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This study aimed to investigate the psychophysiological markers of imagery processes through EEG/ERP recordings. Visual and auditory stimuli representing 10 different semantic categories were shown to 30 healthy participants. After a given interval and prompted by a light signal, participants were asked to activate a mental image corresponding to the semantic category for recording synchronized electrical potentials. Unprecedented electrophysiological markers of imagination were recorded in the absence of sensory stimulation. The following peaks were identified at specific scalp sites and latencies, during imagination of infants (centroparietal positivity, CPP, and late CPP), human faces (anterior negativity, AN), animals (anterior positivity, AP), music (P300-like), speech (N400-like), affective vocalizations (P2-like) and sensory (visual vs auditory) modality (PN300). Overall, perception and imagery conditions shared some common electro/cortical markers, but during imagery the category-dependent modulation of ERPs was long latency and more anterior, with respect to the perceptual condition. These ERP markers might be precious tools for BCI systems (pattern recognition, classification, or A.I. algorithms) applied to patients affected by consciousness disorders (e.g., in a vegetative or comatose state) or locked-in-patients (e.g., spinal or SLA patients).

Objective: A majority of BCI systems, enabling communication with patients with locked-in syndrome, are based on electroencephalogram (EEG) frequency analysis (e.g., linked to motor imagery) or P300 detection. Only recently, the use of event-related brain potentials (ERPs) has received much attention, especially for face or music recognition, but neuro-engineering research into this new approach has not been carried out yet. The aim of this study was to provide a variety of reliable ERP markers of visual and auditory perception for the development of new and more complex mind-reading systems for reconstructing the mental content from brain activity. Methods: A total of 30 participants were shown 280 color pictures (adult, infant, and animal faces; human bodies; written words; checkerboards; and objects) and 120 auditory files (speech, music, and affective vocalizations). This paradigm did not involve target selection to avoid artifactual waves linked to decision-making and response preparation (e.g., P300 and motor potentials), masking the neural signature of semantic representation. Overall, 12,000 ERP waveforms × 126 electrode channels (1 million 512,000 ERP waveforms) were processed and artifact-rejected. Results: Clear and distinct category-dependent markers of perceptual and cognitive processing were identified through statistical analyses, some of which were novel to the literature. Results are discussed from the view of current knowledge of ERP functional properties and with respect to machine learning classification methods previously applied to similar data. Conclusion: The data showed a high level of accuracy (p ≤ 0.01) in the discriminating the perceptual categories eliciting the various electrical potentials by statistical analyses. Therefore, the ERP markers identified in this study could be significant tools for optimizing BCI systems [pattern recognition or artificial intelligence (AI) algorithms] applied to EEG/ERP signals.