Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification.

ABSTRACT: Incorporating brain-computer interfaces (BCIs) into daily life requires reducing the reliance of decoding algorithms on the calibration or enabling calibration with the minimal burden on the user. A potential solution could be a pre-trained decoder demonstrating a reasonable accuracy on the naive operators. Addressing this issue, we considered ambiguous stimuli classification tasks and trained an artificial neural network to classify brain responses to the stimuli of low and high ambiguity. We built a pre-trained classifier utilizing time-frequency features corresponding to the fundamental neurophysiological processes shared between subjects. To extract these features, we statistically contrasted electroencephalographic (EEG) spectral power between the classes in the representative group o

SUBMITTER: Kuc A 

PROVIDER: S-EPMC8635058 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature