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Insights from EEG-Based Pattern Analysis and Image Reconstruction New Research Sensory and Motor Systems The Neural Dynamics of Facial Identity Processing: Insights from EEG-Based Pattern Analysis and Image Reconstruction Dan Nemrodov,1 Matthias Niemeier,1 Ashutosh Patel,1 and Adrian Nestor1 DOI:http://dx.doi.org/10.1523/ENEURO.0358-17.2018 1Department of Psychology, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario M1C1A4, Canada Abstract Uncovering the neural dynamics of facial identity processing along with its representational basis outlines a major endeavor in the study of visual processing. To this end, here, we record human electroencephalography (EEG) data associated with viewing face stimuli; then, we exploit spatiotemporal EEG information to determine the neural correlates of facial identity representations and to reconstruct the appearance of the corresponding stimuli. Our findings indicate that multiple temporal intervals support: facial identity classification, face space estimation, visual feature extraction and image reconstruction. In particular, we note that both classification and reconstruc- tion accuracy peak in the proximity of the N170 component. Further, aggregate data from a larger interval (50–650 ms after stimulus onset) support robust reconstruction results, consistent with the availability of distinct visual information over time. Thus, theoretically, our findings shed light on the time course of face processing while, methodologically, they demonstrate the feasibility of EEG-based image reconstruction. Key words: ERP; face space; image reconstruction; N170; pattern analysis; spatiotemporal dynamics Significance Statement Identifying a face is achieved through fast and efficient processing of visual information. Here, we investigate the nature of this information, its specific content and its availability at a fine-grained temporal scale. Notably, we provide a way to extract, to assess and to visualize such information from neural data associated with individual face processing. Thus, the present work accounts for the time course of face individuation through appeal to its underlying visual representations while, also, it provides a first demon- stration regarding the ability to reconstruct the appearance of stimulus images from electroencephalogra- phy (EEG) data. Introduction this end, considerable efforts have been devoted to char- Elucidating the dynamics of visual face processing is acterizing the time course of face processing especially essential to understanding its underlying mechanisms. To through the use of electroencephalography (EEG) and magnetoencephalography (MEG) given the temporal res- Received October 12, 2017; accepted January 12, 2018; First published Correspondence should be addressed to Dan Nemrodov, Department of January 29, 2018. Psychology at Scarborough, University of Toronto, 1265 Military Trail, Toronto, The authors declare no competing financial interests. Ontario M1C1A4, Canada, E-mail: [email protected]. Author contributions: D.N., M.N., and A.N. designed research; D.N., A.P., DOI:http://dx.doi.org/10.1523/ENEURO.0358-17.2018 and A.N. performed research; D.N. and A.N. analyzed data; D.N., M.N., A.P., Copyright © 2018 Nemrodov et al. and A.N. wrote the paper. This is an open-access article distributed under the terms of the Creative This work was supported by the Natural Sciences and Engineering Research Commons Attribution 4.0 International license, which permits unrestricted use, Council of Canada (A.N. and M.N.) and by a Connaught New Researcher distribution and reproduction in any medium provided that the original work is Award (A.N.). properly attributed. January/February 2018, 5(1) e0358-17.2018 1–17 New Research 2 of 17 olution of these methods. Accordingly, much is known To address the issues above, the current work aims to about the temporal profile of face processing as reflected derive face space constructs from the EEG signal asso- by either traditional event-related potentials (ERPs; Bentin ciated with consecutive time windows separately for dif- and Deouell, 2000; Itier and Taylor, 2002; Huddy et al., ferent facial expressions (i.e., neutral and happy). Then, it 2003; Tanaka et al., 2006; Rossion and Caharel, 2011; reconstructs the appearance of one set of faces (e.g., Zheng et al., 2012), or by spatiotemporal patterns (Liu happy) based on the structure of the face space derived et al., 2009; Cichy et al., 2014; Vida et al., 2017). Com- for the other faces (e.g., neutral). This demonstration pro- paratively less is known about the visual representations vides evidence that the spatiotemporal information of underlying the dynamics of face processing, especially as EEG patterns is rich enough to support: (1) identity-level related to facial identity. To shed