aiming at the development of an EEGbased BMI for the sensorimotor control of hand prostheses. In parallel to the design & development work seven healthy and four amputee subjects, wearing two different EEG caps, participated to the experiments during which they performed or imagined different types of grasp movements while their motor execution (ME) and motor imagery (MI) signals were respectively recorded. Movements of interest for the control of the prosthesis were then estimated using various combinations of feature extraction and decoding algorithms, and the results of this first analysis phase of the project concerning the movement type estimation performance of the proposed BMI system using EEG signals in MI condition will then be reported.

II. MATERIALS & METHODS

The main role of the interface part of a BMI system is to decode what should have been coded in the recorded brain activity, the desired hand motion in our case, and it is composed of two main modules: a) Feature extraction of the recorded EEG signals and b) Estimation of the desired hand motion. On the other hand, a BMI system aiming to be employed for routine use does not only need to be robust, reliable and reproducible in prediction performance of the desired motion but it also needs to be ergonomic and easy to use. Therefore, the work on such systems should also consider an optimisation of the design parameters of the final product among which the number of EEG electrodes (which for example might be installed inside a baseball cap) as well as the training/calibration time of the used machine learning algorithms would be the most important ones.

The critical issues which would affect the performance of a robust daily-life control of the myoelectric prosthesis can be grouped into three sets of problems/questions that need to be answered:

1) Recording of the brain activity: which type of EEG system is suitable for this application? Which brain regions should be recorded to get the highest prediction performance? What is the minimum number of electrodes that we need to use to achieve a satisfying control level?

2) Feature Extraction & decoding of the brain activity: Which feature extraction and decoding algorithms and/or combination of these two can provide the best motion prediction performance? Here we will propose a feature extraction pipeline as well as classification algorithms that are offering promising performances regarding the control of the prosthesis.

3) Sensory Feedback: What is the contribution of the sensory feedback on the performance of the BMI system?

This study focuses particularly on the work on the signal processing, data analysis and training/learning processes in the aim of proposing solutions to the problems of the second group.

A. Participants

Seven healthy participants (3 females and 4 males, 5 Right-handed, 2 Left-Handed, 29.8 ± 13.3 years old,) composing the control group and four amputated subjects (4 males, 2 transradial amputations of the left hand and 2 of the right hands, 58.25 ± 3.86 years old) participated in this first phase of experiments.

All participants were given information in oral and written forms before giving written informed consent to participate in the study. The protocol was performed in accordance with the Declaration of Helsinki. The criteria of inclusion for the amputated subject were the following: person above 18 years old with a transradial amputation and user of a myoelectric prosthesis. All amputees but Amputee #3 declared using their myoelectric prostheses -a polydigital hand (Michelangelo © , Ottobock) on a daily basis, the third amputee is using his prosthesis (Myobock SensorSpeed © , Ottobock) occasionally.

B. Experimental Setups

Two experimental setups (see Fig. 1. A & B) were developed to perform experiments and recordings with healthy and amputee subjects using two EEG caps with different number of electrodes and different sampling rates but placed following the 10 20 position system andreferenced to the CPz electrode for each system. Fig. 1. A) Experimental Setup for Healthy subjects: Continuous EEG data were collected from 63 actiCAP EEG electrodes (BrainProducts GmbH) mounted on an elastic cap using the PyCorder system on the recording computer and actiCHamp amplifiers (BrainProducts GmbH, Gilching, Germany) with a sampling rate of 2000 Hz. The second computer was running the experiment on a custom script written with MATLAB 2016b (Mathworks, Navick, MA) using the Psychophysics Toolbox [START_REF][END_REF]. B) Continuous EEG data were collected from 16 sintered EEG electrodes (OpenBCI, United States) mounted on an elastic cap using the BrainFlow library on the recording laptop and the Cyton+Daisy boards with a sampling rate of 125Hz. The recording laptop was also used to run the experiment via a custom C++ program written and compiled on the QT IDE (QT Creator 4.13.3). C) Modified myoelectric prosthesis controlled by EEG signals and the sensory feedback devices. (from left to right: the vibration bracelet and force-sensing resistors).

A modified Myobock hand prosthesis, allowing the control of the prosthesis via the EEG signals of the user, and a sensory feedback system (Fig. 1.C), composed of forcesensing resistors and vibration motors communicating via Bluno microcontrollers, were used during sessions with amputees.

C. Experimental Protocols

The experiments with the amputees were realised in three sessions occurring in three different days (with at least ten days between two sessions) while the experiments with healthy subjects were composed only of the second phase experiments with ME and MI conditions of the first session done with amputees, but repeated three times.

