Explainable AI framework and quantum machine learning classifier based brain connectivity-feature classification for MI-EEG BCI applications

Key Points

• The study reveals enhanced brain connectivity feature classification for motor imagery using explainable AI.
• Key metrics indicate that the approach achieves a significant improvement in classification accuracy by 20%.
• Analysis employs a quantum machine learning classifier to process MI-EEG data effectively, ensuring reliable connections.
• These results highlight the potential of combining quantum algorithms with BCI technologies for more effective interface designs.