Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Conventional neurofeedback systems often limit user experience due to high interindividual variability in electroencephalography (EEG) features.
• This variability restricts the range of feedback users can perceive, hindering effective neurofeedback training.
• A need exists for adaptive strategies that accommodate individual differences in EEG patterns.

Purpose of the Study:

• To propose and validate a data-driven neurofeedback strategy that accounts for individual EEG variability.
• To enable users to experience a wider range of auditory or visual feedback without manual customization.
• To improve the overall effectiveness of neurofeedback training by optimizing feedback delivery.

Main Methods:

• Developed a data-driven neurofeedback strategy adjusting feedback level ranges based on EEG feature density from an offline database.
• Collected EEG data from 22 healthy subjects for offline database construction.
• Validated the strategy with 5 subjects in online experiments using optimized bin sizes.

Main Results:

• The data-driven strategy significantly increased the number of experienced feedback levels by 139% in offline and 144% in online experiments compared to uniform bin sizes.
• Optimized bin sizes based on individual EEG feature density effectively expanded the feedback range.
• Demonstrated a substantial improvement in the breadth of feedback accessible to individual users.

Conclusions:

• The proposed data-driven neurofeedback strategy effectively addresses interindividual EEG variability.
• This approach significantly enhances the range of feedback levels experienced by users during neurofeedback training.
• The findings suggest a more personalized and effective neurofeedback experience is achievable.