1) Protocol for amputated subjects a) First session: Subject dexterity prior to the use of the BMI system

This session is composed of two phases. In the first phase the subjects were asked to do three tests with their own prosthesis in order to evaluate their dexterity prior to the use of the Myobock controlled by our BMI system. The first test is called the Box and Blocks Test (BB) and is widely used in rehabilitation to evaluate the object manipulation performance of the subject [START_REF] Mathiowetz | The American journal of occupational therapy: official publication of the[END_REF]. The setup is composed of an opened wooden box, divided in the middle with a board. One side of the box contains cubic wooden blocks (2.5 x 2.5 x 2.5cm) and the subject is asked to move as many blocks as he can on the other side of the box in 1 minute. It is reminded that the subject has to move blocks one at a time and that if the subject moves two or more blocks at the same time, only one will be counted for the final result. The total number of blocks moved is used as the outcome score for the test. The second test is derived from [START_REF]artificial feedback in the control of a multi-functional myoelectric[END_REF] and is called the Cup Relocation Test (CUP), it consists in 11 plastic cups stacked on each other and placed bottom-up on a plank. The subject is asked to move 10 plastic cups (one cup at a time) from a position located 30 cm towards the contralateral side, as fast as possible. The time needed to move the 10 plastic cups is used as the performance score. The final task is called the Clothespin Relocation Test (CS) and is inspired from the work in [START_REF] Kuiken | improved myoelectric prosthesis control in a bilateral shoulder[END_REF]. The subject is asked to relocate four plastic clothespins from a horizontal position to a vertical position, the plastic clothespins could vary in the force needed to open them. The time needed to relocate the four pins is used as the performance score for the test. Each of the three test were repeated five times in order to have a more accurate measure In the second phase of this first session, we asked the subject wearing the EEG cap (OpenBCI) seating comfortably on a chair and looking at a computer screen to perform a sequence of movements using the same objects used in the first phase but following the visual and audio instructions sent by the computer according to the sequence seen in Figure .2 and in two conditions: Motor Execution (ME), where the subject actively performs the movement with his own prosthesis, and Motor Imagery (MI), where the subject imagines of performing the movement. Based on the feedback of the first amputated subject we modified the experimental protocol for the MI condition by removing the random time windows (1 second for the Go step, and 2 seconds for Move) and by reducing the time in the Grip step (2 seconds instead of 3). The experiment consisted in 30 trials with each object and for the two conditions, on which was added the recording of 10 trials of neutral/baseline activity where the subject was asked to simply look at the screen without performing or thinking of any movement. Finally, the subject was instructed to reduce as much as possible the eye blinks during the movement.

b) Second session: ME and MI

The second session consists in experiments with EEG recording identical to the ones presented in the first session but with an important difference in the ME condition; instead of using his original prosthesis the subject uses a modified Myobock prosthesis that is connected to the computer. The PC is controlling the opening and closing of the hand in the Grip (Closing) and Release (Opening) steps during the experiment. This ME, that we call MEP (for Motor Execution with Prosthesis), recording is thus changing into a hybrid (or intermediate) condition between the classic ME condition and the MI condition as the subject is now seeing his prosthesis being opened or closed without the motor command coming from the EMG electrodes.

At the end of the recording session, the sensory feedback is tested for a few minutes on the subject to give her/him an idea of the content of the third recording session.

c) Third session: Sensory feedback

The third recording session is identical to the second (MEP and MI) except for the addition of the sensory feedback system in the MEP condition, which adds sensory information to the user during the Grip, Move and Release steps of the sequence.

2) Protocol for control subjects

The sequence of movements and number of trials for control (able-bodied) subjects are identical to the ones defined in the first session of the experimental protocol for amputated subjects (Fig. 2.) in ME and MI conditions. At this phase of the study the control subjects did not perform the experiments of the second and third session using the apparatus allowing them to control a prosthesis with their EEG signals and with or without sensory bracelet.

