Stéphanie Matt
email: stephanie.matt@univ-lorraine.fr

Milena Dzhelyova
email: milena.dzhelyova@uc-louvain.be

Louis Maillard
email: l.maillard@chru-nancy.fr

Joëlle Lighezzolo- Alnot

Bruno Rossion
email: bruno.rossion@univ-lorraine.fr

Stéphanie Caharel
email: stephanie.caharel@univ-lorraine.fr

The Rapid and automatic categorization of facial expression changes in highly variable natural images

Keywords: Facial Expression, Natural context, Perception, FPVS, EEG

Emotional expressions are quickly and automatically read from human faces under natural viewing conditions. Yet, categorization of facial expressions is typically measured in experimental contexts with homogenous sets of face stimuli. Here we evaluated how the 6 basic facial emotions (Fear, Disgust, Happiness, Anger, Surprise or Sadness) can be rapidly and automatically categorized with faces varying in head orientation, lighting condition, identity, gender, age, ethnic origin and background context. High-density EEG was recorded in 17 participants viewing 50 sec sequences with natural variable images of neutral-expression faces alternating at a 6 Hz rate. Every five stimuli (1.2 Hz), variable natural images of one of the six basic expressions were presented. Despite the wide physical variability across images, a significant F/5 = 1.2 Hz response and its harmonics (e.g., 2F/5 = 2.4 Hz, etc.) was observed for all expression changes at the group-level and in every individual participant. Facial categorization responses were found mainly over occipito-temporal sites, with distinct hemispheric lateralization and cortical topographies according to the different expressions. Specifically, a stronger response was found to Sadness categorization, especially over the left hemisphere, as compared to Fear and Happiness, together with a right hemispheric dominance for categorization of Fearful faces. Importantly, these differences were specific to upright faces, ruling out the contribution of low-level visual cues. Overall, these observations point to robust rapid and automatic facial expression categorization processes in the human brain.

Introduction

The ability to quickly and accurately extract one's emotional state from their face is crucial for social interaction. Ekman (1993) defined six emotions as basic (Disgust, Fear, Happiness, Anger, Surprise and Sadness) which are described as discrete entities. In line with Darwin's evolutionary work (1872), basic emotions have been suggested to be universally recognized in the human species through specific patterns of facial movements called Action-Units (Ekman & Friesen, 1978; but see, [START_REF] Jack | Cultural confusions show that facial expressions are not universal[END_REF]. The perception of these emotional faces in humans has been studied extensively, for decades, by means of behavioral (Beaudry, Roy-Charland, Perron, Cormier & Trapp, 2014;[START_REF] Calder | Configural information in facial expression perception[END_REF][START_REF] Palermo | Photographs of facial expression : Accuracy, response times, and ratings of intensity[END_REF], electrophysiological [START_REF] Batty | Early processing of the six basic facial emotional expressions[END_REF][START_REF] Caharel | Familiarity and emotional expression influence an early stage of face processing : An electrophysiological study[END_REF][START_REF] Luo | Three stages of facial expression processing : ERP study with rapid serial visual presentation[END_REF] and neuroimaging [START_REF] Dricu | Perceiving emotional expressions in others : Activation likelihood estimation meta-analyses of explicit evaluation, passive perception and incidental perception of emotions[END_REF][START_REF] Müller-Bardorff | Early brain responses to affective faces : A simultaneous EEG-fMRI study[END_REF][START_REF] Wegrzyn | Investigating the brain basis of facial expression perception using multi-voxel pattern analysis[END_REF] approaches (see [START_REF] Calvo | Perceptual and affective mechanisms in facial expression recognition: An integrative review[END_REF][START_REF] Vuilleumier | Distributed and interactive brain mechanisms during emotion face perception: evidence from functional neuroimaging[END_REF] for reviews). In most of these studies, decontextualized (i.e., segmented) images of faces, usually presented at full-front-views, have been typically used in order to maximize stimulus control and clarity of facial expressions. However, in real life situations, emotional expressions have to be quickly and automatically recognized from faces viewed at different angles, lighting condition or distance (size). Moreover, facial expressions have to be decoded from individuals of different ethnicity, gender and age, in extremely variable natural environmental contexts. While there has been notable increase of studies using natural heterogenous images of faces to study (familiar) face identity recognition in recent years [START_REF] Burton | Why has research in face recognition progressed so slowly? The importance of variability[END_REF][START_REF] Young | Recognizing faces[END_REF] and influential studies on the role of (in)congruent contextual scenes on facial expressions (Righart & de Gelder, 2008a, 2008b;[START_REF] Xu | Facial Expressions in Context : Electrophysiological Correlates of the Emotional Congruency of Facial Expressions and Background Scenes[END_REF]; for reviews see [START_REF] Aviezer | The inherently contextualized nature of facial emotion perception[END_REF][START_REF] Wieser | Faces in context: a review and systematization of contextual influences on affective face processing[END_REF], it is fair to say that the robustness of human facial expression categorization in highly challenging conditions of variable stimulation conditions (i.e., heterogenous natural sets of stimuli) remains largely unknown.

A promising approach to address this question is Fast Visual Periodic Stimulation (FPVS) coupled with electroencephalography (EEG), a technique extended specifically in recent years to provide sensitive measures of (visual) face categorization (e.g., [START_REF] Rossion | Understanding face perception by means of human electrophysiology[END_REF][START_REF] Rossion | Mapping face categorization in the human ventral occipito-temporal cortex with direct neural intracranial recordings[END_REF]. FPVS, also called frequency-tagging, is based on the longstanding observation that the periodic presentation of a stimulus elicits a brain response exactly at the frequency at which the stimulus is presented, this response being objectively (i.e., at the known stimulation frequency) captured in the EEG frequency spectrum [START_REF] Adrian | The Berger rhythm: potential changes from the occipital lobes in Man[END_REF][START_REF] Regan | Some characteristics of average steady-state and transient responses evoked by modulated light[END_REF] for recent reviews, see [START_REF] Norcia | The steady-state visual evoked potential in vision research : A review[END_REF][START_REF] Rossion | Understanding human individuation of unfamiliar faces with oddball fast periodic visual stimulation and electroencephalography[END_REF][START_REF] Wieser | Steady-state visual evoked potentials as a research tool in social affective neuroscience[END_REF]. By means of this approach, Dzhelyova and colleagues (2017) measured the detection of brief changes of facial expression by presenting the same neutral face image at a rapid 6 Hz rate (i.e., 6 images/second), with expressive faces of the same identity appearing every 5 images (i.e., 1.2 Hz). Within a few minutes of visual stimulation, these changes in facial expressions (from neutral to expressive faces) elicited significant responses on occipito-temporal sites with specific spatial patterns of activity according to the emotional expression: the largest ventral occipito-temporal activity was found for Disgust and Fear, while Happiness elicited a more dorsal activation (Dhzelyova, Jacques & Rossion, 2017). In subsequent studies, these results have been replicated with different stimulus sets and extended to the expressions of Anger and Sadness [START_REF] Leleu | Tuning functions for automatic detection of brief changes of facial expression in the human brain[END_REF], also showing robust categorization of a specific expression among each of the other basic expressions [START_REF] Luo | Consistent behavioral and electrophysiological evidence for rapid perceptual discrimination among the six human basic facial expressions[END_REF] or among mixed facial expressions rather than a neutral face [START_REF] Poncet | Rapid and automatic discrimination between facial expressions in the human brain[END_REF]. Despite these impressive results, rapid automatic categorization of facial expressions across different identities has not yet been demonstrated with this approach. Most importantly, highly homogenous sets of visual stimuli, that is, full front faces of human adults presented out of context and with posed expressions, most often extracted from databases where the facial features are exaggerated (e.g., "Radboud Faces Database": [START_REF] Langner | Presentation and validation of the Radboud Faces Database[END_REF]; "Karolinska Directed Facial Expression Database": [START_REF] Lundqvist | The Karolinska Directed Emotional Faces-KDEF (CD ROM)[END_REF]"NimSTIM": Tottenham et al., 2009) have been used in the majority of behavioral and neuroimaging studies (but see, for studies using various environmental contexts: Bublatzki, Kavcıoğlu, Guerra, [START_REF] Bublatzky | Contextual information resolves uncertainty about ambiguous facial emotions: Behavioral and magnetoencephalographic correlates[END_REF]Ferrari, Papagno, Todorov & Cattaneo, 2019;[START_REF] Makeig | Contributions of facial expressions and body language to the rapid perception of dynamic emotions[END_REF][START_REF] Xiao | Can we distinguish emotions from faces? Investigation of implicit and explicit processes of peak facial expressions[END_REF].

