Cyrine Nasri
email: cnasri@loria.fr

Kamel Smaili
email: smaili@loria.fr

Chiraz Latiri
email: chiraz.latiri@gnet.tn

Yahya Slimani
email: yahya.slimani@fst.rnu.tn

A new method for learning Phrase Based Machine Translation with Multivariate Mutual Information

Keywords: statistical machine translation, inter-lingual triggers, multivariate mutual information

Current statistical machine translation systems usually build an initial word-to-word alignments before learning phrase translation pairs. This operation needs so many matching between different single words of both considered languages. We propose a new approach for phrase-based machine translation which does not need any word alignments, it is based on inter-lingual triggers determined by Multivariate Mutual Information. This algorithm segments sentences into phrases and finds their alignments simultaneously. The main objective is to build directly valid alignments between source and target phrases. Inspite of the youth of this method, experiments showed that the results are competitive but needs some more efforts in order to overcome the one of state-of-the-art methods.

Introduction

Machine translation issue could be handled by several ways, some of them are syntax-based (see Ref. 1, 2, 3 and 4), others are based on statistical models. Nowadays, a more attractive approach is to see how to combine a purely statistical technique with some linguistic rules or model (see Ref. [START_REF] Koehn | Statistical phrase-based translation[END_REF] The work presented in this paper is based on statistical method. The principle consists in finding the best translation of a source sentence among several ones. Thus, translating a sentence from language A into B involves finding the best target sentence b * wich maximizes the probability of b given the source sentence a. Bayes rule allows to formulate the probability P(b|a) as follows:

The translation process needs a language model P(b), a translation model P(a|b) and a decoder wich calculates b * . Language model parameters are trained on a target corpus and its task is to build up a correct sentence from partial translations, whereas parameters of the translation model are determined from a parallel corpus and provides the probability that a linguistic unit is translated to another. Then, the decoder provides the best target sentence by taking into account several parameters provided among other the previous models. In this work, we develop a new algorithm for extracting phrase pairs from parallel corpus. This algorithm does not require an initial segmentation on the monolingual text. It uses inter-lingual triggers based on Multivariate Mutual Information between the source and target phrases. In fact, we propose an original method which retrieves automatically phrases and their corresponding translations in one step. It means that a phrase translation is not constructed by agglutining connected words in the target language. The remainder of the paper is organized as follows : section 2 gives an overview of statistical phrase-based machine translation and interlingual triggers. In section 3 and 4 we present our method for learning phrase translations. Section 5 describes how we estimate probabilities for phrase pairs to fit into the SMT decoder. Section 6 shows how we integrate and test our new approach into an entire translation process. Conclusion in section 7, points out the strength of our method and gives some tracks about future work.

Background and related works

Statistical phrase-based machine translation

At present, best performing statistical machine translation systems are based on phrase-based models: models that translate small word sequences at a time. First statistical methods (see Ref. 5) were word-based models, but words, as shown, in later works gige worse results than those based on longer units. But words may not be the best candidates for the smallest units for translation. Sometimes one word in foreign language is translated into two English words, or vice versa. For instance, in French "petit déjeuner " is translated in English by "breakfast". It is important to note that current phrase-based models are not rooted in deeply linguistic in approach of phrases. Koehn and al. defined a phrase as a contiguous multiword sequence, without any linguistic motivation (see Ref. 6). Phrases are mapped oneto-one based on a phrase translation table, and may be reordered. All phrase pairs that are consistent with the word alignment are added to the phrase table. Modern statistical phrase-based models are based on alignment template models (see Ref. 7 and8).

These models defined phrases over word classes that were then instantiated with words. Several methods to extract phrases from a parallel corpus have been proposed. Most make use of word alignments (see Ref. 9, 10 and 11). Phrase alignment may be done directly from senetce-aligned corpora using a probabilistic model (see Ref. 12), pattern mining methods (see Ref. [START_REF] Yamamoto | Learning sequence-tosequence correspondences from parallel corpora via sequential pattern mining[END_REF]), or matrix factorization (see Ref. [START_REF] Goutte | Aligning words using matrix factorisation[END_REF]. In this respect, Lavecchia proposed in Ref. 15 a method which retreives valid linguistic phrases without using any alignments. This method identifies first the best part-of-speech phrases and then from these class phrases, they extracted the corresponding phrases which improve the perplexity of the source language. The obtained phrases are linguistically pertinent and consequently the derived phrases are also relevant. These obtained phrases are then used to rewrite the source training corpus in terms of phrases. Let us give an example, noun det noun is one of the retrieved part-of-speech phrases and from this pattern and the source corpus a phrase as Table de Salon is extracted. The words of this phrase are gathered and used to rewrite the source training corpus.

Inter-lingual Triggers

Inter-lingual triggers are inspired from triggers concept used in statistical language modeling (see Ref. [START_REF] Tillmann | Word triggers and the em algorithm[END_REF]). A trigger is a set composed of words and its bests correlated triggered words in terms of mutual information (MI). In Ref. 17, authors proposed to determine correlations between words coming from two different languages. Each inter-lingual trigger is composed by a triggering source linguistic unit and its best correlated triggered target linguistic units. Based on this idea, we found among the set of triggered target units, potential translations of the triggering source words. Inter-lingual triggers are determined on a parallel corpus according to mutual information measure namely:

M I(a, b) = P(a, b) log P(a, b) P(a)P(b) (2)
where a et b are respectively a source and a target words. Notice that P(a, b) is the joint probability and P(a) and P(b) are the marginal probabilities.

For each source unit a, we kept its k best target triggered units. Interestingly enough, this approach has been extended to take into account triggers of phrases (see Ref. [START_REF] Lavecchia | Phrase-based machine translation based on simulated annealing[END_REF]. The drawback of this method is that phrases are built in an iterative process starting from single words and joining others to them until expected size of phrases is reached. In others words, at the end of the first iteration, sequences of two words are built, the following iteration produces phrases of three words and so on until the stop-criteria is reached. Then, once all the source phrases are built, their corresponding phrases in the target language are retrieved by using n-to-n inter-lingual trigger approach (see Ref. [START_REF] Lavecchia | Phrase-based machine translation based on simulated annealing[END_REF]). In order to avoid the propagation of errors due to the cascade of steps in the previous method, we propose a new approach based on multivariate mutual information which allows to retreive source phrases given target once.

Description of the method

The new approach is based on multivariate mutual information. Before presenting our new approach, we introduce some necessary formalizations related to the multivariate mutual information (MMI).

Principle of multivariate mutual information

Typically, mutual information is defined and studied between just two variables.

Though the approach to evaluate bivariate mutual information is well established, several problems in multi-user information theory require the knowledge of interaction between more than two variables. Since there exists dependency between the variables, we cannot decipher their relationship without considering all of them at once. The seminal work on the information-theoretic analysis of the interaction between more than two variables was first studied in Ref. [START_REF] Mcgill | Multivariate information transmission[END_REF].

The definition of mutual information has been extended to e general case (over more than three variables) by Fano (see Ref. [START_REF] Fano | Transmission of Information: A Statistical Theory of Communication[END_REF]) and re-formulated in a lattice-theoretic framework by Han (see Ref. [START_REF] Sun | Multiple Mutual Informations and Multiple Interactions in Frequency Data[END_REF]). Though each have taken different approaches and the expressions are in terms of different entities (mutual informations on one case and entropies in the other), they can be simplified to be the same.

In this sense, we define the multivariate mutual information as follows:

M M I(X, Y ) = x1.
3.2. How to take advantage from multivariate mutual information in order to learn phrase translation?

Multivariate mutual information calculates the correlation between respectively n and m variables. This concept is very interesting since we propose to take advantage from this principle by associating n words in a source language to m words in a target language. The objective as in Ref. 17 is to use the principle of interlingual triggers except that we use a multivariate mutual information. As illustrative example, guess that we are interested by phrase of length 3 which are translated by two words. For instance, in French "le petit déjeuner " is translated by "the breakfast" in English. We can calculate directly the correlation degree between two linguistics units as follows:

M M I(v, w, x, y, z) = P (v, w, x, y, z) log P (v, w, x, y, z) P (v, w, x)P (y, z)

With v = "le", w = "petit", x = "déjeuner", y = "the" and z = "breakfast".

A new algorithm for training phrases

One of the famous algorithm allowing to develop a phrase based model (see Ref. 6) is based on splitting the source language on several segments and each segment is then translated. Segments correspond to what are called phrases, they are those which are consistent with the word alignment. Words are aligned bidirectionally and the phrases are those with a high union recall alignment. A reordering model is trained using a joint probability which has the role to put in order the phrases of the target language. A reordering model is trained using a joint probability which has the role to put in order phrases of the target language. Consequently, at least the following parameters are necessary to develop a phrase based model: a bidirectional phrase translation probability and a bidirectional lexical translation probability.

