Multisymbol Time Division Coding for High-Frequency Steady-State Visual Evoked Potential-Based Brain-Computer...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Optimizing coding stimulus is critical for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs).
• Existing methods face challenges in encoding a large number of targets with limited stimulation parameters.
• There is a need for efficient encoding strategies to improve BCI usability and reduce training data requirements.

Purpose of the Study:

• To propose and evaluate a novel encoding approach, Multi-Symbol Time Division Coding (MSTDC), for SSVEP-based BCIs.
• To demonstrate MSTDC's capability to encode multiple targets using finite stimulation frequencies and phases.
• To assess the performance of MSTDC in terms of accuracy and information transfer rate (ITR) for multi-target SSVEP BCIs.

Main Methods:

• Developed MSTDC based on maximizing the distance between neural responses.
• Designed an SSVEP-BCI system with 40 targets using four temporal-divided stimuli at 30 Hz with varying phases.
• Devised a 72-target SSVEP-BCI system and employed a Template Splicing task-related component analysis (TRCA) algorithm using a 40-target dataset for training.

Main Results:

• The 40-target system achieved an average accuracy of 96.77% and an ITR of 119.05 bits/min.
• The 72-target system, using the TRCA algorithm, attained an average accuracy of 86.23% and an ITR of 95.68 bits/min.
• MSTDC effectively encoded multiple targets with limited frequencies and phases, demonstrating scalability and reduced training data costs.

Conclusions:

• MSTDC is a feasible and effective technique for developing comfortable, multi-target SSVEP-based BCIs.
• The proposed approach maintains a high information transfer rate while accommodating a large number of targets.
• MSTDC offers a scalable solution that can be readily integrated into other BCI systems, reducing development costs.