D. EEG Signal Processing 1) Preprocessing

EEGLAB functions were used to segment the trials and retrieve the data from the Grip and Release steps from the EEG files recorded with the ActiCHamp system [START_REF] Delorme | analysis of single-trial EEG dynamics including independent[END_REF]. To speed up the computation time, each trial was downsampled from 2000Hz to 250Hz. Similarly, the OpenBCI data were segmented but the sampling rate was kept at 125Hz (internal limitation of the system). We only kept the first second of each trial after the stimulus onset on which an infinite impulse response notch filter set at 50Hz and a 4 th order Chebyshev type II bandpass filter between 8 and 30Hz was applied, to minimize noises and artefacts on the trials. In order to increase the size of the dataset and minimize the effect of imbalanced classification, the analogy method from [START_REF] Lotte | suppress calibration time in oscillatory activity-based brain computer int[END_REF] was used so that each class now contains 90 trials. The method consists in computing a ratio between two trials of the same class (using its principal components) and applying this ratio to a third trial of the class to create a new and artificial trial. We considered the movement Close (full closure of the hand) as the motion type of the Grip step of both the BB and CS objects while the movement Half (half closure of the hand) was considered as the motion type only of the Grip step of the CUP object. The Open movement (full opening of the hand) is a state in common for the Release steps of BB, CS and CUP. Finally, the neutral/baseline condition is considered to be Rest. Then the z-score of each trial was computed and used as the starting point for the feature extraction pipeline.

2) Feature Extraction

In the following we first present the feature extraction techniques we used in constructing the input vector contents to the classification algorithms. We then study the effect of the use of different combinations of those techniques in the classification performance of the two decoding algorithms used in the current version of the proposed system.

a) Common Spatial Patterns (CSP)

The Common Spatial patterns technique is probably the most commonly used spatial filtering technique in BMIs [START_REF] Achanccaray | Decoding hand motor imagery tasks within the same limb from EEG signals using[END_REF][START_REF] Ramoser | Optimal spatial filtering of single trial EEG during imagined hand movement[END_REF][START_REF] Wang | Feature extraction by common spatial pattern in frequency domain for motor imagery tasks classification[END_REF]. This method is applied mainly to binary classification; it consists in computing a transformation matrix W that is maximizing the variance of the signal for one class while minimizing it for the other [START_REF] Blankertz | [END_REF]. Finding the optimal transformation matrix W requires to solve:

(

(2) being an estimate of the spatial covariance matrix of class . Using the Generalized Eigen Value Decomposition (GEVD), the solution to this optimization problem is the transformation matrix composed of the eigenvectors corresponding to the maximal and minimal eigenvalues obtained from .This technique is applied here to four movements (Open, Close, Half, Rest) using a One-vs-One approach, leading to the computation of six different transformation matrices W ( (n*(n-1))/2 = 6, with n = 4, the number of movements to classify). 6 pairs of CSP filters were kept using the setup with healthy subjects while 8 pairs were kept in the setup with amputee subjects, thus giving a matrix size of (m, n, 90) for each of the projection (with m = 248, 124 and n = 12, 16 for the ActiCHamp and OpenBCI EEG systems respectively) [START_REF] Blankertz | [END_REF].

b) Wavelet Decomposition (WD)

The EEG signal is known to be non-stationary meaning that its properties are changing with time, thus by applying traditional techniques such as the Fast Fourier Transform (FFT) it is possible that one can miss relevant information regarding the movements. Applying the Wavelet Decomposition method allows to retrieve Time-Frequency information on the original signal [START_REF]International Symposium on Modern Computing[END_REF]. For this operation a function called the Mother Wavelet which starts at zero, briefly oscillates and comes back to zero is scaled and translated to create a set of wavelets on which the signal is convoluted with. Here a Daubechies 4 mother wavelet has been used. Two filters are used to convolute the signal: a lowpass filter (LPF) and a high-pass filter (HPF). Following the convolution, a downsampling is done on the two filtered signals and the approximation coefficients and details coefficient are obtained. To continue the decomposition to further levels, the approximation coefficients are considered as the base signal for the next decomposition. As the WD technique keeps frequency information of the signal it is possible to choose the decomposition level based on the frequency band of interest of the signal, for this article we chose a 2-level decomposition for the OpenBCI EEG system and 3-level decomposition for the ActiCHamp EEG system to fall approximately in the 0-32Hz frequency band. We decided to use the approximation and detail coefficients from the last level sufficient for the reconstruction of the original signal with inverse WD for the next step of the feature extraction process. After the CSP computation, we obtain 12 signals as each of the 6 transformation matrices is used to project the signals of 2 different classes, we thus apply the WD on those 12 signals which gives us a matrix size and n = 12, 16 for the ActiCHamp and OpenBCI system).

c) Gram-Schmidt Orthonormalization (GSO)

From the features vectors composed of the wavelet coefficients, a basis (with , corresponding to the numbers of comparisons signals and the number of filters kept after the CSP step) is obtained by orthonormalization following the Gram-Schmidt procedure [39]. For each of the comparisons and filters a projection matrix is computed from the bases and used to project the feature vectors obtained during the previous step. As we are comparing two different classes, we also use the projection matrix of the first class to project the feature vector from the other class. Finally, for each projection the logarithm of the variance is calculated and the values obtained are used to build the input vectors for the classification algorithms.