In this work, we start by identifying the longest phrases with thier translations and then the less longest and we finish with phrases of two words. This is motivated by the fact that we would like to appreciate the real contribution of each segment without the influence of its sub-segments. In fact, a long segment is linguistically more informative than a shorter one included into it. The algorithm we propose in the following is based on retrieving phrases and their translations by using multivariate mutual information without any alignment. Firstly, this algorithm provides a many-to-many translation table, and MMI permits to find phrases like x 1 , x 2 , ..., x m -→ y 1 , y 2 , ..., y n , such as m and n are respectively the maximal length of source and target phrases. suppose that an English phrase is in general longer than the French one. This is not true in all the cases but in most cases this hypothesis is true. By iterating the different steps of Algorithm 1, we get a list of phrases and their translations.

Algorithm 1 A phrase model based on multivariate mutual information 1: for i = lenM ax to 2 do

2:

Extract the n i best phrases (most frequent) from the french corpus F

3:

Extract the n i best phrases (most frequent) from the english corpus E 4: end for 5: for i = lenM ax to 2 do 6: MMI(f 1 , ..., f i , a 1 , ..., a i ) = P(f 1 , ..., f i , a 1 , ..., a i ) log P (f1,...,fi,a1,...,ai) P (f1,...,fi)P (a1,...,ai) 7: end for

Estimate probabilities for phrase pairs

Translation probabilities reflect the probability that a sequence of words in a source language translates into sequence of words in a target language. We use the principle proposed in Ref. [START_REF] Lavecchia | Phrase-based machine translation based on simulated annealing[END_REF] to compute phrase translation probabilities. In our algorithm phrases and their translations are obtained by selecting those which have the best values of multivariate mutual information. In order to build, a translation table which can be processed by Moses, we have to transform each MMI into a probability. For that, we proceed as in Ref. [START_REF] Tillmann | Word triggers and the em algorithm[END_REF] ∀f, e i ∈ T rig(f

)P (e i |f ) = M M I(e i , f ) ei∈T rig(f ) M M I(e i , f ) (5)
Where Trig(f ) is the set of k English segments triggered by the French phrases f. Table I illustrates retrieved phrases by our algorithm. The first column presents French phrases. For each French phrase, best corresponding translations are presented in column 2. The third column indicates the value of MMI assigned to the translation proposed in the second column. In the same way, the fourth column indicates the probability. A qualitative analysis showed that our method leads to pertinent inter-lingual triggers. Thus, triggered sequences could often be considerated as potential translation of the triggering French phrase. And finally the fourth column shows the probability of the french phrase. It is calculated as we have shown in equation 5.

Experiments

In this section we evaluate our phrase-based system based on Multivariate Mutual Information. The experiments presented below have been conducted on the proceeding of the European Parliament (see Ref. [START_REF] Koehn | Europarl: A multilingual corpus for evaluation of machine translation[END_REF]). We used French-English parallel corpus. Table 2 gives details about the used the parallel corpus. We use a train corpus to extract French phrases and to compute inter-lingual triggers (few examples are given in table 1). A development corpus is used to select the best phrase translations among all those determined by the set of inter-lingual triggers. Finally, we use a test corpus to validate our approach. 3 shows the results obtained with our method are close to the baseline system. Table 4 illustrates the evolution of BLEU in accordance to the size of phrases introduced in translation table. The first conclusion is the one that proved several years ago by the community that the introduction of phrases improve the results. Results S1 corresponds to word-to-word translation and S8 corresponds to a translation using all the possible phrases. The improvement eceeds 9 points which is a considerable achievement. Then what we can remark is that, the introduction of phrases of 8,7 and 6 words improve the results with more than 2 points. This mean that influence of long phrases is suitable but not as relevant as the introduction of phrases of 5 words. These phrases bring more than 2.5 in terms of BLEU. But the best improvement is brought by sequence of words of 4,3, and 2 words: more than 4.5! Consequently, all these sequences of different sizes are necessary to improve the results. Phrases beyond of 8 words are not relevant.

Conclusion

The proposed approach in this work is based on the concept of multivariate mutual information. This measure is used to determine directly many-to-many phrases. The first positive result is that this approach allowed to find out valid linguistic phrases and their corresponding translations. The second advantage is that our method does not need word alignments. Technically we do not need to include any alignment variable in the calculation of the translation probability. So, the translation probability is calculated directly through the correlation between the source and the target corpora. The matching between the source and the target segments is handled by associating the best target segment to a source segment. Consequently, the calculation of the translation table is faster than the baseline method and provides results less noisy. The investigation we did to explain the difference between our method and the baseline system is likely due to the non discriminative probabilities of our translations. Indeed, the translation probability assigned to a pair of phrases is calculated by a standard normalization of the multivariate mutual information, the consequence is that the probabilities are close to each other and this does not allow a high discrimination between partial translations in the decoding step. Work is under progress to overcome this limit.

Table 1 .

 1 Examples of retrieved phrases and their translations

	French	English	MMI	Prob	Table 2. An overview of the experimen-
	autour de around	0.0054	0.276	tal material
		around the 0.0022	0.122	Corpus Sentences English	French
	a été prise was taken	0.001	0.212		words	words
		been taken 0.00037 0.074	Train	596831	15138093 16613485
		taken	0.00034 0.068	Dev	1444	14077	13776
	semaine	last week	0.016	0.194	Test	500	1153	1352
	dernière	week	0.0095	0.16
		last	0.009	0.158

Table 3

 3 We compare the output of MOSES (see Ref. 19) using its default phrase table (refined alignments from Giza++ (see Ref. 20)), against those produced by our method. For the language model, we use a trigram for all the experiments. Results are presented in table 3 ans 4 in terms of BLEU (see Ref. 21). Table

			Table 4. Evolution of BLEU in accor-
			dance of phrases's types
	. Evaluation results on the Eu-	Set Selected Triggers BLEU
	roparl corpora : French to English MT	S 1	1FR -→ 1EN	34.16
	task. The test corpus contains 500 aligned sentences	S 2 S 3	S 1 + 8FR S 2 + 7FR	36.32 36.36
	System BLEU	S 4	S 3 + 6FR	36.8
	Baseline	44.3	S 5	S 4 + 5FR	39.58
	MMI	43.79	S 6	S 5 + 4FR	41.12
			S 7	S 6 + 3FR	43
			S 8	S 7 + 2FR	43.79


Julien Modolo

Alex W Thomas

Alexandre Legros
email: alegros@lawsonimaging.ca

Human Exposure to power frequency Magnetic Fields up to 7.6 mT: an Integrated EEG/fMRI Study

Keywords: Extremely low-frequency magnetic fields, 50 and 60 Hz, human, EEG, fMRI, threshold

We assessed the effects of power-line frequency (60 Hz in North America) magnetic fields (MF) in humans using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Twenty-five participants were enrolled in a pseudodouble-blind experiment involving "real" or "sham" exposure to sinusoidal 60 Hz MF exposures delivered using the gradient coil of a MRI scanner following two conditions: 1) 10 second exposures at 3 mT (10 repetitions); 2) 2 second exposures at 7.6 mT (100 repetitions). Occipital EEG spectral power was computed in the alpha range (8-12 Hz, reportedly the most sensitive to MF exposure in the literature) with/without exposure.

Brain functional activation was studied using fMRI blood-oxygen level dependent (BOLD, inversely correlated with EEG alpha power) maps. No significant effects were detected on occipital EEG alpha power during or post-exposure for any exposure condition. Consistent with EEG results, no effects were observed on fMRI BOLD maps in any brain region. Our results suggest that acute exposure (2 to 10 s) to a 60 Hz MF from 3 to 7.6 mT (30,000 to 76,000 times higher than average public exposure levels for 60 Hz MF) does not induce detectable changes in EEG or BOLD signals. Combined with previous findings in which effects were observed on the BOLD signal after a 1 h exposure to a 3 mT, 60 Hz MF, this suggests that MF exposure in the low mT range (< 10 mT) might require prolonged durations of exposure to induce detectable effects.

