Hannan Naseem Riaz, a Ph.D. student, has successfully presented her paper, “Inefficacy Prediction of Alpha Up-Regulation Neurofeedback Training Using Eyes-Open Resting State Wavelet Features and Machine Learning*.” at the 7th IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES 2022). The conference fee is fully funded by the Center of Healthcare Science and Technology (CHST) under the Excellent Research Center Award Fund. The paper aims to investigate the EEG learning process in alpha neurofeedback as an early-stage predictor of learners and non-learners in terms of the enhancement of alpha-band activities. 25 healthy participants have been trained using alpha up-regulations. 8 of them were unable to regulate their alpha band within each session. Hence in this work resting state eyes-open EEG is used to predict the learning performance of the NFT participants. Using machine learning. A comparison of three machine learning algorithms; LDA, SVM, and GBM is performed to predict the non-learners based on the absolute alpha power and its Daubechies (level-4) wavelet decompositions eyes-open resting state EEG signals.
