Eduardo Hernández-Padilla, Aldo Bazán-Ramírez, Wilfredo Bazán-Ramírez, Juan Solano-Gutierrez
Parental participation and parents' support: effects on mathematics achievement, 2018 national assessment of learning, Mexico
Frontiers in psychology
electroencephalogram eeg signals critical interpreting sensorimotor activities predicting body movements however efficacy identifying intralimb movements dorsiflexion plantar flexion foot remains suboptimal study aims explore whether various eeg signal quantities effectively recognize intralimb movements facilitate development braincomputer interface bci devices foot rehabilitation research involved twentytwo healthy righthanded participants eeg data collected using 21 electrodes positioned motor cortex two electromyography emg electrodes recorded onset ankle joint movements study focused analyzing slow cortical potential scp sensorimotor rhythms smr alpha beta bands eeg five key featuresfourthorder autoregressive feature variance waveform length standard deviation permutation entropywere extracted modified recurrent neural network rnn including long shortterm memory lstm gated recurrent unit gru algorithms developed movement recognition compared conventional machine learning algorithms including nonlinear support vector machine svm k nearest neighbourhood knn classifiers performance proposed models assessed using two data schemes withinsubject acrosssubjects findings demonstrated gru lstm models significantly outperformed traditional machine learning algorithms recognizing different eeg signal quantities intralimb movement study indicates deep learning models particularly gru lstm hold superior potential standard machine learning techniques identifying intralimb movements using eeg signals accuracies lstm within across subjects 9887 180 8738 086 respectively whereas accuracy gru within across subjects 9918 128 8644 069 respectively advancement could significantly benefit development bci devices aimed foot rehabilitation suggesting new avenue enhancing physical therapy outcomes