Introduction

Experimental evidence suggests that human exposure to extremely low-frequency (ELF, < 300 Hz) magnetic fields (MF) can modulate specific aspects of human physiology [START_REF] Sastre | Nocturnal exposure to intermittent 60 Hz magnetic fields alters human cardiac rhythm[END_REF][START_REF] Sait | A study of heart rate and heart rate variability in human subjects exposed to occupational levels of 50 Hz circularly polarised magnetic fields[END_REF][START_REF] Graham | Exposure to strong ELF magnetic fields does not alter cardiac autonomic control mechanisms[END_REF][START_REF] Sastre | Brain frequency magnetic fields alter cardiac autonomic control mechanisms[END_REF][START_REF] Ghione | Human head exposure to a 37 Hz electromagnetic field: effects on blood pressure, somatosensory perception, and related parameters[END_REF][START_REF] Tabor | Influence of 50 Hz magnetic field on human heart rate variability: linear and nonlinear analysis[END_REF] and neurophysiology [START_REF] Heusser | Influence of an alternating 3 Hz magnetic field with an induction of 0.1 millitesla on chosen parameters of the human occipital EEG[END_REF][START_REF] Cook | Resting EEG is affected by exposure to a pulsed ELF magnetic field[END_REF][START_REF] Marino | Effect of low-frequency magnetic fields on brain electrical activity in human subjects[END_REF][START_REF] Cook | Resting EEG effects during exposure to a pulsed ELF magnetic field[END_REF][START_REF] Ghione | Effects of 50 Hz electromagnetic fields on electroencephalographic alpha activity, dental pain threshold and cardiovascular parameters in humans[END_REF]Legros et al., 2011a;[START_REF] Legros | Effects of a 60 Hz Magnetic Field Exposure Up to 3000 muT on Human Brain Activation as Measured by Functional Magnetic Resonance Imaging[END_REF]. Due to the continuously increasing human exposure to ELF MF since the industrial revolution, there is a growing need to understand the effects and underlying mechanisms of ELF MF exposure in humans. The levels of ELF MF for safe exposure are evaluated by international organizations such as the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the Institute of Electrical and Electronics Engineers (IEEE), who provide recommendations aiming to protect the general public and workers [START_REF] Icnirp | Guidelines for limiting exposure to time-varying electric, magnetic, and electromagnetic fields (up to 300 GHz). International Commission on Non-Ionizing Radiation Protection[END_REF]IEEE, 2002]. In the current guidelines (from 2010), ICNIRP guidelines are expressed in terms of electric field induced at the level of tissue [START_REF] Icnirp | Guidelines for limiting exposure to time-varying electric and magnetic fields (1 Hz to 100 kHz)[END_REF], and provide a recommendation of 0.12 V/m at 60 Hz (corresponding, for occupational exposure, to 1000 µT according to ICNIRP, or 2710 µT according to IEEE). This value results in a MF flux density of 6366 µT using a 0.1-meter radius head (maximal value for occupational exposure of the CNS tissue of the head). For comparison purpose, the average level of 60 Hz MF exposure for the general public is on the order of 0.1 µT [START_REF] Who | Extremely Low Frequency Fields Environmental Health Criteria Monograph[END_REF]. Obviously, these values have to be taken with caution, since using an homogeneous sphere model is a simplification. Indeed, the detailed head geometry, tissue anisotropies in electrical conductivity and realistic, non-homogeneous magnetic field distributions have to be considered for accurate estimates.

Previous studies suggest that global head exposure to an ELF MF can modulate electroencephalographic activity (EEG) and evoked potentials in healthy human volunteers [START_REF] Lyskov | Influence of short-term exposure of magnetic field on the bioelectrical processes of the brain and performance[END_REF][START_REF] Lyskov | Provocation study of persons with perceived electrical hypersensitivity and controls using magnetic field exposure and recording of electrophysiological characteristics[END_REF][START_REF] Cook | Resting EEG is affected by exposure to a pulsed ELF magnetic field[END_REF][START_REF] Marino | Effect of low-frequency magnetic fields on brain electrical activity in human subjects[END_REF][START_REF] Cook | Resting EEG effects during exposure to a pulsed ELF magnetic field[END_REF][START_REF] Ghione | Effects of 50 Hz electromagnetic fields on electroencephalographic alpha activity, dental pain threshold and cardiovascular parameters in humans[END_REF]. For example, a possible increase in EEG alpha (8-12 Hz) activity due to a specific pulsed ELF MF exposure has been suggested [START_REF] Cook | Resting EEG is affected by exposure to a pulsed ELF magnetic field[END_REF][START_REF] Cook | Resting EEG effects during exposure to a pulsed ELF magnetic field[END_REF]. In terms of effects on brain functional activity (measured using functional magnetic resonance imaging, fMRI), a 1 h human exposure to a 3 mT sinusoidal MF at 60 Hz has been reported to result in a modulation of brain activation associated with a finger tapping task or a mental rotation task [START_REF] Legros | Effects of a 60 Hz Magnetic Field Exposure Up to 3000 muT on Human Brain Activation as Measured by Functional Magnetic Resonance Imaging[END_REF]. Interestingly, EEG alpha activity is known to be negatively correlated with the Blood Oxygen Level Dependent (BOLD) signal [START_REF] Laufs | EEG-correlated fMRI of human alpha activity[END_REF] measured by fMRI.

Since previous work reports that exposure to ELF MF increases EEG alpha oscillations of healthy subjects [START_REF] Cook | Resting EEG is affected by exposure to a pulsed ELF magnetic field[END_REF][START_REF] Ghione | Effects of 50 Hz electromagnetic fields on electroencephalographic alpha activity, dental pain threshold and cardiovascular parameters in humans[END_REF], ELF MF exposure should have resulted, if fMRI had been used in parallel, in decreased BOLD activation. Reported effects of ELF MF on brain activity often indicate that the human EEG is prone to modulation in the occipital lobe in the alpha band [START_REF] Cook | Human electrophysiological and cognitive effects of exposure to ELF magnetic and ELF modulated RF and microwave fields: a review of recent studies[END_REF][START_REF] Cook | Resting EEG is affected by exposure to a pulsed ELF magnetic field[END_REF][START_REF] Ghione | Effects of 50 Hz electromagnetic fields on electroencephalographic alpha activity, dental pain threshold and cardiovascular parameters in humans[END_REF]. Importantly, the duration of exposure is a critical factor to take into account to address the problem of biological mechanisms involved into the experimentally recorded effect: in the case of biological effects reported after the cessation of prolonged exposure (> 30 min), it has indeed been suggested that changes in synaptic plasticity could be involved [Modolo et al., 2013a;Modolo et al., 2013b] with some recent experimental support for this hypothesis [START_REF] Xiong | Changes of dendritic spine density and morphology in the superficial layers of the medial entorhinal cortex induced by extremely low-frequency magnetic field exposure[END_REF]. In the case of acute exposure, changes in synaptic plasticity are unlikely since such effects take time to develop. Since recommendations at the core of ICNIRP and IEEE recommendations are based on the effects of acute exposure, there is a strong need to understand and characterize the effects of acute MF exposure at power frequencies. More specifically, ICNIRP and IEEE recommendations are based on the most reliable biological effect on humans from MF exposure, which is the perception of magnetophosphenes (flickering lights perceived eyes closed when exposed to a sufficiently strong MF) [START_REF] Lovsund | Magnetophosphenes: a quantitative analysis of thresholds[END_REF][START_REF] Icnirp | Guidelines for limiting exposure to time-varying electric and magnetic fields (1 Hz to 100 kHz)[END_REF]. The threshold for magnetophosphene perception, which is still not precisely evaluated (taken into account in guidelines by a safety factor), needs to be therefore explored and exceeded to consolidate the estimation of its value, using high levels of MF exposure. Therefore, in this paper, we studied the effect of acute MF exposure at power frequencies on human brain activity using hybrid neuroimaging, for MF flux density values exceeding current ICNIRP and IEEE recommendations, in order to observe acute brain tissue responses (different from magnetophosphenes).

To achieve this objective, we used EEG, a measurement of electrical activity produced by cortical sources with excellent temporal resolution (on the millisecond scale). However, EEG has fundamental limitations regarding 1) the spatial resolution (on the order of the square centimetre), and 2) the inability to measure the activity from deep brain regions.

Therefore, in order to compensate for these limitations and provide novel insights into the effects of power-frequency MF on human brain activity, we also used, simultaneously, functional magnetic resonance imaging fMRI. fMRI offers a greatly improved spatial resolution compared to EEG (cubic millimeter). By combining EEG and fMRI, we chose complementary imaging modalities in an original approach in bioelectromagnetics to characterize possible spatiotemporal patterns of brain activity changes and improve existing characterization of the MF flux density threshold resulting in reliable effects in humans.

A typical fMRI experiment involves a repetitive pattern of "task/no task" blocks, with the BOLD signal being measured in "task" and "no task" conditions. The BOLD signal is then compared between conditions, in order to identify brain regions involved during a specific task. In this paper, we used the MRI scanner itself to deliver the MF exposure, using the Z-gradient, as developed and described previously by our group [Legros et al., 2011b]. In the present protocol, no specific task was performed, and the "task" condition corresponded to the delivered MF exposure. The "no task" condition corresponded to a period free of exposure immediately following the "task" period. Unfortunately, since the MRI scanner is used both to deliver the exposure and to measure functional brain activity while the subject lies within the scanner, it is not possible to measure the BOLD signal during the "task" condition, but rather just after. However, the temporal course of the BOLD response to a stimulus is relatively slow, and features a small post-stimulus "dip" of negative amplitude following the larger positive response (due to a blood flow increase) during the task itself [START_REF] Kim | Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals[END_REF]. As a consequence, we hypothesized the following: 1) Performing BOLD measurements immediately after MF exposure should enable the measure of possible changes in brain functional activity due to acute MF exposure.