Here we extend recent research with the FPVS-EEG approach on categorization of faces and of familiar face identities with natural (i.e., unsegmented) variable images [START_REF] Rossion | Fast periodic presentation of natural face images reveals a robust face-selective electrophysiological response in the human brain[END_REF][START_REF] Rossion | Mapping face categorization in the human ventral occipito-temporal cortex with direct neural intracranial recordings[END_REF]Zimmerman, Yan & Rossion, 2019, respectively) to provide a quantified electrophysiological measure of the detection of brief changes of facial expressions with such natural heterogenous stimulus sets. To do that, we selected natural emotional faces of the six basic emotions embedded in a naturalistic context: faces of both children and adult, varying in head orientation, identity, sex, ethnical origin. Moreover, faces were presented in their original background, which varied within each emotional category as well as between each of the categories of facial expression (Fig. 1). Despite the high heterogeneity of the stimuli and the challenging condition of stimulation (i.e., only one glance at a face), we expected robust automatic categorization responses for all facial expressions tested. Moreover, considering previous neuroimaging (Harry, Williams, Davis & Kim, 2013;[START_REF] Wegrzyn | Investigating the brain basis of facial expression perception using multi-voxel pattern analysis[END_REF][START_REF] Zhao | Neural Responses to Rapid Facial Expressions of Fear and Surprise[END_REF] and electrophysiological results [START_REF] Brenner | The role of encoding and attention in facial emotion memory : An EEG investigation[END_REF][START_REF] Campagnoli | How the visual brain detects emotional changes in facial expressions: Evidence from driven and intrinsic brain oscillations[END_REF]Dhzelyova et al., 2017;[START_REF] Turano | Fear boosts the early neural coding of faces[END_REF], these categorization responses were expected primarily over occipito-temporal regions, with higher neural responses for negative than positive expressions [START_REF] Batty | Early processing of the six basic facial emotional expressions[END_REF][START_REF] Caharel | Familiarity and emotional expression influence an early stage of face processing : An electrophysiological study[END_REF][START_REF] Vytal | Neuroimaging Support for Discrete Neural Correlates of Basic Emotions : A Voxel-based Meta-analysis[END_REF][START_REF] Whalen | Neuroscience and Facial Expressions of Emotion : The Role of Amygdala-Prefrontal Interactions[END_REF]. Larger categorization responses were also expected over the right hemisphere, in line with the dominance of this hemisphere in facial expression categorization and face recognition in general [START_REF] Calvo | Brain lateralization of holistic versus analytic processing of emotional facial expressions[END_REF][START_REF] Hillger | Separable mechanisms in face processing : Evidence from hemispheric specialization[END_REF][START_REF] Jacques | The initial representation of individual faces in the right occipito-temporal cortex is holistic : Electrophysiological evidence from the composite face illusion[END_REF][START_REF] Rossion | Holistic Face Categorization in Higher Order Visual Areas of the Normal and Prosopagnosic Brain : Toward a Non-Hierarchical View of Face Perception[END_REF][START_REF] Sergent | Face perception and the right hemisphere[END_REF][START_REF] Sergent | FUNCTIONAL NEUROANATOMY OF FACE AND OBJECT PROCESSINGA POSITRON EMISSION TOMOGRAPHY STUDY[END_REF], and as previously observed in FPVS-EEG studies (Dhzelyova et al., 2017;[START_REF] Leleu | Tuning functions for automatic detection of brief changes of facial expression in the human brain[END_REF]. Finally, the specific contribution of high-level categorization processes, i.e., going beyond physical image cues, was isolated by comparing these responses to the exact same images presented upside-down, expecting a substantial decrease of response in this stimulation condition.

Materials and Methods

We report how we determined our sample size, all data exclusions, all inclusion/exclusion criteria, whether inclusion/exclusion criteria were established prior to data analysis, all manipulations, and all measures in the study.

2.1.

Participants Seventeen participants (7 males, mean age = 23.16, SD = 2.24, range = 19.25-27.5; all right-handed) provided signed and informed consent and were paid for their participation in the experiment, which was approved by the ethical committee of the University of Louvain. The sample size was established according to previous FPVS-EEG studies (e.g., Dzhelyova et al., 2017;[START_REF] Leleu | Tuning functions for automatic detection of brief changes of facial expression in the human brain[END_REF]Poncet et al., 2018), considering that this approach is characterized by a very high signal-to-noise ratio (SNR) [START_REF] Norcia | The steady-state visual evoked potential in vision research : A review[END_REF][START_REF] Regan | Human Brain Electrophysiology: Evoked Potentials and Evoked Magnetic Fields in Science and Medicine[END_REF]. This high SNR is partly due to the large number of categorization responses measured (i.e., here 60 changes of facial expressions in a stimulation sequence, resulting in 240 trials for the 4 repetitions of each condition, as described below). All participants reported normal or corrected-tonormal vision. None of the participants reported any history of psychiatric or neurological disorders.

2.2.

Stimuli One hundred images of natural facial expressions were selected on the basis of a pilot experiment carried out on a large set of images collected on the internet (n= 303). The faces in the selected images for the experiment varied in head orientation, lighting direction, identity, age, sex, ethnical origin, and surrounding background. These stimuli were rated by an independent group of participants (n = 23,mean age = 21.23,SD = .19,. In that pilot study, participants were asked to categorize the displayed expression in a seven-alternative forced-choice task (Disgust, Fear, Happiness, Anger, Sadness, Surprise, Neutral and undefined) and evaluate its intensity on a scale ranging from 1 (low intensity) to 9 (very high intensity). In order to select the stimuli, a cut-off of 70% for each image was used as the lower recognition rate. Based on this criterion but also on the matching of the stimuli according to their intensity rating, forty face images corresponding to a neutral expression and ten images for each basic emotion (Disgust, Fear, Happiness, Surprise, Anger and Sadness) were selected (see Table 1). For these remaining images, the hit rate was very high (mean hit rates = 91.83 %, SD = 6.00). Repeatedmeasures analysis of variances (ANOVA) with Emotion (Disgust, Fear, Happiness, Anger, Surprise, Sadness and Neutral) as within-subject factor were conducted both for hit rates and intensity ratings on this image set. A significant effect of emotion was observed on intensity ratings (F(6, 132) = 4.21, ε = .42, p = .01, ηp² = .16). Post-hoc analyses with Bonferroni correction for multiple comparisons showed that the neutral expression was less intense than Fear, Happiness, Sadness and Surprise (respectively p = .0017, p = .003; p = .0057 and p = .003). The main effect of emotion on hit rates was also significant (F(6, 132) = 6.96, ε = .54, p = .0003, ηp² = .24), due to Fear being less well recognized than Anger (p = .03), Happiness (p < .0001), Sadness (p = .003) and Surprise (p = .03). Also, neutral faces were less well recognized than happy (p = < .0001) and sad (p = .006) faces. This observation is consistent with previous studies with homogenous image sets showing that Fear is less well recognized and often confused with Surprise (e.g., [START_REF] Tracy | The automaticity of emotion recognition[END_REF].