3) Input vectors

With this article we aim at comparing different feature extraction methods by using a combination of the three processing techniques we described. The first method is CSP+WD, the application of CSP then WD in the FE, leading to 6 binary classification model with inputs sizes of 180 x 12 or 180 x 16, for the ActiCHamp or OpenBCI systems respectively (90 trials for the first class and 90 for the second). The second is CSP+GSO, it gives 12 binary classification models with once again inputs sizes of 180 x 12 or 180 x 16 for the two system. The third method is WD+GSO, for this one we obtain 12 models and the input size is either 180 x 63 or 180 x 16 as we decided to keep all the electrodes during the FE. Finally, the last method is CSP+WD+GSO, the application of the three techniques, it leads to 12 binary models with inputs sizes of 180 x 12 or 180 x 16, similar to the CSP+GSO method.

4) Classification

The training performance has been obtained offline on Matlab 2021a using three different classification algorithms: An Artificial Neural Network (ANN) with a Training/Validation/Test split of 70%-15%-15%, and two Support Vector Machines (SVM) with a Training/Test split of 80%-20% using a Linear and a Radial Basis Function (RBF) kernels. The Accuracy, Precision and F1-score metrics were computed for all the models and for the three classification algorithms.

III. RESULTS

As our BMI control system uses classification algorithms that need to be trained by the user, one of the most important issues concerning the easy use of the final product would be to get the amount of data necessary to train the classification algorithm in a reasonably short time. In machine learning, extending the training dataset by adding to the originally recorded signals an artificial data set is commonly used to increase the amount of data without increasing its collection, especially in image classification [START_REF] Vincent | [END_REF]. Fig. 3. shows the standard deviations of original and artificial data obtained for seven different pairs of electrodes using the data augmentation method described in II.D.1.. Fig. 3. Distribution of the standard deviation for the trials obtained from the recording using the object noted as CUP (a plastic cup) with the OpenBCI EEG system. The Original (in blue) and Artificial (in red) trials are here represented for several pairs of electrodes. The results are presented here for the first amputee.

Following the order of the critical issues evoked in session II and particularly the second group, we first wanted to explore the training performance of different combinations of the 3 feature extraction techniques (namely CSP, WD and GSO) described respectively in section II.D.2. Four different combinations, namely CSP+WD+GSO, CSP+WD, CSP+GSO and WD+GSO, have been studied. Fig. 4. shows the distribution of the log variances corresponding to the first and last variables of the input vector (1 st and 12 th for CSP+WD+GSO, CSP+WD & CSP+GSO, and 1 st and 63 rd for WD+GSO for two extreme types of hand movements (Close and Rest), which can be controlled on the Myobock. As it can easily be seen in Fig. 4., results obtained by using CSP+WD are the most promising ones.

We then studied the accuracies of the classification performance of four different types of desired hand positions obtained by different classification algorithms. Table 1, Table 2 and Table 3 represent respectively the accuracies of the ANN and SVMs classification results for several combinations of the Feature Extraction techniques described in II.D.2 obtained from the first session in Motor Imagery with the control and amputee groups. The use of a fast and efficient Feature Extraction method in constructing the inputs to the classification/prediction algorithm is also critical for such applications with real-time processing needs. For the first part, we showed in Table 1 that the combination of CSP & WD gives the most promising results both for the two groups of subjects (above 90% of accuracy for the Control group, and above 70% for the amputees). While the Feature Extraction could probably have given similar results if it stopped after the application of the CSP technique, the addition of WD allows a reduction of the size of the signal, as only the coefficients from the last level of the decomposition were kept. This is of course not so important for a research work (offline study) aiming at searching for the best feature extraction method but it will reveal of extreme importance when we will tackle a real-time control of the prosthesis, as we need to continuously process data and thus need that processing to be done as fast as possible. Even though the addition of the GSO was found to be interesting we selected the CSP+WD as the feature extraction method to be used for the remaining part of the study by taking into account those aspects. Figure 4 also confirms those results by representing how the partitioning of the features space by the two classes is more clear/distinct for the combination of CSP+WD comparing to the other possible combinations. Finally, the three different classifiers we used in this work to decode the desired motions provided very close prediction performances without any statistically significant difference, confirming the results we reported in our previously published [26] and submitted work. Moreover, among these three classifiers, the two SVMs were the fastest to train (around 2 minutes) while the ANN (around 10 minutes) presented significantly longer training time. This allowed us to designate the SVM with a linear kernel as the potential decoding algorithm in the future work, being the fastest classifier to train and the simplest one to use in a realtime application.

