Intersession Robust Hybrid Brain-Computer Interface: Safe and User-Friendly Approach with LED Activation Mechanism

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Traditional visual stimulus-based Brain-Computer Interface (BCI) systems can negatively impact user eye health due to visual fatigue.
• There is a need for BCI systems that offer secure activation and maintain stability across varying physiological and psychological conditions.
• Integrating Electroencephalography (EEG) and Electrooculography (EOG) signals presents a promising avenue for developing advanced BCI functionalities.

Purpose of the Study:

• To introduce a hybrid BCI system integrating EEG and EOG signals with a novel, secure activation mechanism.
• To minimize visual fatigue and eye strain associated with conventional visual stimulus BCI systems.
• To enhance system stability and accuracy by reducing inter-session variance.

Main Methods:

• Development of a hybrid BCI system utilizing Electroencephalography (EEG) and Electrooculography (EOG) signals.
• Implementation of a 7 Hz LED stimulus for safe system activation, reducing visual fatigue.
• Application of the Correlation Alignment (CORAL) method for inter-session variance reduction and Bootstrap Aggregating for classification.

Main Results:

• The hybrid BCI system demonstrated increased accuracy from 81.54% to 94.29% after applying the CORAL method.
• The system effectively reduced visual fatigue while maintaining command generation through moving objects.
• The proposed system showed robust stability and adaptability to users' changing cognitive states.

Conclusions:

• The hybrid BCI system offers a safe and effective activation mechanism, mitigating eye health concerns.
• The integration of CORAL and Bootstrap Aggregating significantly enhances BCI system accuracy and stability.
• This approach provides a practical and high-performing BCI solution, comparable or superior to existing systems, even with fewer EEG channels.