A case for hybrid BCIs: combining optical and electrical modalities improves accuracy.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Brain-computer interfaces (BCI) require user-friendly hardware for widespread adoption.
• Near-infrared spectroscopy (NIRS) and electroencephalography (EEG) are key modalities in BCI research.
• Existing BCI systems face challenges with hardware usability, size, and cost.

Purpose of the Study:

• To design, build, and validate a hybrid BCI system integrating NIRS with EEG using novel electrode types.
• To evaluate the performance of the hybrid system in a motor imagery task.
• To compare the efficacy of different electrode configurations and signal modalities.

Main Methods:

• Development of a novel hybrid hardware system combining NIRS and EEG with regular disk and tri-polar concentric ring electrodes (TCRE).
• Implementation of a two-dimensional motor imagery paradigm with 16 volunteers in off- and online sessions.
• Application of advanced signal processing techniques to extract and classify features from NIRS, EEG, and tEEG (EEG through TCRE).

Main Results:

• Tri-polar concentric ring electrodes (TCRE) demonstrated improved classification accuracy compared to standard disk electrodes and NIRS alone.
• The hybrid NIRS-EEG-tEEG system significantly outperformed individual modalities in classification accuracy.
• Synchronous recording of multiple modalities provided a comprehensive dataset for BCI analysis.

Conclusions:

• The developed hybrid BCI system enhances usability and performance through the integration of NIRS and EEG with TCRE.
• Combining multiple BCI modalities, particularly with advanced electrodes, offers superior accuracy for motor imagery tasks.
• This research paves the way for more effective and user-friendly BCI applications.