Basic Taste Sensation Recognition From EEG Based on Multiscale Convolutional Neural Network With Residual Learning

TastePeptides-EEG: An Ensemble Model for Umami Taste Evaluation Based on Electroencephalogram and Machine Learning

“…Gao used multiscale convolutional neural network with residual learning to fit multichannel time-electrical signal data and proposed the EEG-MSRNet model. This model achieved an accuracy rate of 49.95% and an AUC31 of 0.71 for the five basic tastes . In addition to EEG data, You Wang used differential electrodes to detect facial and chewing muscles to obtain surface electromyography (sEMG) and further achieved an accuracy rate of 74.46% by random forest algorithm .…”