Fear

Procedure 2.3.1. EEG testing

The procedure was similar to a previous study (Dzhelyova et al., 2017) using fast periodic visual stimulation (FPVS) to identify a neural signature to facial expression categorization. After electrode cap placement, participants were seated in a dimly lit room, at a viewing distance of 80 cm from a computer monitor (LED screen (BenQXL2420T) with a 1920 x 1080 pixels resolution and a 120 Hz refresh rate). During stimulation, stimuli appeared in the center of a uniform light gray background and displayed with 383 pixels x 383 pixels resolution and a size of 7.249 x 7.249 degrees of visual angle at this distance. During the experiment, a fixation cross was displayed in the center of the images and the participants were instructed to press the "space bar" when they noticed a brief and random color change of the fixation cross from blue to red (lasting 200 msec, 12 times during a sequence). This orthogonal task ensured that participants paid attention to the screen during the sequence.

Each sequence started with a variable duration of 2-5 sec of a blank screen, 2 sec of gradually fading in of the stimuli presentation, followed by 50 sec of stimulation sequence and 2 sec of gradually fading out of the stimuli. Images were presented at a base rate stimulation of 5.9991 Hz (i.e., approximately 6 images per second, image presentation duration ≈ 167 msec) with the constraint that every fifth images an emotional expression was presented (expression-specific rate = 6/5 = 1.2 Hz), resulting in a sequence Neutral Neutral Neutral Neutral Expressive Neutral Neutral Neutral Neutral Expressive (see Fig. 1). For each sequence, different individuals with a neutral expression were randomly selected as the base face (N) and different individuals with a given emotional expression (E) were randomly inserted at the expression-specific rate. Images were presented through a sinusoidal contrast modulation (0-100%). Each pixel of the images reached the full luminance value of the face stimulus after half a cycle, approximately 83 msec after the stimulus onset ((1000/6)/2). A practical advantage of the sine mode of stimulation is that it is a smoother stimulation than the square wave stimulation and it is defined with a single parameter (stimulus onset asynchrony (SOA) or rate). Another advantage is that the visual stimulation is present almost all the time, thus giving a continuously changing percept.

In order to respect equiprobability presentation of the pictures, the entire set of images was repeated 6 times within every sequence (6 Hz x 50 sec = 300 pictures presented during each sequence, including 60 expressive faces (= 300/5) and 240 neutral faces). In order to control for low-level effects, each sequence, assessing the discrimination of one specific emotional expression compared to neutral faces, was presented both at upright and inverted orientations. The presence of an inverted face condition was motivated by the fact that, while images-based cues are preserved by inversion, high-level processing of faces, including facial expression processing, is impaired by this manipulation of orientation (e.g.; [START_REF] Calvo | Detection of emotional faces : Salient physical features guide effective visual search[END_REF]Farah, Tanaka & Drain, 1995;[START_REF] Van Belle | Face inversion impairs holistic perception : Evidence from gaze-contingent stimulation[END_REF]. In total, twelve conditions were implemented: 6 emotional expressions (Disgust, Fear, Happiness, Surprise, Anger and Sadness) and 2 orientations (upright or inverted). Each condition was repeated 4 times, resulting in 48 sequences. 8 blocks of 6 sequences were created corresponding to each emotional expression (one orientation per block). Blocks' and sequences' order were randomized for every participant.

By presenting faces at 6 Hz, we expected an EEG response at 6 Hz and at harmonics (i.e., 12 Hz, 18 Hz etc.) reflecting the general visual responses to the images. In order to quantify the discrimination response to emotional expressions, only frequencies corresponding to the presentation rate of the expressive faces were considered, namely the expression-specific frequency (F/5 = 1.1998 Hz, rounded to 1.2 Hz) and its harmonics (i.e. 2F/5 = 2.3996 Hz, 3F/5 = 3.5994 Hz, 4F/5 = 4.7992 Hz, rounded to 2.4 Hz, 3.6 Hz and 4.8 Hz respectively).

EEG acquisition

Scalp electroencephalographic (EEG) activity was recorded using a 128channels BIOSEMI Active-Two (the common mode sense active electrode was used as reference and the driven right leg passive electrode was used as ground) system (BioSemi, Amsterdam, Netherlands), including both standard 10-20 system locations and additional intermediate positions. Electrode offset was reduced 30 µV for each individual electrode. Eye-movements were monitored with 4 external electrodes, included 2 electrodes for vertical eye movements placed above and below the right eye and 2 electrodes for horizontal eye movements placed at the corner of each eye. Continuous EEG was sampled at 512 Hz.

EEG Analysis

All EEG processing steps were carried out using Letswave 5 (http://nocions.webnode.com/letswave), and Matlab R2014a (The Mathworks).

Preprocessing

The continuously recorded data were first filtered using a bandpass filter at 0.1-120 Hz (Butterworth filter, fourth order) and then resampled to 256 Hz to save disk space and reduce computational load. The data were then cropped into 56 sec segments (4 sec before and 2 sec after each stimulation sequence corresponding to the 2 sec fading in, 2 sec fading out and 2 extra sec). For 5 participants who blinked on average above 0.1 times/sec (average number of blinks across participants = 0.1, SD = 0.07), blinks were corrected by means of independent component analysis (ICA) using the runica algorithm [START_REF] Bell | An information-maximization approach to blind separation and blind deconvolution[END_REF][START_REF] Makeig | Contributions of facial expressions and body language to the rapid perception of dynamic emotions[END_REF], as implemented in EEGLAB. This algorithm outputs a square mixing matrix in which the number of components corresponds to the number of channels. For each of these 5 participants, only the first component, accounting for most of the variance, representing vertical eye movements was removed. Next, noisy or artifact ridden channels (not more than 5% of the electrodes) were re-estimated using linear interpolation of the 3 nearest spatially neighboring electrodes, (on average,1.8 electrodes were interpolated). All data segments were re-referenced to a common average reference.

Frequency-domain analysis

Preprocessed data segments were cropped down to an integer number of 1.2 Hz cycles beginning immediately after the fade in, until approximately 49.18 sec (≈ 50 sec, 59 cycles, 12589 time bins in total) in order to separate each condition. Data were then averaged in the time domain, separately for each condition: Orientation (Upright, Inverted) and Facial Expression (Disgust, Fear, Happiness, Surprise, Anger, Sadness) and for each participant. A Fast Fourier Transformation (FFT) was then applied to these averaged segments and amplitude spectra were extracted for all channels. The FFT transformation yielded a spectrum ranging from 0 to 256 Hz with a high spectral resolution of about 1/49 s, i.e. 0.02 Hz. Z-scores were calculated on the FFT grand-averaged data pooled across all electrodes for each condition in order to assess the significance of the responses at different harmonics. Z-scores were computed as in a previous study, using the mean and the standard deviation of the 20 frequency bins surrounding the frequency of interest (excluding the immediately neighboring bin and the 2 most extreme value, e.g. Liu-Shuang, [START_REF] Liu-Shuang | An objective index of individual face discrimination in the right occipito-temporal cortex by means of fast periodic oddball stimulation[END_REF], resulting in 10 bins in each side. For the following analyses, we considered for the expression-specific response all consecutive harmonics that were significant with a Z-score above 2.32 (p < .01, one tailed, i.e. signal > noise) in at least one condition since one might expect differences in the strength of the discrimination responses depending on the facial expression. For the base rate response, reflecting the general visual processing of the faces, as we expected no differences for the different expression discriminations, harmonics were considered as significant until the Zscore was no longer above 2.32 (p < .01) for all the conditions. Based on this criterion, the expression-specific response was quantified as the sum of the expression-specific response harmonics (Retter, Rossion & Schiltz, in press) until the 8 th harmonic (i.e. 8F/5 = 9.598 Hz, rounded 9.6 Hz, excluding the 5 th harmonic corresponding to the base frequency rate F = 6 Hz). The response at the base frequency rate was quantified as the sum of the base rate until the 5 th harmonic (i.e. 5F = 30 Hz) (see Figs. 2A and2B).