2) Acute 60 Hz MF exposure should induce a transient decrease of EEG alpha activity in the occipital cortex along with a decrease of functional brain activation as measured by BOLD in this region.

3) Magnetophosphene perception, if reported by subjects, should be associated with a transient decrease of EEG alpha activity and BOLD signal in the occipital (visual) cortex.

Hypothesis 2 is based on the fact that the EEG alpha rhythm and BOLD response are inversely correlated, therefore an alpha decrease should result in a positive BOLD response during the stimulus, and to a negative BOLD "dip" post-stimulus. These hypotheses where tested through an experimental protocol testing two different flux/exposure durations, which were defined according to the maximal capabilities of our scanner.

Methods

General. A total of N=25 (n=13 for Group 1 -5 males and 8 females-, n=12 for Group 2, -5 males and 7 females-) healthy volunteers (age 24.16 ± 6.2, standard error of the mean) were enrolled in this experiment, with ethics approval (REB #17816) from the Health Sciences Research Ethics Board of Western University (London, ON, Canada). Exclusion criteria included the presence of any metallic object implanted in body, the possibility of pregnancy (as a precautionary measure and required statement of our Ethics committee), and intake of caffeine, alcohol or tobacco in the last 24 h; or any cardiovascular/neurological disorder. After providing consent to participate to the experiment, subjects were equipped with a 64-channel MRI-compatible EEG cap (Compumedics-Neuroscan, Charlotte, NC) before moving to the MRI control room.

Participants were then asked to fill an MRI screening questionnaire to ensure participant safety in the MRI environment. After the experimenter reviewed the MRI screening questionnaire to confirm safety requirements were met, the participant was setup on the MRI patient table with a pulse oximeter (for post-processing of ballistocardiogram artefacts) on the index finger from one hand and a button-press on the other hand to report possible magnetophosphene perception (flickering lights perceived when exposed to sufficiently strong time-varying magnetic fields, see [START_REF] Lovsund | Magnetophosphenes: a quantitative analysis of thresholds[END_REF]).

Participants had to wear earplugs during the entire experiment to reduce the noises produced by the MRI scanner, and also to prevent them from hearing the slight buzzing sound produced by the MRI scanner during the 60 Hz MF exposure. When subjects were equipped with earplugs, they were unable to detect this buzzing sound that could provide, in theory, an auditory clue regarding the MF exposure status. After direct interaction with the participant was finished, they were then randomly assigned to one of two different groups (Fig. 1). Subsequent interaction with the subject was through the MRI intercom using scripted, standardized instructions before each sequence. For each sequence during which EEG and/or BOLD signals were acquired, subjects were instructed to remain calm, stay still and let their mind wander. The pseudo-double-blind was partially broken at this time as the operator was now aware of the MF exposure condition.

MF exposure. 60 Hz MF exposures were delivered using a special programming of the gradient system within a Siemens 3T MRI/PET Biograph scanner (Siemens, Munich, Germany) dedicated to research purposes. This scanner has dual simultaneous MRI/PET capability, however, the present experiment was involving the MRI functionality only and none of the PET related functions were used at any time. Two different levels of MF flux density were used in this experiment (as mentioned in Fig. 1) with a different duration and imaging paradigm for each: 3 mT (10 s), and 7.6 mT (2 s). As mentioned previously, these MF flux density values were chosen as the best compromise between the scientific questions addressed, the chosen imaging modalities, and the technical constraints of using the MRI scanner as an active 60 Hz sinusoidal MF exposure device.

First, regarding the 3 mT (10 seconds) sequence, 3 mT was actually the highest MF flux density that we were able to reach (by moving the MRI table further away from the isocentre) without compromising BOLD images quality, while using the maximal Zgradient field value (38 mT/m). Regarding the duration of 10 s, it was fixed in order to acquire a few BOLD images after each block of exposure, which resulted in 8 BOLD images per block of 10 seconds. We therefore chose the values of 3 mT and 10 seconds were obtained. Second, regarding the 7.6 mT (2 s) sequence, 7.6 mT was the highest MF flux density value reachable at the maximal Z-gradient field value (38 mT/m) at the maximal possible distance from the isocentre still in the linearity zone of the gradient field (20 cm). This resulted in a MF flux density value of 7.6 mT. The duration itself was chosen to be short, in order to capture a possible immediate, acute EEG response to the MF exposure, while being sufficiently long in order to allow EEG analysis during the MF exposure itself. We have therefore set this duration to 2 s. Let us mention that, in theory, it would have been possible to include in the protocol periodic patient table movements further in the bore in the Z-direction to obtain a higher MF flux density before resuming to the original position to acquire fMRI images. However, these numerous table movements may had induced electric current in the subject (movement within the MRI 3T static field) with different frequency content and intensity than those of interest, potentially interfering with the effects we aimed to identify. Finally, during each exposure condition, subjects had to keep their eyes closed in order to maximize alpha activity, and also to allow for magnetophosphene perception (which are reported to be observed best eyes closed). EEG. Subjects were setup using an MRI-compatible system, 64-channel EEG cap connected to a Synamps 2 amplifier (Compumedics-Neuroscan, USA) synchronized with the MRI scanner clock. EEG electrodes were pre-filled using QuikCell sponges (Compumedics-Neuroscan, USA) before being expanded using injection of a saline solution to decrease electrodes' impedance below 10 kΩ. Impedances values were checked 1) during the EEG cap setup; 2) once the subject was setup on the MRI patient table before beginning the imaging and exposure protocol; and 3) between each MRI sequence; to ensure optimal EEG signal quality throughout the entire experiment. To maximize signal-to-noise-ratio of the EEG signal in the MRI environment, the MRI Cold Head device (used to retain the helium cooling for the MRI coil in liquid form to maintain superconductivity) was turned "off" for the entire experiment. As can be seen in Figure 1, EEG was recorded continuously, even during imaging and MF exposure sequences, thanks to the MRI-compatibility of our EEG system. Artefacts induced in the EEG signals due to the MRI environment, such as ballistocardiogram or gradient field artefacts; were post-processed before bandpass-filtering between 5 and 15 Hz using CURRY 7.0 (Compumedics-Neuroscan, USA). The EEG spectral power in the alpha band was calculated for the "exposure" and "sham" conditions for occipital electrodes (O2, O1 and OZ), for each of the three MF exposure conditions aforementioned using Matlab (The Mathworks, USA) custom-made scripts. Regarding the 3 mT exposure sequence (10 s), we calculated the alpha power of the EEG on the averaged 10-second EEG responses both in the "sham" and in the "exposure" condition (over the 12 repetitions). Regarding the 7.6 (2 s on vs. 2 s off, 100 repetitions), we performed an averaging of the 100, 2 s epochs during exposure, similarly to the 100 epochs without exposure. An ANOVA with multiple comparisons and a within/between subject factor was conducted to detect possible significant effects of the 60 Hz MF exposure on EEG alpha power. The standard p-value of 0.05 was used as a threshold for significance (Bonferroni-corrected value). fMRI. Two novel fMRI sequences were designed and developed (in collaboration with Siemens Canada, Montreal) specifically for this experiment in order to perform functional imaging and deliver MF exposure within the same imaging sequence. The first sequence interleaved BOLD blocks with MF exposure blocks (see Fig. 2, top row), and aimed to detect post-stimulus decreases in BOLD signal [START_REF] Kim | Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals[END_REF]. In this design, we considered the 10-second, 3 mT MF exposure as the stimulus. Another constraint was that it is not possible to simultaneously perform BOLD imaging and deliver MF exposure (both involving the MRI scanner Z-gradient). For this sequence, a total of 96 functional images (12 repetitions with 8 images per repetition) were acquired using the following parameters: TR=2540 ms (repetition time), TE=45 ms (echo time), FOV=192x192 mm (field of view), matrix size 64x64 (image resolution), slice thickness=3.5 mm. MF exposure and BOLD imaging blocks were 10000.5 and 10160 ms in duration (10.0005 and 10.16 s), respectively. In other words, BOLD images were acquired during blocks of 10 seconds, which immediately followed each exposure block (either "real" or "sham"). The second sequence, used for the 7.6 mT conditions (see Fig. 2, bottom row) aimed to provide MF exposure (100 blocks of 2 s separated by 2 s without exposure for the 7.6 mT condition) while the EEG was simultaneously recorded. The design of these sequences is illustrated in Figure 2. In order to combine functional and anatomical images during post-processing, a complete anatomical image of each subject's brain was acquired (176 images total) prior to functional images. Anatomical and functional images were then imported in BrainVoyager QX 2.8 (Brain Innovation, The Netherlands), and correlated before being transformed in the standard Talairach space to enable group analysis. Functional images were then analyzed using BrainVoyager QX using a general linear model (GLM), while the Bonferroni correction was utilized to account for multiple comparisons.

