Training and transfer effect of evoked brain responses by brain-computer interaction

Area of Science:

• Neuroscience
• Biomedical Engineering
• Cognitive Science

Background:

• Electroencephalography (EEG)-based neurofeedback training (NFT) is vital for improving brain-computer interface (BCI) performance by guiding users to regulate neural activity.
• Existing NFT methods often lack cross-task transfer effects, limiting improvements to trained tasks and common cognitive functions.

Purpose of the Study:

• To propose and evaluate a steady-state visual evoked potential (SSVEP)-based table hockey BCI game as an NFT approach.
• To investigate the cross-task transfer effects of SSVEP-based NFT on BCI performance and neural activity.

Main Methods:

• Forty healthy subjects were randomized into four groups: 10-frequency NFT, 5-frequency NFT with transfer, placebo, and control.
• Participants underwent five days of NFT training, completing an online SSVEP task before and after training.
• EEG data and subjective experiences were recorded throughout the study.

Main Results:

• NFT groups showed significant improvements in SSVEP classification accuracy, accompanied by increased SSVEP power and inter-trial phase coherence (ITPC).
• NFT led to an expansion of activated cortical areas.
• Crucially, performance enhancements generalized to untrained tasks, suggesting effective transfer of learned neural regulation.

Conclusions:

• The proposed SSVEP-based NFT approach effectively enhances BCI performance in trained tasks.
• NFT induces transferable neural changes to adjacent frequencies, augmenting neural populations involved in processing visual stimuli.
• This study advances the understanding of how self-regulation during NFT improves task-related common functions.