SNR and baseline-corrected amplitude were used to describe the response at the frequency of interest. SNR was computed as the amplitude value at the bin of interest divided by the average amplitude of the 20 surrounding frequency bins (excluding the 2 most extreme bins and the immediately neighboring bin, resulting in 10 bins in each side, e.g. [START_REF] Liu-Shuang | An objective index of individual face discrimination in the right occipito-temporal cortex by means of fast periodic oddball stimulation[END_REF]. SNR spectra were used for data visualization, since the harmonic responses at high frequency ranges may be of small amplitude but with a high SNR. In order to combine expression-specific response harmonics (i.e. f/5 = 1.2 Hz; 2f/5 = 2.4 Hz etc.), we first applied a baselinecorrection to the amplitude by subtracting the average noise level of the surrounding 20 bins and then summed the baseline-corrected amplitudes 8 expression-specific frequency harmonics (see Figs. 2C and2D).

For the group analysis, grand-averages of the SNR and baseline-corrected amplitudes were computed across participants for each condition and electrode separately. In order to better visualize hemispheric differences for each emotion, we cropped the FFT spectrum in different epochs, centered at the expression-specific response (1.2 Hz and the subsequent harmonics until 9.6 Hz), surrounded by 10 neighboring bins, representing the noise level. Then, we summed the epochs corresponding to the expression-specific frequencies excluding 5 th epoch corresponding to the base rate (6 Hz). Similarly, for the general visual response, we cropped the FFT spectrum in different epochs, centered this time at 6 Hz and the 5 harmonics, surrounded by 10 neighboring bins, and summed these epochs. We applied a baseline-correction by subtracting the average noise level of the surrounding 10 bins excluding the immediately adjacent and the maximal ones. To visualize data at the group level, we average each condition and electrode separately. Then, we averaged for each expression changes the electrodes corresponding to the ROI defined below.

For the statistical analysis, based on previous research (Dzhelyova et al., 2017;[START_REF] Leleu | Tuning functions for automatic detection of brief changes of facial expression in the human brain[END_REF] and visual inspection of topographical maps, we determined regions of interest (ROIs). Each ROI corresponded to the pooling of the four channels with the strongest activation. Thereby, we defined, for general visual response one medial ROI comprising the medial occipital (mO) sites (mO: Oz, OIz, Iz and O2) and for expression-specific response 2 ROI, the left and right posterior-occipital (lOT; rOT) sites (P9, PO7, PO9, PO11 and P10, PO8, PO10, PO12 respectively).

Repeated-measures analysis of variances (ANOVA) were conducted on the baseline-corrected amplitudes with Expression (Disgust, Fear, Happiness, Surprise, Anger, Sadness), Orientation (Upright, Inverted) and ROI as within factors.

Greenhouse-Geisser correction were applied to adjust degrees of freedom wherever the assumption of sphericity was violated (corresponding to the epsilon (ε) values). For significant effects or interactions, post-hoc comparisons were performed using Bonferroni adjustment. Effect size was estimated with partial η2. The alpha significance value used was 0.05.

Individual Analysis

The response to facial expression was also evaluated at the individual level for upright faces. In order to visualize the overall response for each individual participant, we cropped the FFT spectrum in different epochs, centered at the expression-specific response (1.2 Hz and the subsequent harmonics until 9.6 Hz), surrounded by 10 neighboring bins, representing the noise level. Then, we summed the epochs corresponding to the expression-specific frequencies excluding the 5 th epoch corresponding to the base rate (6 Hz). For each participant and condition, we computed an average of responses in each of the ROI defined at the group level (PO7, PO9, PO11, P9 and PO8, PO10, PO12, P10 for the left and right hemisphere respectively). Then, we calculated Z-scores on the FFT spectrum (see group level analysis for the method) in order to assess the significance of the responses in both ROIs for each expression at the individual level. We also applied a baselinecorrection by subtracting the average noise level of the surrounding 10 bins excluding the immediately adjacent and the maximal ones. Interhemispheric differences for each expression were estimated by pairwise t-tests to compare left and right ROI amplitude per participants.

Brain topographical analysis

Previous studies (Dzhelyova et al., 2017;[START_REF] Leleu | Tuning functions for automatic detection of brief changes of facial expression in the human brain[END_REF][START_REF] Poncet | Rapid and automatic discrimination between facial expressions in the human brain[END_REF] showed distinct brain responses according to the facial expression in the occipito-temporal regions. However, to examine potential topographical differences, individual baseline-corrected FFT spectrum data for each facial expression in upright and inverted orientation were normalized. For each participant, the baselinecorrected spectrum for each condition was first scaled by dividing the value at each electrode by the scalp-wide root-mean-square value (i.e. the square root of the sum of squares for all the 128 electrodes) [START_REF] Mccarthy | Scalp distributions of event-related potentials : An ambiguity associated with analysis of variance models[END_REF]. These normalized amplitudes were then summed for the significant harmonics. Normalized amplitudes were used for this analysis to ensure that topographical differences are not driven by amplitude differences between conditions. A mass univariate test was then used to estimate the effects of Expression (Disgust, Fear, Happiness, Surprise, Anger and Sadness) for each orientation on the 128 electrode sites. To this end, 10 000 random permutations were computed and for each permutation, an ANOVA test was conducted. Then, for each permutation, we stored the absolute maximum F-value in order to create the empirical F-distribution for our experiment. The 0.05 tails of this distribution were later used for determining statistical significance. The described method has been shown to provide a highly conservative threshold controlling for multiple comparison (Groppe, Urbach & Kutas, 2011). Significant differences between experimental conditions at one of the electrode sites were followed by a Bonferroni post hoc analysis.

Time-domain analysis

In order to visualize the shape of the periodic changes time-locked to the emotional expression stimulus and estimate the speed and time course of facial expression changes, we performed time-domain analysis. Preprocessed and rereferenced data were low-pass FFT filtered at 30 Hz. A FFT notch filter with 0.05 width selectively removed the dominating base frequency, 6 Hz and its harmonics (i.e., 12Hz, etc.). The first and last 2 sec of the sequence corresponding to the fade in and fade out were excluded. Overlapping, locked to the expression-specific presentation epochs were segmented, lasting for an overall duration of 2000 msec (12 cycles of the base rate). For each sequence, 57 epochs were available per repetition for each condition (57 expression-specific images x 833 msec per cycle = 47 510 msec). These epochs were averaged for each participant and baselinecorrected by subtracting the signal within the 167msec pre-stimulus presentation, corresponding roughly to 1 cycle of the base rate. A t-test comparing when the waveforms were significantly different from 0 (p < .05, for at least 5 consecutive points, approximately 20 msec) was performed.

Results

Behavioral Data of the Change of Fixation Color Task

Repeated-measures analysis of variances (ANOVA) with Orientation (Upright, Inverted) and Facial Expression (Anger, Disgust, Fear, Happiness, Sadness, Surprise) as within-subject factors indicated no differences in task performance between upright and inverted faces (accuracy: F(1, 16) = 1.04, ε = 1.00, p = .32; Response Times (RTs): F(1, 16) = 2.65, ε = 1.00, p = .12) as well as between the different facial expressions (accuracy: F(5, 80) = 1.01, ε = .72, p = .41; RTs: F(5, 80) = .76, ε = .43, p = .48). The interaction between Orientation and Facial Expression also did not reach significance (accuracy: F(5, 80) = .98, ε = .70, p = .42, RTs: F(5, 80) = .78, ε = .39, p = .46).

