A novel training-free externally-regulated neurofeedback (ER-NF) system using phase-guided visual stimulation for alpha modulation.

The efficacy of neurofeedback is a point of great controversy, because a certain proportion of users cannot properly regulate their brain activities and thereby fail to benefit from neurofeedback. To address the neurofeedback inefficacy problem, the present study is aimed to design and implement a new neurofeedback system that can more effectively and consistently regulate users' brain activities than the conventional way of training users to voluntarily regulate brain activities. The new neurofeedback system delivers external visual stimuli continuously at a specific alpha phase, which is real-time decoded from ongoing alpha wave, to regulate the alpha wave. Experimental results show that the proposed training-free externally-regulated neurofeedback (ER-NF) system can achieve consistent (effective in almost all sessions for almost all users), flexible (either increasing or decreasing peak alpha frequency and alpha power), and immediate (taking or losing effect immediately after stimulation is on or off) modulation effects on alpha wave. Therefore, the ER-NF system holds great potential to be able to more reliably and flexibly modulate cognition and behavior.

Enhancing detection of steady-state visual evoked potentials using channel ensemble method.

Objective.This study proposed and evaluated a channel ensemble approach to enhance detection of steady-state visual evoked potentials (SSVEPs).Approach.Collected multi-channel electroencephalogram signals were classified into multiple groups of new analysis signals based on correlation analysis, and each group of analysis signals contained signals from a different number of electrode channels. These groups of analysis signals were used as the input of a training-free feature extraction model, and the obtained feature coefficients were converted into feature probability values using thesoftmaxfunction. The ensemble value of multiple sets of feature probability values was determined and used as the final discrimination coefficient.Main results.Compared with canonical correlation analysis, likelihood ratio test, and multivariate synchronization index analysis methods using a standard approach, the recognition accuracies of the methods using a channel ensemble approach were improved by 5.05%, 3.87%, and 3.42%, and the information transfer rates (ITRs) were improved by 6.00%, 4.61%, and 3.71%, respectively. The channel ensemble method also obtained better recognition results than the standard algorithm on the public dataset. This study validated the efficiency of the proposed method to enhance the detection of SSVEPs, demonstrating its potential use in practical brain-computer interface (BCI) systems.Significance. A SSVEP-based BCI system using a channel ensemble method could achieve high ITR, indicating great potential of this design for various applications with improved control and interaction.