@article{ref1,
title="Event-related brain potential markers of visual and auditory perception: a useful tool for brain computer interface systems",
journal="Frontiers in behavioral neuroscience",
year="2022",
author="Proverbio, Alice Mado and Tacchini, Marta and Jiang, Kaijun",
volume="16",
number="",
pages="e1025870-e1025870",
abstract="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. <br><br>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. <br><br>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. <br><br>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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="1662-5153",
doi="10.3389/fnbeh.2022.1025870",
url="http://dx.doi.org/10.3389/fnbeh.2022.1025870"
}