EEG Data: Frequency Analysis

Base Frequency: General Visual Stimulation

Although it was not the main purpose of this study, we analyzed the responses obtained at the base rate frequency, reflecting the general response of the visual system of the stimulation. There were robust responses at this frequency, with larger responses over medial occipital sites, extending slightly towards the right lateral occipital sites (see Figs. 2A and2B). In order to evaluate the effects of Orientation and Expression at the base rate frequency in these regions, we performed repeated measures ANOVAs on the summed baseline corrected amplitudes over the medial occipital region (mO: Oz, OIz, Iz and O2) (sum of the 5 base rate harmonics (i.e., until 29.99 Hz over these sites). A main effect of Facial Expression was revealed, F(5, 80) = 4.08, ε = .696, p = .008, ηp² = .20, due to a lower response for Sadness compared to Happiness and Anger, irrespectively of orientation (p = .005 and p = .004, respectively). However, neither the effect of Orientation (F(1,16) = .008, ε = 1.00, p = .931), nor the interaction between Orientation and Expression (F(5,80) = .56, ε = .74, p = .68), were significant.

Expression-specific Frequency: Discrimination of Emotional Expression

Despite the heterogeneity and the brevity of the image presentation, responses corresponding to the frequency of expression change (from a neutral face to an expressive face) were clearly observable at 1.2. Hz and its harmonics (see Figs. 2C andD). Scalp topographies of the summed responses of the first 8 harmonics (i.e., f/5; 2f/5 etc., until 9.6 Hz excluding the base rate) (see Fig. 3) showed that the expression-specific responses were larger on the occipito-temporal regions. In addition, interhemispheric differences in these regions emerged according to the different types of expression change.

While the change from a neutral to a fearful or happiness expression seemed to engage mainly the right hemisphere, a change to a sad expression seemed to further engage the left hemisphere (see Fig. 3). The other changes, especially changes to Disgust, Surprise and Anger, seemed to be bilaterally distributed. Interestingly, among all the expressions used, the largest response was observed for faces expressing Sadness. Moreover, these occipito-temporal activities were reported only for upright faces, thus confirming that face inversion strongly disrupts the processing of emotional expressions (see Fig. 3).

Response over the whole scalp

To test these observations without predefined ROIs, a repeated-measures ANOVA was first carried out on the grand-averaged summed baseline-corrected amplitudes across all the 128 channels response with Orientation and Facial Expression as within-subject factors. This analysis revealed a main effect of both Facial Expression, F(2, 80) = 3.62, ε = .62, p = .018, ɳp 2 = .18 and Orientation, F(1, 16) = 56.77, ε = 1.00, p < .0001, ɳp 2 = .78. The interaction between Orientation and Facial Expression was also significant, F(5, 80) = 3.62, ε = .795, p = . 01, ɳp 2 = .18, and was explained by a significant effect of Facial Expression only for upright orientations (Upright: F(5,80) = 4.16, ε = .64, p = .009, ɳp 2 = .002; Inverted: F(5,80) = 2.24, ε = .81, p = .07). This last effect was due to higher amplitudes for changes to Anger than changes to Fear and to Happiness (p = .018 and p = .01, respectively).

ROI Analysis

Since the discrimination response to emotional expression was distributed mainly over the occipito-temporal sites, two main regions of interest in the right and the left hemisphere were defined for further analysis. Each ROI comprised 4 electrodes, P9, PO7, PO9 and PO11 for the left hemisphere and the corresponding electrodes in the right hemisphere (see Fig. 3). Thus, as above, a repeatedmeasures ANOVA was carried out on the amplitudes by adding the ROI, left and right occipito-temporal sites (lOT; rOT), as a factor. This analysis revealed that the expression-specific response was larger for upright than for inverted orientations, F(1, 16) = 90.31, ε = 1.00, p < .0001, ɳp 2 = .85. The amplitudes were also significantly different among the 6 expressions, regardless of their orientation, F (5, 80) = 4.6, ε = .52, p = .0095, ɳp 2 = .22. However, the main effect of ROI did not reach significance, F(1,16) = 0.10, ε = 1.00, p = . 76. More interestingly, the two-way interactions with Facial Expression reached significance (Expression x ROI: F(5,80) = 17.09, ε = .796, p < .0001, ɳp 2 = .52, and Expression x Orientation: F(5, 80) = 2.94, ε = .66, p = .037, ɳp 2 = 16) as well as the 3-way interaction between Expression, Orientation and ROI, F(5,80) = 4.96, ε = .53, p = .006, ɳp 2 = .24. This last interaction was explained by the same level of activity in left and right hemisphere (p > .05) for inverted faces. However, for upright faces, higher responses were measured in the right hemisphere for Fear expressions (p < .0001) and in left hemisphere for sad (p = .002) and Surprised (p = .02) faces. Interhemispheric differences for the other expressions did not reach significance (ps > .05) (see Fig. 3). Additionally, regardless of the ROI, the main effect of Expression was significant only for upright orientation (Upright: F(5,80) = 4.63, ε = .56, p = .008, ɳp 2 = .22; Inverted: F(5,80) = 1.86, ε = .72, p = .13). Indeed, Sadness elicited a stronger activity than Fear and Happiness (p = .01 and p = .001 respectively with a Bonferroni correction for multiple comparisons).

Responses to facial expression changes at the individual level

All individual participants showed significant responses for each of the 6 expression changes presented in upright faces, either in the left, the right or both left and right ROI (Z-score > 1.96, i.e., p <.05) (See Fig. 4). Moreover, t-tests were performed at individual level on the baseline-corrected amplitude data to examine the interhemispheric differences for each expression (see Fig. 4). In a large number of participants, larger responses were observed in right than in left hemisphere, especially for Disgust (4 participants against 2, and 11 had activation in both hemisphere), Fear (7 against 2, and 8 with no differences), Happiness (5 against 2, and 10 with no differences) and Anger (7 against 1, 9 with no differences). In return, larger responses were observed in the left hemisphere for Surprise (in 5 participants against 4 in the right hemisphere, 8 with no differences) and Sadness (6 against 2, 9 with no differences).

Topographical analysis

In order to evaluate the spatial differences characteristic to the expression changes of the six basic emotions on the 128 electrode sites, mass univariate analyses were performed. While no effect was found on any of the electrode sites in the inverted orientation (all p > .05), a significant effect of Expression (p < .01) was observed on 7 occipito-temporal electrodes in the upright orientation, namely 4 electrodes in the left hemisphere (PO9, PO11, I1, POI1) and 3 in the medial region (POOz, Oz, OIz). Over the left occipito-temporal electrodes (PO9, PO11, I1, POI1), higher activities were recorded for Disgust and Sadness compared to Fear (all p < .027), indicating a stronger left lateralization for the former expressions than for Fear, and reinforcing the left lateralization for Sadness expression as revealed by the conventional analysis. However, on the electrodes located on medio-posterior region (POOz, Oz, OIz), Happiness and Anger elicited higher responses compared to all other expressions (all p < .0268), suggesting a more medial distribution for these expressions.

Time-domain analyses

These analyses, by subtracting Upright minus Inverted faces, revealed the succession of two clear deflections in occipito-temporal (PO9/PO10) sites following the presentation of an emotional expression (see Fig. 5): a negativity peaking at around 235 msec and lastly, followed by a positivity at around 397 msec (note that the sinusoidal presentation leads to a slight delay before the image is visible on the screen). Time-point analyses (p < .05, for at least 5 consecutive points, approximately 20 msec) on differential waveforms for each expression change showed significances during the time window from 189 msec to 346 msec over bilateral occipito-temporal sites for all the expressions, except for Surprise over the right occipito-temporal sites and Happiness in both hemispheres. Finally, significant differences appeared between 349 and 431 msec over bilateral occipito-temporal sites for all the expressions, except for Surprise and Disgust for which the responses appeared only on the left hemisphere and Sadness for which responses did not appear either in left or in the right hemisphere (see Table 2).