Note that, in an attempt to deliver with the MRI Z-gradient the highest MF flux density possible, we identified a maximum value of 3 mT as a compromise between the quality of fMRI images and the MF flux density. As a reminder, at the MRI bore isocentre, the MF flux density has zero variation applied to the static MF (3T) and linearly increases up to a certain distance from the isocentre along the Z-axis before decaying (for our specific scanner, 20 cms). In this study, the maximal usable Z-gradient value avoiding MRI amplifiers safety shutoff (built-in MRI software safety preventing amplifiers heating and damage) was 38 mT/m, translating into a maximal MF flux density variation of 7.6 mT that can be repeatedly delivered during short periods of time (on the order of three s).

Regarding the use of fMRI along with MF exposure, given that the quality of fMRI images can be preserved up to approximately 8 cms from the isocentre on our specific MRI scanner, this resulted in a 3 mT MF exposure using a Z-gradient value of 38 mT/m. In addition, in an attempt to use the maximum MF flux density, we also included a condition including EEG only (no fMRI) with repeated 7.6 mT MF exposure (as detailed in the Methods Section) at 20 cms from the isocentre. This supplementary condition involving EEG was added to identify possible responses evoked by short MF stimulus (event-related potentials, ERPs). Let us mention that the same sequence was used for the 7.6 mT in both groups, since it involved both exposed and non-exposed epochs (2 seconds each, 100 repetitions), enabling the comparison of EEG alpha power with and without exposure. This protocol was designed on the model of an Evoked-Related Potential (ERP) protocol, treating the MF exposure as the stimulus, hence we did not include a sham-sham condition (which would correspond to look for a response to no stimulus).

Questionnaires

Before being setup in the MRI scanner, subjects had to complete a standard MRI screening questionnaire for their safety, ensuring that they did not have metallic implants or tattoos for example. Subjects were then setup in the MRI room once the experimenter validated satisfactory replies to the MRI screening questionnaire (no counter-indication for MRI). After each of the three MF exposure conditions, subjects were asked to answer, through the MRI intercom, a questionnaire related to the MF exposure condition they just completed. They had to rate their level of comfort (1=best, 10=worst), and level of perceived stress (1=worst, 10=best) for each sequence. Subjects were also asked if they had any perception of magnetophosphenes (described as "flickering lights" occurring in the field of vision eyes closed in the absence of light).

Results

In this section, the acronyms "sham" and "exp" are used to designate the sham condition (no exposure) and the exposure condition, respectively.

Levels of stress and comfort

In the 3 mT 60 Hz sinusoidal MF condition, stress scores for exposed subjects were not significantly different from sham subjects (p=0.22, F=0.77, 7.3 in the sham group vs 6.5 in the exposed group, standard t-test). No significant difference in the level of comfort was found between the two groups (p=0.33, F=0.41, 3.5 in the exposed group vs 3.15 in the sham group). In the 7.6 mT condition, levels of stress were comparable (7.84 for the sham group vs. 7.66 for the exposed group) while the level of comfort was slightly decreased in the exposed group (3.08 in the exposed group vs. 2.77 in the sham group); but these differences in stress and comfort were not significant (p=0.42, F=0.7; and p=0.32, F=0.6; respectively). Overall, levels of stress and comfort were not statistically different in any of the two tested MF exposure conditions.

Occipital EEG alpha power

We present below results regarding EEG spectral power calculated on the occipital electrodes (O2, O1 and OZ) in the alpha range (8-12 Hz) for the two aforementioned MF exposure conditions.

• 3 mT, 60 Hz condition (10 s, 12 repetitions) We present in Figure 3 the EEG results regarding the 3 mT MF exposure condition, that involved 10 s duration blocks, with 12 blocks of exposure. As a reminder, subjects in Group 1 were never exposed to the 3 mT MF exposure during this sequence, while subjects in Group 2 were alternatively exposed (3 mT, 60 Hz) and not exposed (no MF). Error bars represent standard error of the mean (SEM).

The ANOVA conducted on the results shown in Figure 3 concluded that there was no main effect of the exposure factor ("no MF" vs. "MF") for any of the three occipital electrodes regarding EEG alpha power (O1: F = .278, p = .6, partial η 2 = .01, power = .08; O2: F = .001, p = .97, partial η 2 < .01, power = .05; OZ: F = .493, p = .49, partial η 2 = .02, power = .1); and no exposure x group ("actual exposure" vs. "sham exposure") interaction effect (O1: F = .950, p = .34, partial η 2 = .04, power = 0.15; O2: F = 2.057, p = .17, partial η 2 = .07 , power = .28; OZ: F = .108, p = .74, partial η 2 = .11, power = .06).

• 7.6 mT, 60 Hz condition (2 s, 100 repetitions) Finally, we present EEG results obtained in the second condition in Figure 4. Regarding the 7.6 mT MF exposure condition, results shown in Figure 4 regarding EEG alpha power did not identify any significant exposure effect for any of the three occipital electrodes (O1: F = 1.265, p = .27, partial η 2 = .05, power = .19; O2: F = 1.285, p = .27, partial η 2 = .05, power = .19; OZ: F = 3.483, p = .07, partial η 2 = .13, power = .43).

Brain functional activation -fMRI

In order to detect possible differences in brain functional activation due to the 3 mT 60 Hz MF exposures (10 s, 12 repetitions), we produced BOLD activation maps as a subtraction of "exposure" minus "rest" images (see Fig. 2), where the "exposure" condition could be either "MF exposed" ("exp") or "sham". We present in Figure 5 the GLM results regarding fMRI group results (N=25), and emphasize the left motor cortex and cerebellum. The reason for this emphasis is that, in a previous experiment, these regions have been shown to have their activity modulated in a finger tapping task after a 60-min exposure to a 60 Hz MF of identical MF flux density (3 mT, see [Legros et al., 2011b;[START_REF] Legros | Effects of a 60 Hz Magnetic Field Exposure Up to 3000 muT on Human Brain Activation as Measured by Functional Magnetic Resonance Imaging[END_REF]). However, since the experimental protocol was different (e.g., a motor task was involved, whereas no task was included in the present study), it was not obvious that these regions could be modulated in the present experiment. As can be seen in Figure 5, the GLM analysis did not reveal any brain region with significant change in functional activation, including the left cerebellum and motor cortex (p > 0.05 for every voxel, Bonferroni-corrected value). This result is consistent with the absence of effects on the EEG alpha power for this specific MF exposure condition. This lack of effects after a shorter MF exposure duration (but identical MF flux density and frequency) than in previous work [Legros et al., 2011b;[START_REF] Legros | Effects of a 60 Hz Magnetic Field Exposure Up to 3000 muT on Human Brain Activation as Measured by Functional Magnetic Resonance Imaging[END_REF] is commented in Discussion.

Buttonpress data

No subject among the 25 enrolled in this study reported any perception of magnetophosphenes, which was confirmed both from the absence of buttonpresses during the course of the experiment and from verbal confirmation when completing the questionnaire following exposure sequences. This absence of magnetophosphene perception is consistent with the lack of changes in EEG alpha activity or functional activity as assessed by BOLD.

Discussion

We have presented the results of an experiment integrating simultaneous EEG and fMRI investigating the effect of two 60 Hz MF exposure conditions of different MF flux densities (3 and 7.6 mT) and durations (2 and 10 s) on 1) cortical electrical activity, and 2) brain functional activity (related to oxygen consumption by brain tissue). To the best of our knowledge, this study is the first using EEG and fMRI simultaneously to explore the effects of power-line frequency MF above reference levels for the general public and workers. The MF flux density and exposure duration values were chosen as the best compromise between our MRI scanner specifications (linked to the MF flux density that it is possible to produce using the Z-gradient) and neuroimaging data quality in order to detect changes in brain electrical/functional activation due to acute MF exposure.

The fMRI BOLD maps produced, aiming to identify immediately post-exposure (60 Hz, 3 mT, 10 s, 12 repetitions) acute effects on brain functional activation did not identify any significant measurable effects. As mentioned previously, since the MF exposure was delivered using the MRI Z-gradient coil, it was not possible to perform at the same time fMRI BOLD and MF exposure, leading to the choice of measuring the well-established post-stimulus BOLD decreased response. Interestingly, EEG data provides a consistent view with BOLD (acquired simultaneously) results, suggesting an absence of EEG effects due to the 3 mT 60 Hz MF exposure on brain electrical or functional activity. It is worth mentioning that the level of MF flux density during this sequence was identical to previous research in our group [START_REF] Legros | Effects of a 60 Hz Magnetic Field Exposure Up to 3000 muT on Human Brain Activation as Measured by Functional Magnetic Resonance Imaging[END_REF], while the exposure duration was different (60-min in previous research, 10-second repeated 12 times in the present study).

