Abstract
Fast periodic visual stimulation (FPVS) allows the recording of objective brain responses of human face categorization (i.e., generalizable face-selective responses) with high signal-to-noise ratio. This approach has been successfully employed in a number of scalp electroencephalography (EEG) studies but has not been used with magnetoencephalography (MEG) yet, let alone with combined MEG/EEG recordings and distributed source estimation. Here, we presented various natural images of faces periodically (1.2 Hz) among natural images of objects (base frequency 6 Hz) whilst recording simultaneous EEG and MEG in 15 participants. Both measurement modalities showed face-selective responses at 1.2 Hz and harmonics across participants, with high and comparable signal-to-noise ratio (SNR) in about 3 min of stimulation. The correlation of face categorization responses between EEG and two MEG sensor types was lower than between the two MEG sensor types, indicating that the two sensor modalities provide independent information about the sources of face-selective responses. Face-selective EEG responses were right-lateralized as reported previously, and were numerically but non-significantly right-lateralized in MEG data. Distributed source estimation based on combined EEG/MEG signals confirmed a more bilateral face-selective response in visual brain regions located anteriorly to the common response to all stimuli at 6 Hz and harmonics. Conventional sensor and source space analyses of evoked responses in the time domain further corroborated this result. Our results demonstrate that FPVS in combination with simultaneously recorded EEG and MEG may serve as an efficient localizer paradigm for human face categorization.
Highlights
The speed and efficiency of our ability to categorize a large number of objects from visual input within a fraction of a second has been the focus of extensive neuroscientific research over the last decades (Cichy at al., 2014; Fabre-Thorpe, 2011; Lamme and Roelfsema, 2000; Palmeri and Gauthier, 2004; Riesenhuber and Poggio, 2002; Serre at al., 2007; Thorpe, 2009)
The categorization of a visual stimulus as a face may appear as a trivial task because it is achieved at astonishing speed and automaticity by a neurotypical human adult (Crouzet et al, 2010; Fletcher-Watson, Findlay et al, 2008; Hershler and Hochstein, 2005; Lewis and Edmonds, 2003), it is a challenging function for which artificial systems still lag well behind the human brain (e.g. Scheirer et al 2014)
A fast periodic visual stimulation (FPVS) paradigm combined with electroencephalographic (EEG) recordings has been suggested as an efficient way to study multiple levels and types of face categorization (Rossion, 2014b), in particular the categorization of natural visual stimuli as faces (Rossion et al, 2015)
Summary
The speed and efficiency of our ability to categorize a large number of objects from visual input within a fraction of a second has been the focus of extensive neuroscientific research over the last decades (Cichy at al., 2014; Fabre-Thorpe, 2011; Lamme and Roelfsema, 2000; Palmeri and Gauthier, 2004; Riesenhuber and Poggio, 2002; Serre at al., 2007; Thorpe, 2009). The neurotypical adult human brain quasi-automatically categorizes face stimuli at multiple levels: e.g., according to its emotional expression, sex, race, familiarity, etc. A fast periodic visual stimulation (FPVS) paradigm combined with electroencephalographic (EEG) recordings has been suggested as an efficient way to study multiple levels and types of face categorization (Rossion, 2014b), in particular the categorization of natural visual stimuli as faces (Rossion et al, 2015). The FPVS approach provides high signal-to-noise ratio (SNR) responses, allowing short recording durations. In recent years, this paradigm has been increasingly used with numerous variable natural images of faces and nonface objects, providing face-selective responses
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