PO9

First

Discussion

With FPVS-EEG, we provide evidence of the human brain's ability to reliably detect changes from neutral faces to briefly presented facial emotions with highly heterogeneous natural images, i.e. varying in terms of background, head orientation, identity, age, lighting condition, ethnical origin, etc. Whole scalp analysis showed activities mainly over posterior regions. In particular, emotional face detection elicited activity over occipito-temporal sites, with distinct hemispheric lateralization and cortical topographies according to the specific expression. Analysis centered on posterior ROI highlighted higher activity for the expression of Sadness as compared to Fear and Happiness. Taking into account interhemispheric differences, Sadness and Surprise elicited larger responses over the left hemisphere whereas Fear elicited a larger response over the right hemisphere. The other emotions (Disgust, Happiness, Anger) activated both hemispheres to the same extent. Finally, brain topographical analysis underlined stronger responses over medio-posterior regions for Happiness and Anger, while Disgust and Sadness recruited the left lateral occipito-temporal regions to a larger extent than Fear.

4.1.

Robust electrophysiological marker of emotional face categorization The present study highlighted the capacity of the brain to automatically categorize facial expressions from visual stimuli embedded in a natural environment. Faces were presented rapidly (with a 166 msec SOA) in a continuous train of stimulation and despite the brief appearance of expressive faces every five faces (i.e. at a 1.2 Hz rate), highly significant neural responses were observed at that frequency of interest in the EEG spectrum. Moreover, despite the great variability of the images, these responses were significant not only at the group level but also at the individual level, thus underlining the robustness of the responses observed with this approach.

Several sources of evidence suggest that the recorded neural response to expression changes reflect high-level processes. Indeed, even if picture size did not vary across images (unlike in the previous FPVS-EEG studies measuring discrimination of facial expression, e.g., Dzhelyova et al., 2017;[START_REF] Leleu | Tuning functions for automatic detection of brief changes of facial expression in the human brain[END_REF], size and spatial location of the faces in the images were highly variable across stimuli, so that the changes of expression could not be captured by simple local (i.e. pixelwise) changes of the stimuli. Most importantly, all stimuli varied considerably in terms of their background, head orientation, sex, identity, age, etc. so that the differential response occurring at 1.2 Hz (and expressed as a sum of harmonics of that frequency) could not be attributed to the same systematic differences with the neutral faces. Hence, the expression-specific response (i.e., 1.2 Hz) was massively and significantly reduced for inverted faces, showing that this response largely reflects high-level processes. Moreover, the probability of appearance of each image (neutral or expressive faces) in each stimulation sequence was strictly equal, and the fast rate of stimulation together with the high variability of images in such paradigms prevent participants to temporally track the appearance of expression changes (i.e., the "oddball" response is not due to temporal predictability, see [START_REF] Quek | Category-selective human brain processes elicited in fast periodic visual stimulation streams are immune to temporal predictability[END_REF]). Yet, one acknowledged limitation of our study is the small number of pictures used for each emotional expression, both in order to equalize the repetition of each image and because of to the difficulty of finding clear natural images of expressive faces.

Overall, all these elements indicate that the neural responses to the detection of expression changes are related to high-level processes linked to emotion processing rather than to low-level processes linked to specific visual characteristics of the images.

4.2.

Human brain detection of facial expression according to emotion When all basic emotional expressions are evaluated, Fear generally elicits the largest neural response [START_REF] Batty | Early processing of the six basic facial emotional expressions[END_REF][START_REF] Poncet | Rapid and automatic discrimination between facial expressions in the human brain[END_REF][START_REF] Turano | Fear boosts the early neural coding of faces[END_REF]but see Lynn & Salisbury, 2008), whereas the smaller response is reported for Sadness [START_REF] Leleu | Tuning functions for automatic detection of brief changes of facial expression in the human brain[END_REF]. This is in line with behavioral evidence that Sadness is often confused with neutral expressions [START_REF] Palermo | Photographs of facial expression : Accuracy, response times, and ratings of intensity[END_REF][START_REF] Tottenham | The NimStim set of facial expressions : Judgments from untrained research participants[END_REF]. Moreover, previous frequency-tagging EEG research has revealed that the human brain's activity increases with increasing intensity of the different type of emotion, with a 40 % threshold for all the expressions, except for sadness for which the detection intensity threshold was 60 % [START_REF] Leleu | Tuning functions for automatic detection of brief changes of facial expression in the human brain[END_REF] when prototypical expressions are presented. Here, in contrast to these observations, the largest activity over occipito-temporal sites was observed to Sadness, significantly stronger than for Fear and Happiness. This discrepancy with previous studies could be explained by the high variability of the images used here, contrasting with the majority of studies on emotional perception that used stereotypical and posed faces. For instance, the presence of tears and its important social meaning could amplify the differences of neural response between neutral and sad faces and, in our attempt to match the intensity of all expressions, we could have potentially selected more triggering sad expressions compared to prototypical images. More generally, previous behavioral and neuroimaging studies have shown that gaze direction (directed or averted; e.g. [START_REF] Adams | Amygdala responses to averted vs direct gaze Fear vary as a function of presentation speed[END_REF][START_REF] Conty | When eye creates the contact ! ERP evidence for early dissociation between direct and averted gaze motion processing[END_REF][START_REF] Milders | Detection of emotional faces is modulated by the direction of eye gaze[END_REF][START_REF] Sander | Interaction effects of perceived gaze direction and dynamic facial expression : Evidence for appraisal theories of emotion[END_REF], head orientation (e.g. [START_REF] Hess | Looking at You or Looking Elsewhere : The Influence of Head Orientation on the Signal Value of Emotional Facial Expressions[END_REF][START_REF] Sauer | Effects of gaze direction, head orientation and valence of facial expression on amygdala activity[END_REF], ethnical origin (e.g. [START_REF] Beaupré | An ingroup advantage for confidence in emotion recognition judgments : The moderating effect of familiarity with the expressions of outgroup members[END_REF][START_REF] Jack | Cultural confusions show that facial expressions are not universal[END_REF][START_REF] Krämer | Neural correlates of emotion perception depending on culture and gaze direction[END_REF]see Nelson & Russel, 2013 for a review) but also emotional scenes in background (e.g. Righart & de Gelder, 2008a, 2008b;[START_REF] Xu | Facial Expressions in Context : Electrophysiological Correlates of the Emotional Congruency of Facial Expressions and Background Scenes[END_REF] could influence the recognition of emotional expressions and underlying neural activities. More specifically, regarding contextual influence, when emotional faces are more subtle, the context would facilitate their recognition (Bublatzki et al., 2020;[START_REF] Xiao | Can we distinguish emotions from faces? Investigation of implicit and explicit processes of peak facial expressions[END_REF].

