Event-related brain potential markers of visual and auditory perception: A useful tool for brain computer interface systems - Proverbio, Alice Mado; Tacchini, Marta; Jiang, Kaijun (Frontiers in Behavioral Neuroscience, 2022)
Objective. The majority of BCI systems, enabling the communication with locked-in-patients, 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 (ERP) has taken off especially for face or music recognition, but this new approach is generally unknown to neuro-engineering research. 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 mental content from brain activity. Approach. Thirty participants were administered 280 colour pictures (adult, infant and animal faces, human bodies, written words, checkerboards and objects) and 120 auditory files (speech, music, affective vocalizations). This paradigm did not involve target selection to avoid artefactual 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 x 126 electrode channels (1 million 512,000 ERP waveforms) were processed and artifact-rejected. Main results. Clear and distinct category-dependent markers of perceptual and cognitive processing were identified through statistical analyses, some of which were novel in the literature. Results are discussed in the view of current knowledge on ERP functional properties, and with respect to machine-learning classification methods previously applied to similar data. Significance. The data showed a high level of accuracy (p ≤ 0.01) in the ability of statistical analyses to discriminate the perceptual categories eliciting the various electrical potentials. Therefore, the ERP markers here identified might be precious tools for optimizing BCI systems (pattern recognition, or Artificial Intelligence (AI) algorithms) applied to EEG/ERP signals.