In terms of accumulated time of exposure, it sums up to 2 min for the current study versus 60 min in previous research where significant effects on brain functional activation were identified [START_REF] Legros | Effects of a 60 Hz Magnetic Field Exposure Up to 3000 muT on Human Brain Activation as Measured by Functional Magnetic Resonance Imaging[END_REF]. Therefore, participants to the present study have been exposed for a duration 30 times lower, with an identical MF flux density, suggesting that MF exposure duration might be a critical parameter in possible neurophysiological effects. Our current results support that, for a 60 Hz sinusoidal MF at 3 mT, there might exist a minimal exposure duration (between 2 and 60 min) required to induce brain activity modulation; and that no acute effects are induced on brain electrical or functional activity. Importantly, our EEG and fMRI results consistently point at an absence of acute electrical/functional activity modulation due to a 10-second 3 mT MF exposure at 60 Hz. Regarding EEG results acquired at the highest 60 Hz MF flux density exposure in this study (7.6 mT, 2 s, 100 repetitions), no significant effects were found on occipital EEG alpha power (p=0.084).

The absence of neurophysiological effects in humans, observed in the present study as a result of 60 Hz MF exposures at 3 and 7.6 mT, is informative in terms of possible interaction mechanisms between power-line frequency MF and the central nervous system. Considering these results in the light of previous research in which effects were observed on brain functional activation with a longer MF exposure period [START_REF] Legros | Effects of a 60 Hz Magnetic Field Exposure Up to 3000 muT on Human Brain Activation as Measured by Functional Magnetic Resonance Imaging[END_REF] suggests the possibility of a "cumulative effect" of the exposure, which would take a certain time to buildup. One possible biological substrate for this effect is the modulation of 'synaptic plasticity', which is one plausible candidate explaining the persistence of effects after exposure, and also that effects take place after some time [Modolo et al., 2013a;Modolo et al., 2013b]. This possibility is supported by recent experimental results on synaptic plasticity mechanisms in rats exposed to 50 Hz MF [START_REF] Xiong | Changes of dendritic spine density and morphology in the superficial layers of the medial entorhinal cortex induced by extremely low-frequency magnetic field exposure[END_REF]. To summarize, our results do not provide support for acute effects on brain electrical activity and functional activation due to 60 Hz sinusoidal MF exposure up to 7.6 mT (i.e. about 76,000 times higher than the typical daily exposure of the public to power-line frequency MF). However, these results do not rule out the possibility that subtle residual effects might be induced at these levels for longer MF exposure periods.

Importantly, there remains the possibility that effects of the MF exposure protocols used in this study were affected by the onset of the MRI Z-gradient coil. When MRI gradient coils are turned on, the frequency content of the produced MF can reach several hundreds of Hertz, and it cannot be excluded that this interfered with the EEG or BOLD physiological response that we aimed to identify. That being said, in a previous study [START_REF] Legros | Effects of a 60 Hz Magnetic Field Exposure Up to 3000 muT on Human Brain Activation as Measured by Functional Magnetic Resonance Imaging[END_REF], we were able to identify changes in BOLD activation in a group exposed to a 60 Hz MF as compared to a sham group. This demonstrates, even if the exposure duration in this previous study [START_REF] Legros | Effects of a 60 Hz Magnetic Field Exposure Up to 3000 muT on Human Brain Activation as Measured by Functional Magnetic Resonance Imaging[END_REF] was longer than in the present study (60 min vs. 10 s) that the experimental protocol is in theory able to detect physiological changes due to MF exposure as assessed by BOLD.

One limitation of our study, and more specifically regarding functional brain activation results as measured by BOLD imaging, is that the post-stimulus decrease in the BOLD signal (post-stimulus, the stimulus being the 10-second MF exposure) that we were aiming to detect is lower in amplitude compared to the increase in BOLD signal that is expected during a motor or cognitive task. Consequently, it is not possible to exclude the possibility that the 60 Hz MF exposure induced a decrease in the BOLD signal immediately post-exposure, but that the signal-to-noise ratio was too low to be detected with our BOLD imaging parameters. One possible solution to overcome this issue would be to increase the number of BOLD images in the sequence, however this would increase the chances of subjects moving excessively or falling asleep given that the current sequence lasts over 8 min. Also, if the 60 Hz MF exposure effect is purely acute, it is possible that the associated BOLD response decays faster than it is possible to measure it, which might result in a failure to detect an effect. Second, the amplitude of the BOLD response undershoot is significantly smaller than the amplitude of the main (positive) BOLD response, which means that the amplitude of the effect due to the MF exposure might be too small to be detected using our experimental protocol. Third, there were no published values on using BOLD to image short-term effects of MF exposure, which we could have used as guides for our experimental protocol. Consequently, in the limits of what can done realistically using an MRI as an active MF exposure device, we did not detect any changes in post-stimulus brain activation due to 60 Hz sinusoidal MF exposures using BOLD. Based on the results from the O1 electrode for example, we can compute a posteriori for the 3 mT condition that the required number of subjects to reach a statistical power of 0.8 for our observed partial η 2 of .01 and p-value (on the order of .6) would have been 32. Therefore, what we can conclude from the statistical power of our study, is that our results do not point at any effect on EEG signals, but cannot completely exclude that some effects are present. If they exist, the effect size would be a 20% difference or less in the EEG alpha spectral power.

Finally, none of the subjects enrolled in this experiment reported the magnetophosphene perception. Previous reports indicated that the magnetophosphene threshold was between 5 and 10 mT at 20 Hz, and increases with frequency [START_REF] Lovsund | Magnetophosphenes: a quantitative analysis of thresholds[END_REF], therefore the absence of magnetophosphene perception in our study is consistent with these previous reports.

Conclusion

We conducted a simultaneous EEG/fMRI experiment in up to 25 healthy human volunteers to identify possible neurophysiological correlates of 60 Hz sinusoidal MF exposure effects. Two conditions of 60 Hz sinusoidal MF exposures were tested (3 mT, 10 s, 12 repetitions; 7.6 mT, 2 s, 100 repetitions). First, in the 3 mT condition, our results did not identify any significant acute effect on post-stimulus fMRI BOLD signal, which was confirmed by an absence of occipital EEG changes in the alpha range. Second, in the 7.6 mT condition where EEG only was monitored, there was no significant effects on occipital EEG alpha activity. Overall, we did not identify any significant effect of powerline frequency 60 Hz sinusoidal MF exposure at 3 and 7.6 mT for durations from 2 s to 10 s, either on human brain electrical activity as recorded by EEG, or functional activation as measured with fMRI BOLD. No magnetophosphene perception was reported by the subjects. Taken together, our results support the absence of specific acute effects on human brain activity as a result of acute power-line frequency MF exposure up to 7.6 mT. These results suggest that power-frequency MF cannot trigger acute effects on the central nervous system detectable using the currently fMRI and EEG technologies below the magnetophosphene detection threshold, which is consistent with the basic restrictions and the reference levels recommended by both ICNIRP and IEEE [START_REF] Icnirp | Guidelines for limiting exposure to time-varying electric and magnetic fields (1 Hz to 100 kHz)[END_REF]IEEE, 2002].

Fig. 1 .

 1 Fig. 1. Neuroimaging and 60 Hz MF exposure protocol followed for Groups 1 (N=13)

Fig. 2 .

 2 Fig. 2. Top row. fMRI sequence 1 designed to record possible evoked responses in the

Fig. 3 .

 3 Fig. 3. EEG alpha power (in µV 2 per Hz) in occipital electrodes without ("sham") and

Fig. 4 .

 4 Fig. 4. EEG alpha power (averaged over the 100 repetitions, expressed in µV 2 per Hz) in

Fig. 5 .

 5 Fig. 5. Example of GLM results in the left cerebellum (left panel) and in the left motor

Acknowledgments

The authors thank Drs. Jean Théberge (Lawson Health Research Institute, London, ON) and Raphaël Paquin (Siemens Canada, Montréal, QC) for their help in programming and installing the MF sequences on the MRI scanner.

Financial Disclosure

Grant sponsors: Hydro-Québec (Canada) (http://www.hydroquebec.com/en/); EDF-RTE (France) (http://www.edf.com/the-edf-group-42667.html and http://www.rtefrance.com/fr/). Grant sponsor: CIHR (Canadian Institutes of Health Research (http://www.cihrirsc.gc.ca/e/193.html); grant number: grant #187204. Grant sponsor: CFI/ORF (Canadian Fund for Innovation / Ontario Research Fund, http://www.innovation.ca/en); grant number: Biomedical Multimodality Hybrid Imaging grant #11358.