light on this issue, the face discrimination; (2) neural-based face space estima- present work employs an image-reconstruction paradigm tion; (3) visual feature synthesis; and (4) facial image (Miyawaki et al., 2008; Naselaris et al., 2009; Nishimoto reconstruction. Further, this work characterizes the neural et al., 2011; Cowen et al., 2014; Chang and Tsao, 2017) dynamics of expression-invariant face processing while, seeking to approximate the visual appearance of individ- more generally, it provides proof of concept for the pos- ual faces from spatiotemporal EEG patterns. Concretely, sibility of EEG-based image reconstruction. this work aims to answer whether the visual information involved in face identification can be recovered from EEG Materials and Methods signals and, further, whether such information can sup- Participants port the characterization of neural-based face space Thirteen healthy adults (six males, seven females; age along with the reconstruction of individual face images. range: 18–27 years) with normal or corrected-to-normal Recent applications of pattern analysis have focused vision were recruited from the University of Toronto com- on the temporal profile of face discrimination at the cat- munity to participate in the study in exchange for mone- egory (Carlson et al., 2013; Van de Nieuwenhuijzen et al., tary compensation. All participants provided informed 2013; Cauchoix et al., 2014; Cichy et al., 2014; Kaneshiro consent and all experimental procedures were approved et al., 2015) and the exemplar level (Davidesco et al., by the Research Ethics Board at the University of Toronto. 2014; Ghuman, et al., 2014). For instance, expression- The data of all participants were included in the analyses invariant identity discrimination has been conducted reported below. using MEG (Vida et al., 2017), electrocorticography (Ghu- man et al., 2014) and EEG (Nemrodov et al., 2016). These Stimuli studies found multiple, distinct temporal windows sensi- A total of 140 face images of 70 individuals, each tive to facial information and, consistent with results from displaying a neutral and a happy expression were used as monkey neurophysiology (Hung et al., 2005; Freiwald experimental stimuli. Out of these, 108 images of 54 et al., 2009) and human psychophysics (Lehky, 2000; unfamiliar males were selected from three databases: AR Tanaka and Curran, 2001; Crouzet et al., 2010), have (Martinez and Benavente, 1998), Radboud (Langner et al., estimated an early onset for such sensitivity (Ghuman 2010), and FEI (Thomaz and Giraldi, 2010). The remaining et al., 2014; Nemrodov et al., 2016). Further, the cortical 32 images displayed faces of six famous male and 10 source of this information was attributed primarily to the female individuals selected from open access sources. To fusiform gyrus (FG) in line with homologous investigations be clear, unfamiliar male face stimuli are the focus of the of face identification using functional magnetic resonance present investigation while female faces were used as go imaging (fMRI; Nestor et al., 2011; Goesaert and Op de trials in a go/no-go gender recognition task (see Experi- Beeck, 2013; Anzellotti et al., 2014). mental design) and additional famous male faces were Yet, the representational basis of facial identity that included to promote alertness (however, no results are allows successful discrimination from neural data remains reported for them below due to the smaller stimulus set to be elucidated. Arguably, neural patterns elicited by that precluded a separate examination of famous face face perception speak to the properties of a representa- recognition). tional face space (Valentine, 1991), or, more generally, of All images featured young adult white individuals with a representational similarity space (Kriegeskorte et al., frontal view, gaze and illumination. The stimuli were se- 2008). In an effort to clarify the nature of such represen- lected so that no facial accessories, hair or makeup ob- tations, recent fMRI work (Nestor et al., 2016) has com- scured the internal features of the face and so that all bined the study of face space and neural-based image happy expressions displayed an open-mouth smile. reconstruction. Specifically, this work has derived visual These images were: (1) scaled uniformly and aligned with features from the structure of FG-based face space and, roughly the same position of the eyes and the nose; (2) then, used such features for facial image reconstruction. cropped to eliminate background; (3) normalized with the However, this work did not consider the temporal aspects same mean and root mean square (RMS) contrast values color ءbءaءof face processing, neither did it assess the invariant separately for each color channel in CIEL structure of a facial identity
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