Inefficacy Prediction of Alpha Up-Regulation Neurofeedback Training Using Eyes-Open Resting State Wavelet Features and Machine Learning

Abstract

Neurofeedback Training (NFT) is an effective way for the participants to self-regulate the Electroencephalography (EEG) activity based on real-time feedback. This procedure has been proven to improve the neurological disorders in mentally ill patients and the psychological behavior of healthy individuals. Despite the considerable success of neurofeedback techniques, it is observed that some subjects fail to learn how to control their brain activities during neurofeedback training. This study is aimed 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 upregulations. 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.