The funders had no role in study design, data collection and analysis, decision to publish, study approvals, or preparation of the manuscript.

Conflict of interest: none.

Running title: Integrated EEG-fMRI and 60 Hz MF up to 7.6 mT.


Boyang Zhao
email: zhaoboyang@stu.xjtu.edu.cn

Xifan Wang

Pierre Haessig

A Novel SoC Feedback Control of ESS for Frequency Regulation of Fractional Frequency Transmission System with Offshore Wind Power

Keywords: Energy Storage, frequency regulation, fractional frequency transmission system

The impact of high penetration of Renewable Energy Source (RES) and the decoupled power-frequency relationship raise the concern for RES participating in the grid frequency regulation. Energy Storage System (ESS) has shown its potential in power system with RES. This paper proposes a new control method of ESS considering the ancillary service efficiency and economical aspects. The proposed state of charge (SoC) feedback control is based on net-zero concept of frequency regulation model. A feedback gain is introduced to satisfy the grid regulations and economical index. The proposed control method is investigated over fractional frequency wind power system, which uses low frequency transmission lines or grids to transmit wind power. Simulations are done in Python and MATLAB/Simulink. The simulation results verify the effectiveness of proposed coordinate control strategy.

I. INTRODUCTION

Renewable Energy Source (RES) has been vastly integrated into power system during the last decades due to its sustainable and pollution-free feature. Widely using of double fed induction generator (DFIG) and permanent magnet synchronous generator (PSMG) leads to the dramatic increasing in installed capacity of wind turbine generation (WTG) where the global installation reaches 486.8 GW in 2016 [START_REF]Global wind report 2016[END_REF].

Fractional Frequency Transmission System (FFTS) was first proposed in 1994 and its feasibility has been analyzed [START_REF] Wang | The fractional frequency transmission system[END_REF], [START_REF] Wang | Feasibility study of fractional frequency transmission system[END_REF]. It is a very promising transmission method for wind power generation. By using low transmission frequency(50/3Hz), this novel method is able to increase the transmission capacity and reduce the fluctuation at the connection point of wind farm [START_REF] Liu | Integrating offshore wind power via fractional frequency transmission system[END_REF]. Relevant experiments have been published in [START_REF] Wang | Experiment on grid-connection process of wind turbines in fractional frequency wind power system[END_REF] as the system is called Fractional Frequency Wind Power System (FFWPS). Only onshore back-to-back MMC or M 3 C converter is used thus there is potential to incorporate energy storage system (ESS) into the converter to accomplish frequency conversion and energy storage function. High penetration of RES has brought the potential stability issues of power system considering the randomness and fluctuation of RES [START_REF] Lalor | Frequency control and wind turbine technologies[END_REF]- [START_REF] Delille | Dynamic frequency control support by energy storage to reduce the impact of wind and solar generation on isolated power system's inertia[END_REF]. Wind power generators are usually connected to the grid with power electronic converters to improve the power quality. However, these converters lead to the decoupled relationship between grid frequency and output power which decrease the reliability of power system [START_REF] Conroy | Frequency response capability of full converter wind turbine generators in comparison to conventional generation[END_REF]. In addition, wind turbines usually operate in Maximum Power Point Tracking (MPPT) mode, which results in the unavailability for wind turbine participating in frequency regulation. And for a regional grid where there is high penetration of RES, the equivalent system inertia is reduced which risks the frequency safety and stability with large frequency nadir and rate of change of frequency (ROCOF) [START_REF] Kovaltchouk | Assessment of the impact of frequency containment control and synthetic inertia on intermittent energies generators integration[END_REF], [START_REF] Wilches-Bernal | A fundamental study of applying wind turbines for power system frequency control[END_REF].

Several control strategies have been proposed to deal with issues mentioned above [START_REF] Sun | Review on frequency control of power systems with wind power penetration[END_REF]. The intuitive method is to emulate the inertia, which uses kinetic energy of rotor. The inertial response of DFIG using filed-oriented control is performed and analyzed in [START_REF] Mullane | The inertial response of inductionmachine-based wind turbines[END_REF]. However, it should be noted that there is a recovery duration for wind turbine restoring rotation speed, during which the WTG will produce less power than the nominal.

WTG can operate in deloading mode to save the available power reserve. The deloading control can be achieved by overspeeding the WTG [START_REF] Sun | Review on frequency control of power systems with wind power penetration[END_REF] or additional pitch control [START_REF] Ghosh | Doubly fed induction generator (dfig)-based wind farm control framework for primary frequency and inertial response application[END_REF]. No recovery process is needed since WTG no longer operates in MPPT, but both methods may introduce mechanical abrasion thus increase repair rate.

Introducing ESS into RESs for frequency regulation has promising feature as reducing WTG generation loss. Due to its high investment and maintenance cost, ESS is only able to reduce frequency excursion in a short period of time. Therefore, a wind farm level coordinate control is needed. [START_REF] Dang | Soc feedback control for wind and ess hybrid power system frequency regulation[END_REF] has proposed a state of charge (SoC) feedback control of wind/ESS hybrid power system for frequency regulation. However, this strategy is only valid in an isolated power system since conventional generators respond to frequency fluctuation when SoC of ESS is too high or too low. Sizing of ESS is also crucial as it impacts the performance of frequency regulation. A battery energy storage system (BESS) is also optimized for frequency control in [START_REF] Mercier | Optimizing a battery energy storage system for frequency control application in an isolated power system[END_REF], but this does not hold ture when power system is large.

A SoC feedback control of ESS for frequency regulation has been proposed in this paper. ESS control should both maintain the health of ESS and frequency regulation. The novelty of this paper lies in the net-zero concept of ESS control. It is based on the fact that power generation and consumption are always balanced in a certain time interval but cannot always match at each time instant, which is called net-zero correction here. In this sense, the ESS can produce less when the SoC is low but it can compensate the error when SoC is high to keep a net-zero error during a short period. Thus grid frequency is in fluctuation around its nominal value due to this correction and frequency regulation service is done. A feedback gain concerning the SoC of ESS then is introduced to realize the net-zero correction. Furthermore, a low-pass filter is used to decompose frequency signal fed to WTG and ESS for full utilization of frequency regulation potential of these units.

The outline of this paper is as follows: Section II presents short description of simulation system modeling; Section III proposes a coordinate SoC feedback control; Section IV gives the simulation results and performance of coordinate SoC feedback control and conclusions are presented in Section V.

II. SYSTEM MODELING

A. Wind Turbine Generator Description

DFIG and PMSG are now the most applied WTGs in wind power generation due to its variable rotation speed feature. The power electronic converters allow fast output control of WTGs. There are manufactures that provide high-capacity WTGs among which GE has published its DFIG dynamic model in [START_REF] Miller | Dynamic modeling of ge 1.5 and 3.6 mw wind turbine-generators for stability simulations[END_REF]. Since the time constant of converter is much larger than mechanical time constant, power converter has been simplified as a first-order inertial element. Therefore, only mechanical model of DFIG will be discussed.

Single-mass rotor model is introduced to present the DFIG mechanical dynamics.

2Hω m ω m dt = P m -P e (1)
P m and P e represent the mechanical power extracted from wind turbine and electrical active power of DFIG respectively. ω m is the rotor rotation speed and H is inertial constant. Mechanical power P m yields to (2) and depends on air density ρ, WT blades swept area A, wind speed v wind , pitch angle β , and tip-speed ratio λ.

P m = 1 2 C p (λ, β)ρA(v wind ) 3 (2)
The performance coefficient C p (λ, β) is fitted by a fourth order polynomial, which is The coefficients and detailed control model of DFIG can be found in [START_REF] Miller | Dynamic modeling of ge 1.5 and 3.6 mw wind turbine-generators for stability simulations[END_REF], [START_REF] Ullah | Temporary primary frequency control support by variable speed wind turbines-potential and applications[END_REF]. Experimental C p (λ, β) curve is shown in Fig. 1.

C p (λ, β) = 4 i=0 4 j=0 α i,j β i λ j (3)

B. ESS Description

Energy storage is used for frequency regulation in coordination with WTG. In this paper, the ESS is incorporated in the onshore back-to-back MMC or M 3 C converter of FFTS which can save construction cost of ESS. There are numerous types of utility scale battery or ultra-capacitor with fast transient can be introduced in converters mentioned above. Therefore, a system-level simplified battery ESS model is used where inverter connected to ESS is assumed to be lossless. ESS output P ESS is always satisfied when the request charing/discharging power P req can be reached and thus saturation is manged, as shown in [START_REF] Liu | Integrating offshore wind power via fractional frequency transmission system[END_REF] where ∆t is simulation time step. Note that when P ESS is positive (negative), ESS is discharging (charging).

