| Summary: | The neural processing of incoming stimuli can be analysed from the
electroencephalogram (EEG) through event-related potentials (ERPs). The P3
component is largely investigated as it represents an important
psychophysiological marker of psychiatric disorders. This is composed by several
subcomponents, such as P3a and P3b, reflecting distinct but interrelated sensory
and cognitive processes of incoming stimuli. Due to the low EEG
signal-to-noise-ratio, ERPs emerge only after an averaging procedure across
trials and subjects. Thus, this canonical ERP analysis lacks in the ability to
highlight EEG neural signatures at the level of single-subject and single-trial.
In this study, a deep learning-based workflow is investigated to enhance EEG
neural signatures related to P3 subcomponents already at single-subject and at
single-trial level. This was based on the combination of a convolutional neural
network (CNN) with an explanation technique (ET). The CNN was trained using two
different strategies to produce saliency representations enhancing signatures
shared across subjects or more specific for each subject and trial. Cross-subject
saliency representations matched the signatures already emerging from ERPs, i.e.,
P3a and P3b-related activity within 350–400 ms (frontal sites) and 400–650 ms
(parietal sites) post-stimulus, validating the CNN+ET respect to canonical ERP
analysis. Single-subject and single-trial saliency representations enhanced P3
signatures already at the single-trial scale, while EEG-derived representations
at single-subject and single-trial level provided no or only mildly evident
signatures. Empowering the analysis of P3 modulations at single-subject and at
single-trial level, CNN+ET could be useful to provide insights about neural
processes linking sensory stimulation, cognition and behaviour.
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