We also observed a different lateralization of brain responses depending on facial expressions, with stronger activity over left than right occipito-temporal sites for Sadness and Surprise and the opposite for Fear. Hemispheric lateralization differences have often been explained as a result of different processing strategies, with the right hemisphere being more dedicated in holistic processing of the face [START_REF] Caharel | Early holistic face-like processing of Arcimboldo paintings in the right occipitotemporal cortex : Evidence from the N170 ERP component[END_REF][START_REF] Hillger | Separable mechanisms in face processing : Evidence from hemispheric specialization[END_REF][START_REF] Jacques | The initial representation of individual faces in the right occipito-temporal cortex is holistic : Electrophysiological evidence from the composite face illusion[END_REF][START_REF] Rossion | Holistic Face Categorization in Higher Order Visual Areas of the Normal and Prosopagnosic Brain : Toward a Non-Hierarchical View of Face Perception[END_REF][START_REF] Sergent | Face perception and the right hemisphere[END_REF][START_REF] Sergent | FUNCTIONAL NEUROANATOMY OF FACE AND OBJECT PROCESSINGA POSITRON EMISSION TOMOGRAPHY STUDY[END_REF] while the left hemisphere would be relatively more involved in analytic processing [START_REF] Hillger | Separable mechanisms in face processing : Evidence from hemispheric specialization[END_REF][START_REF] Sergent | About face : Left-hemisphere involvement in processing physiognomies[END_REF]. These observations have also been reported more recently for emotional expression perception. For example, [START_REF] Calvo | Brain lateralization of holistic versus analytic processing of emotional facial expressions[END_REF] reported distinct amplitudes of ERP components between emotional expressions when the whole face was presented in the right hemisphere, while higher responses were observed in the left hemisphere when presenting isolated facial features (e.g., smiling mouth). Using eye tracking, [START_REF] Calvo | Detection of emotional faces : Salient physical features guide effective visual search[END_REF] found that participants focus preferentially on critical regions to categorize a given expression, for example on the mouth for Happiness and Disgust and on the eye region for Fear, Sadness and Anger [START_REF] Bombari | Emotion recognition : The role of featural and configural face information[END_REF][START_REF] Wegrzyn | Mapping the emotional face. How individual face parts contribute to successful emotion recognition[END_REF]. Thus, our results might suggest that the saliency of the eye region, and possibly the tears, in sad faces leads to an increase in part-based processing subtended preferentially by the left hemisphere, whereas the detection of Fear would be relatively more holistic, preferentially subtended by the right hemisphere. This latter result is supported by increased inversion effects for Fearful faces compared to Happiness and Disgust (Dzhelyova et al., 2017). This holistic processing for Fear could explain the reduced brain activity observed here, as this emotion could be discriminated at a glance without checking every single part of the face thus leading to a lesser attentional cost. In the same vein, the higher activity for a face expressing Sadness could be explained by a higher attentional recruitment to be processed part by part, especially driven by eye-regions [START_REF] Bombari | Emotion recognition : The role of featural and configural face information[END_REF][START_REF] Wegrzyn | Mapping the emotional face. How individual face parts contribute to successful emotion recognition[END_REF]. Despite strong evidence of the preferential involvement of holistic processing, it has been shown that emotional faces are recognized above chance level when the stimuli are scrambled, inverted or blurred, thus suggesting that both processes can be used [START_REF] Bombari | Emotion recognition : The role of featural and configural face information[END_REF]. In our study, the lack of difference of interhemispheric activation for Disgust, Anger and Happiness suggests that the detection of these expressions would be based on a combination of both holistic and feature-based processing. These latter results regarding Anger and Happiness corroborate previous findings highlighting the implication of these two modes of processing for these expressions [START_REF] Meaux | Facing mixed emotions : Analytic and holistic perception of facial emotion expressions engages separate brain networks[END_REF].

Finally, although Surprise has been less frequently studied [START_REF] Leleu | Tuning functions for automatic detection of brief changes of facial expression in the human brain[END_REF]Lynn & Salbury, 2009;[START_REF] Poncet | Rapid and automatic discrimination between facial expressions in the human brain[END_REF], it was previously assumed that diagnostic features are not clear for this facial expression [START_REF] Calvo | Brain lateralization of holistic versus analytic processing of emotional facial expressions[END_REF][START_REF] Calvo | Selective eye fixations on diagnostic face regions of dynamic emotional expressions : KDEF-dyn database[END_REF]. For example, [START_REF] Smith | Transmitting and Decoding Facial Expressions[END_REF] emphasized the involvement of the mouth in their response classification technique, whereas Guo (2012) emphasized on the importance of the eye region for the recognition of Surprised faces. However, the involvement of the left hemisphere during the processing of this facial expression in our study using natural and highly variable images suggests that it could be processed rather part by part than holistically, contrary to previous research suggesting a holistic processing by using posed and decontextualized faces [START_REF] Calvo | Brain lateralization of holistic versus analytic processing of emotional facial expressions[END_REF]. Future studies will be necessary to disentangle the respective implication of holistic and feature-based processing depending on the type of emotion as well as the impact of contextual and dynamic aspects in expression processing.

Differential spatial distributions depending on emotional expressions

Although occipito-temporal activity was prominent for each of the 6 expressions, our study highlighted two distinct neural signatures according to the expression. More specifically, Happiness and Anger produced larger responses over medio-occipital region compared to other expressions, while Disgust and Sadness produced larger over the left lateral occipito-temporal region than Fear. Distinct spatial occipito-temporal distributions were also previously found by means of a similar FPVS-EEG paradigm (Dzhelyova et al., 2017;[START_REF] Leleu | Tuning functions for automatic detection of brief changes of facial expression in the human brain[END_REF]). Dzhelyova et al. (2017) observed more dorsal occipito-temporal distribution for Happiness and more ventral occipito-temporal distribution for Fear and Disgust expression. These authors interpreted this distinct distribution in terms of regions involved in avoidance or approach-related expressions (see Elliot & Covington, 2001 for a review). These two motivational systems would be underpinned by distinct neural substrates [START_REF] Pizzagalli | Frontal Brain Asymmetry and Reward Responsiveness : A Source-Localization Study[END_REF]. In line with this interpretation, our observations pointed out more medio-occipital distributions for Anger and Happiness, both being associated with approach-related expression [START_REF] Carver | Anger is an approach-related affect : Evidence and implications[END_REF][START_REF] Davidson | Anterior cerebral asymmetry and the nature of emotion[END_REF]. In contrast, the other expressions, Disgust, Surprise and Sadness (except Fear), recruiting more lateral occipito-temporal regions, are generally associated with avoidance [START_REF] Lee | Discrete Emotion and Motivation : Relative Activation in the Appetitive and Aversive Motivational Systems as a Function of Anger, Sadness, Fear, and Joy During Televised Information Campaigns[END_REF].

Time course of facial expression discrimination

Our analyses revealed two time-domain responses during which expressive faces were discriminated from neutral faces, with specific patterns according to the emotional expressions. A first temporal window appeared early between 189 and 346 msec and a second temporal window occurred later between 349 and 431 msec over occipito-temporal regions. Our results thus highlight a rapid discrimination of emotional faces among neutral ones for all expressions as soon as the first time window, except for Happiness for which this discrimination only appears later, during the second time window. Even if a strict comparison between the time components observed during FPVS and ERPs obtained during standard stimulation cannot be drawn, the time windows identified here are similar to the modulation of ERP components by facial expressions (e.g., N170: [START_REF] Balconi | Consciousness and emotion : ERP modulation and attentive vs. pre-attentive elaboration of emotional facial expressions by backward masking[END_REF][START_REF] Batty | Early processing of the six basic facial emotional expressions[END_REF]Eimer, Holmes & McGlone, 2003;[START_REF] Meaux | Early visual ERPs are influenced by individual emotional skills[END_REF]. Specifically, we observed a large (189-346 msec) window during which a bilateral negativity occurred over occipito-temporal sites for all expressions (except Happiness in both hemisphere and Surprise in the right hemisphere). This time window could be put in parallel with the well-known N170 component, a negative component, peaking between 140 and 200 following stimulus onset. This facesensitive component (see [START_REF] Rossion | Does physical interstimulus variance account for early electrophysiological face sensitive responses in the human brain? Ten lessons on the N170[END_REF] for review), associated with structural encoding of faces [START_REF] Bentin | Electrophysiological Studies of Face Perception in Humans[END_REF], has been shown to be modulated by facial expression using static (e.g. [START_REF] Batty | Early processing of the six basic facial emotional expressions[END_REF][START_REF] Caharel | The effects of familiarity and emotional expression on face processing examined by ERPs in patients with schizophrenia[END_REF][START_REF] Dasilva | On dissociating the neural time course of the processing of positive emotions[END_REF][START_REF] Leppänen | Differential electrocortical responses to increasing intensities of Fearful and happy emotional expressions[END_REF] and dynamic (ERPs: [START_REF] Miyoshi | Face-specific N170 component is modulated by facial expressional change[END_REF]MEG: Sato, Kochiyama & Uono, 2015) presentations. Notably, its amplitude increases for negative expressions compared to positive ones (e.g. [START_REF] Batty | Early processing of the six basic facial emotional expressions[END_REF][START_REF] Caharel | The effects of familiarity and emotional expression on face processing examined by ERPs in patients with schizophrenia[END_REF][START_REF] Turano | Fear boosts the early neural coding of faces[END_REF]see Hinojosa, Mercado & Carretié, 2015 for a metaanalysis). Qualitatively, we also observed in the present study larger waveform amplitudes for negative emotions, especially for Sadness compared to Happiness and Surprise. Recently, a study highlighted the sensitivity of the N170 to the eyeregions by combining eye movements analysis and EEG recording when participants visualized emotional faces [START_REF] Neath-Tavares | Neural processing of fearful and happy facial expressions during emotion-relevant and emotion-irrelevant tasks : A fixation-to-feature approach[END_REF]. Thus, a possible explanation for the larger amplitude observed for Sadness could be that the processing of this emotion is relatively more dependent on the eye regions, as previously mentioned [START_REF] Bombari | Emotion recognition : The role of featural and configural face information[END_REF][START_REF] Wegrzyn | Mapping the emotional face. How individual face parts contribute to successful emotion recognition[END_REF].