P ESS =      SoC×E ESS ∆t for P req > SoC×E ESS ∆t (SoC-1)×E ESS ∆t for P req < ((SoC-1)×E ESS ∆t P req for else (4)
Practically, SoC of ESS is set in a certain interval to ensure lifetime, stability concerns and deterioration mitigation. Thus, typical SoC upper and lower limits are set to be 0.8 and 0.2 respectively.

E ESS here denotes the capacity of ESS. A 15 minutes of 2% wind farm installed capacity should be satisfied in wind farm participating frequency regulation as described in [START_REF] On | Germany grid code regulations for high and extra high voltage[END_REF]. However, this capacity is the most conservative choice which means only ESS participates frequency regulation without WTGs in wind farm. Thus a smaller capacity ESS is used in this simulation which is 15 minutes of 1% wind farm installed capacity.

C. Load Description

Imbalance in generation and consumption leads to frequency fluctuation. Therefore, the changing frequency can reflects some load changing information. A typical frequency pattern [START_REF] Rte | Network frequency[END_REF] is presented in Fig. 2. Fig. 2. Frequency Daily Pattern Thus the power system load is defined in [START_REF] Wang | Experiment on grid-connection process of wind turbines in fractional frequency wind power system[END_REF] where k L is static load-frequency coefficient and set to be 1.5. f and f N are measured and nominal grid frequency respectively.

∆P L = -k L (f -f N ) (5)

D. Simulation System Description

Classic Load-Frequency Control (LFC) is used in this paper. The structure of power system simulation model is depicted in Fig. 3. It consists of conventional steam turbine, DFIG wind farm, ESS, load and power system model. DFIG wind farm, ESS and load has already been described above. Steam turbine is composed by governor model and reheat turbine model. R denotes the speed regulation parameter of the governor and T G is the mechanical governor response time. K r , T r are steam turbine reheat constant and reheat time constant respectively. T t is the steam turbine time constant. Power system is represent by inertia constant H and load-damping coefficient D. The parameters of power system and steam turbine are given in Appendix.

III. COORDINATE CONTROL STRATEGY

WTG should no longer operate in MPPT when it participates in frequency regulation service. Overspeed and pitch control are most discussed deloading control method for WTG participation frequency regulation [START_REF] Sun | Review on frequency control of power systems with wind power penetration[END_REF], [START_REF] Loukarakis | Frequency control support and participation methods provided by wind generation[END_REF]. However, overspeed cannot correctly function without pitch control when wind speed is low so that coordinate control is necessary. As a result, this control method is implemented by DFIG deloading control realized by pitch control and ESS SoC feedback control.

A. DFIG Deloading Control

Pitch angle is considered to be smaller value in order to abstract more mechanical power from wind turbine. To deload wind turbine, an additional angle is introduced to maintain a certain deloading coefficient k del . The required pitch angle value is approximately linear to desired deloading power [START_REF] Loukarakis | Frequency control support and participation methods provided by wind generation[END_REF]. A proportional gain is used to modify pitch angle θ respond to frequency deviation ∆f as emulation of droop control for steam turbine. The complete deloading control block diagram is shown in Fig. 4. T servo is the time constant of pitch blade actuator.

B. SoC Feedback Control

Capacity of battery ESS is limited thus its operating condition can be critical, where the upper and lower bounds are possibly reached. Practically, in order to expand the life of ESS, feedback control of ESS is introduced. As mentioned in Section II, the upper and lower limits are typically 0.8 and 0.2 respectively. The convention is always set the reference SoC to be 0.5 [START_REF] Dang | Soc feedback control for wind and ess hybrid power system frequency regulation[END_REF], however, it will lessen cycling life of ESS and impair the ESS performance since frequency deviation signal is independent of SoC.

In this paper, the adjustable reference SoC is implemented to fully utilize the battery ESS system. SoC reference value SoC ref yields to [START_REF] Lalor | Frequency control and wind turbine technologies[END_REF], when SoC exceed its bounds, a penalty function in [START_REF] Li | Battery energy storage station (bess)based smoothing control of photovoltaic (pv) and wind power generation fluctuations[END_REF] is added to bring SoC into desired operation interval. The complete coordinate control scheme is depicted in Fig.

DFIG wind farm responds to low-frequency component of frequency deviation and high-frequency component is fed to

ESS where the total power of DFIG and ESS is the output of FFWPS. For the ESS control, An independent droop controller is introduced to simulate governor 5% droop behavior. Also, it is noticed that there is a power error between desired and practical ESS output. In long-term operation, the root mean square value of this power error, also called PRMSE, should be minimized with feedback gain chosen carefully. In addition, load and generation may not be the same at each time instant. However, its must be equalized in a certain period of time. AGC is used to fulfill this function. Thus, a net-zero production and consumption is observed and net-zero of frequency deviation is achieved. Based on this concept, the optimization of PRMSE means the optimum control of wind farm participating frequency regulation.

IV. SIMULATION RESULTS

In this section, the proposed coordinate control scheme is verified in MATLAB/Simulink and Python, also the simulation results are presented. An FFWPS without ESS for participating frequency regulation is used as benchmark to outline the effectiveness of proposed control strategy.

A. Original Simulation System

A 594MW offshore wind farm is considered in this benchmark system. It is composed by 165 GE 3.6MW DFIGs as discussed above. 4 days have been randomly selected to represent the typical wind farm output during different seasons. The wind farm output curve is shown in Fig. 6. For the simplicity of simulation, only the spring wind output is considered in the following cases.

B. Benchmark System

The power system is set to have wind power penetration as 20%, deloading factor k del is 10% and the droop of wind farm is 5%. Detailed parameters is given in Appendix. Load is described as Section II. Note that no ESS is implemented in this system, thus low-pass filter is no longer needed. Then the FFWPS output without ESS for frequency regulation is shown in Fig. 7 . Pitch angle is shown in Fig. 8 in comparison of MPPT mode. Obviously, by comparing Fig. 6 and Fig. 7,the power generation of wind farm is decreased due to deloading control. Fluctuation of wind farm is observed for the primary frequency regulation. A 10% deloading is necessary for frequency regulation. As described before, pitch control is applied for the deloading and frequency regulation so that the pitch angle is frequently adjusted as shown in Fig. 8. Mechanical abrasion may reduce the life of DFIG and introduce additional maintenance cost.

C. Test System

Incorporating ESS into FFWPS is beneficial to reducing mechanical abrasion and generation loss. Low-pass filter is implemented for the ESS application. Chosen of time constant τ of this filter needs to be optimized and is not discussed here. In this simulation, τ = 15 min, and other parameters remains same as benchmark system. Fig. 9 and Fig. 10 are the wind farm output power and pitch angle of DFIG. The ESS output reference in this case is shown in Fig. 11. In this case, fluctuation of wind farm output is significantly reduced and pitch angle adjusting frequency is intensely decreased. This is due to the low-frequency component of frequency deviation is fed to DFIG. It is noted that deloading factor is not necessary to be 10% due to less reserve demand, which shows economical benefits with less generation loss.

ESS desire ouput power is responding to high-frequency deviation thus fast change is observed. Sizing of ESS can be simply decided as the maximum regulation power of ESS is satisfied. Note that this is the most conservative sizing criteria. The SoC feedback control is implemented with feedback gain α chosen as 0.5. The simulation result is shown in Fig. 12. P droop is the reference power of ESS. A time interval from 12h to 19h is investigated. SoC of ESS is maintained in the desired bound and only small excursion is observed. The ESS is not available from 16h30 to 17h as P ESS is nearly zero. This may introduce financial penalty in the sense of power market. However, the revenue of more generation with less deloading may compensate or even exceed this penalty. The system frequency is compared and shown in Fig. 13, the effectiveness is verified with small frequency deviation in SoC feedback control system.

  This work was supported by the State Grid Corporation of China under Grant 52094016000C.

Fig. 1 .

 1 Fig. 1. Wind Power Cp Curve

Fig. 3 .

 3 Fig. 3. Power system simulation model block diagram

Fig. 4 .

 4 Fig. 4. Pitch Angle Control for frequency regulation

  for SoC > 0.85 0.5 for 0.15 < SoC < 0.85 0.2 for SoC < 0.15[START_REF] Lalor | Frequency control and wind turbine technologies[END_REF]

Fig. 5 .

 5 Fig. 5. Coordinate control strategy of ESS and FFWPS

Fig. 6 .

 6 Fig. 6. Wind farm output power (p.u.)

Fig. 7 .

 7 Fig. 7. FFWPS output without ESS for frequency regulation

Fig. 9 .

 9 Fig. 9. FFWPS output with ESS for frequency regulation

Fig. 11 .

 11 Fig. 11. ESS output power reference for frequency regulation

Fig. 12 .

 12 Fig. 12. ESS output of SoC feedback control with α = 0.5

Fig. 13 .

 13 Fig. 13. System frequency