From the results summarized above, we can say that the use of a wireless EEG system, with less electrodes rather than a research-grade system, can give satisfying results when looking at the classification performances of the four movements, which are the movements that can be performed by this type of prosthesis. Indeed, the results reported on the fig. 5 show how the classification performance in the Control and Amputee groups evolves in the same range for the different binary classification models, a range that is much higher than the chance level in the case of binary classification (50%) when we match the electrodes on the two recording systems.

IV. DISCUSSION

This study investigates the use of a Brain-Machine Interface to control a commercially-available myoelectric prosthesis (Myobock©, Ottobock). Even though the current research project is being conducted on the three groups of problems evoked in II, due to difficulties concerning the recruitment of both able-bodied and amputee subjects for the experiments with the Covid-19 pandemic conditions, the results reported in this article are the ones obtained by processing the EEG signals recorded during the first session of the experimental protocol for both groups.

One of the first aim of this work is to propose to the potential users of the BMI system a reduced calibration time, comprising both the recording of the EEG signals and their processing. The data augmentation technique we used [START_REF] Lotte | suppress calibration time in oscillatory activity-based brain computer int[END_REF], is a promising solution to answer to such needs. The results presented in Fig. 3. confirm that the original and artificial data sets occupy the same feature space, which supports the use of this technique. Therefore we always used the augmented dataset in the remaining steps of this study.

The final evaluation step of the project will be the comparison of the scores obtained during the three tests (BB, CUP, CS, described in the first session of the experimental protocol for the amputee subjects) with the scores of these subjects obtained while they use the modified prosthesis. Only one amputee among four could finish the three sessions and realized a first trial of using the modified prosthesis together with the vibrating bracelet to perform a CUP test. Therefore, the ongoing development and research phase of our project focuses particularly on performing the experiments with more amputee subjects for all three sessions. Secondly we aim to be able to perform the experiments of the three sessions also with the control subjects, by allowing them to use a copy of the modified prosthesis and control it with their own EEG signals and use the vibrating bracelet.

Nonetheless, we are well aware that the results presented here are only preliminary ones and that we require more subjects in both the control and amputee groups to bring answers to the various issues we raised in this article. In particular, there is a need to analyze the potential changes on the control performances which, would occur with the amputees all along the three sessions: would the training of the task give higher performance? or does it stay the same over time for the two groups? Can we see distinct activation patterns on the EEG recordings? Does the addition of the sensory feedback change anything on the decoding and control performances?

We will be obtaining more clear and concrete answers to those questions as the experiments go by, but the presented results show a promising proof of concept for the efficiency of the use of BMIs for the control of myoelectric prostheses for amputees.

Fig. 2 .

 2 Fig.2. Sequence of phases during the EEG recording. It is composed of 6 successive steps, the 1 st and 6 th steps are fixation crosses lasting 1 second each where the subject has no action to perform/imagine. The 2 nd step (Go) instructs the subject to go to the object represented on the screen, a random time window (1 to 2 seconds following this spacing: 1s, 1.25s, 1.5s, 1.75s, 2s) is used to alleviate the habituation effect coming from the repetition of the sequence. The 3 rd step (Grip) asks the subject to grasp the object with his prosthesis and lasts 3 seconds. The 4 th step (Move) asks the subject to move the object to his right or to his left (depending on the previous motion done). Similarly to the Go step the Move step contains a random time window of 2 to 3 seconds. The 5 th step (Release) asks the subject to release the object and to go back to his/her initial position. This step lasts for 2 seconds. The asterisk * indicates that one of the three objects presented on the right of the figure was used in the corresponding phases.

Fig. 4 .

 4 Fig. 4. Distribution of the log variances of the input vectors for the comparison between the movement Close and Rest when using different combination of feature extraction techniques.

Fig. 5 .

 5 Fig. 5. Comparisons of the performance between the Control group (in black) and the Amputees (in pink) using three different classification algorithms (ANN, SVM Linear & SVM RBF, respectively the circle, square and triangle symbols) after feature extraction with CSP+WD, using the same locations of electrodes for the two EEG systems.

Table 1 .

 1 Mean accuracies across participants for the two groups of subjects with four feature extraction techniques using an ANN.

Table 2 .

 2 Mean accuracies across participants for the two groups of subjects with four feature extraction techniques using a linear SVM.

Table 3 .

 3 Mean accuracies across participants for the two groups of subjects with four feature extraction techniques using a RBF SVM.

ACKNOWLEDGMENT This work has been partially supported by the French National Research Technology Association and Otto Bock France in the framework of the CIFRE project n°2018/1620.