Altogether, we observed clear categorization responses of emotional expression appearing as soon as early perceptual stages of face processing.

Conclusions

To conclude, the present study provides objective evidence for robust rapid and automatic facial expression categorization processes in the human brain from highly variable natural images. Specifically, our findings highlighted that this ability of the human brain to detect facial emotions can be generalized across changes of identity, viewpoint, sex, ethnic origin, but also across the context of the image (natural environments). This effect is extremely robust since it was observed for each change of expression in every individual tested, emphasizing the high sensitivity of the FPVS-EEG approach.

Fig. 1 .

 1 Fig. 1. Stimuli and experimental design. A. Example of a sequence with neutral expression faces at the base rate (6 Hz -blue line) with the constraint that every five faces an emotional expression, Happiness in this example, was periodically inserted at the expressionspecific rate of 1.2 Hz (6 Hz/5 = 1.2 Hz -orange dotted line). B. Each sequence started with a fixation cross presentation which varied between 2-5 seconds, followed by a 2 seconds fade-in, 50 sec of stimuli presentation and finished with a 2 seconds fade-out. Each sequence was presented with two orientations (upright and inverted faces) and presented four times, resulting in 48 sequences (2 orientations; 6 expressions -Disgust, Fear, Happiness, Surprise, Anger and Sadness -and four repetitions).

Fig. 2 .

 2 Fig. 2. Grand-average EEG spectra in SNR of all emotional expressions (average across Disgust, Fear, Happiness, Surprise, Anger and Sadness). The upper part of the figure represents neural response to the base rate (6 Hz) and its significant harmonics (2F = 12 Hz, 3F = 18 Hz, 4F = 24 Hz and 5F = 30 Hz) over a medial occipital region extending slightly to right occipito-temporal region of interest (ROI) (averaged across Oz, OIz, Iz and O2 channels), shown separately for A. upright faces and B. inverted faces. Lower part of the figure represents emotional-specific response (F/5 = 1.2 Hz) and its significant harmonics (2F/5 = 2.4 Hz, 3F/5 = 3.6 Hz, 4F/5 = 4.8 Hz, 6F/5 = 7.2 Hz, 7F/5 = 8.4 Hz and 8F/5 = 9.6 Hz) over occipito-temporal ROI (averaged across left and right occipito-temporal channels, P9, PO7, PO9 and PO11 and P10, PO8, PO10 and PO12 respectively), shown separately for C. upright and D. inverted faces. 3Dtopographic scalp maps (posterior view) on the average of all emotions and significant responses (same scale for both orientations, ranging from 0 to 6 µV for the general visual responses (A. and B.) and ranging from 0 to 2 µV for the expression-specific responses (C. and D.) showed the distinct scalp distribution.

Fig. 3 .

 3 Fig. 3. Neural responses to expression-specific frequency for A. upright and B. inverted faces. The sum of baseline-corrected amplitudes (in µV) of the expression-specific response and its significant harmonics (until 8F/5, i.e. 9.6 Hz, excluding the base rate at 6 Hz) over left (average across P9, PO7, PO9, PO11 channels; green bars) and right (average across P10, PO8, PO10, PO12 channels; blue bars) occipito-temporal regions plotted separately for each facial expression (respectively left OT and right OT). Error bars are standard errors of the mean (SEM). * indicates a significant difference between left and right ROI for the concerned emotional expression. Scalp topographies (in µV) shown for each expression (Disgust, Fear, Happiness, Surprise, Anger and Sadness) separately with the same scale (from 0 to 3.00 µV) for the summed baseline-corrected amplitude of the 1.2 Hz response and its subsequent significant harmonics. Neural responses to emotional expression are largely reduced for inverted faces compared to upright faces.

Fig. 4 .

 4 Fig. 4. Individual responses to emotional expression changes. A. Hemispheric lateralization at individual level based on summed baseline-corrected amplitude for each expression change (dark blue indicated a higher amplitude in the right than in the left hemisphere; dark green indicated a higher amplitude in the left than in the right hemisphere (p < .05); light color (blue or green) indicated a trend toward significance (.05 < p < .07) and orange indicated a significant response in both left and right ROI without interhemispheric

Fig. 5 .

 5 Fig. 5. Temporal dynamics of the brief expression changes detection. The differential responses between upright and inverted faces are shown independently for each emotional expression over two occipito-temporal channels A. in the left hemisphere (PO9) and B. in the contralateral hemisphere (PO10). Data are displayed for a segment of 1 second locked to the presentation of the emotional expression, and these EEG data are shown with the signals at 6Hz and its harmonics filtered out.

Disgust Happiness Sadness Anger Surprise Neutral

	Hit	rates	83.48	91.74	99.57	96.09	94.91	93.91	84.13
	(%)		(16.64)	(10.72)	(2.09)	(7.22)	(7.83)	(10.33)	(15.61)
	(SD)
	Intensity	7.14	6.79	7.11	7.07	6.92	7.11	6.25
	(SD)		(1.10)	(1.55)	(1.14)	(1.19)	(1.35)	(1.17)	(2.01)

Table 1 . Behavioral rating of natural images. Mean hit rate (SD) and mean intensity (SD) for selected images for each emotional expression category.

 1

time window Second time window

			Start	End	Start	End
		Disgust	208	263	380	415
		Fear	204	240	388	403
	ess	Happin			349	415
	e	Surpris	189	231	361	403
		Anger	204	286	361	431
	s	Sadnes	208	333
		PO10	First time window Start End	Second time window Start End
		Disgust	208	255
		Fear	212	271	376	423
	ess	Happin			369	415
		Surpris
	e
		Anger	208	259	369	411
	s	Sadnes	224	345

Table 2 . Time course (in msec) of the detection of emotional expression changes. Time points at which differential waveforms (upright minus inverted) for each emotional expression differed significantly (p < .05) over left (PO9) and right (PO10) occipito-temporal channels.

 2

Acknowledgment

The authors thank Ernesto Palmero Soler for his help in the statistical analysis of the EEG data and Margaret Moulson for the collection of the experimental images. We also thank the editor and the two anonymous reviewers for their careful reading and constructive comments on a previous version of this paper.

Data and code availability

The study data is available at https://data.4tu.nl . The EEG data were analyzed using the open source Letswave software package (https://github.com/NOCIONS/Letswave5), running in MATLAB R2013a (MathWorks, USA).

All face stimuli used in the experiment were collected from the Internet. The identity of the copyright holders was unknown, thus preventing us from making the face stimuli publicly available or sharing them.

EEG data, behavioral data and data analysis steps of the study are available at https://osf.io/tkrha/.

The experimental design was implemented using custom software written in Java. The code is available at https://osf.io/tkrha/.

No part of the study procedures and analyses was pre-registered prior to the research being conducted.

Biobehavioral

Reviews, 71, 810-828. https://doi.org/10.1016/j.neubiorev.2016.10.020 Dzhelyova, M., Jacques, C., & Rossion, B. (2017)