SSVEP-Based Brain-Computer Interface With a Limited Number of Frequencies Based on Dual-Frequency Biased Coding

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) face challenges in encoding numerous targets with limited frequencies.
• Efficient target encoding is crucial for practical SSVEP BCI speller applications.

Purpose of the Study:

• To introduce and validate a novel dual-frequency biased coding (DFBC) method for SSVEP-based BCI spellers.
• To enhance the number of targetable characters in a virtual speller using a limited frequency resource.
• To evaluate the performance of DFBC against traditional coding methods.

Main Methods:

• Developed a dual-frequency biased coding (DFBC) method encoding targets with permuted flickering periods at different frequencies.
• Implemented a 48-character virtual speller using the DFBC paradigm.
• Collected SSVEP signals from three occipital channels (O1, Oz, O2) for target identification.
• Validated the method through offline (11 participants) and online (7 participants) experiments.

Main Results:

• The DFBC method achieved an accuracy of 76.6% in offline experiments and 79.3% in online experiments.
• DFBC demonstrated superior performance compared to the traditional multiple frequencies sequential coding (MFSC) method.
• The inherent self-correction capability of DFBC contributed to its high accuracy.

Conclusions:

• Dual-frequency biased coding (DFBC) is an effective strategy for encoding a large number of targets in SSVEP-based BCI spellers.
• DFBC offers improved accuracy and efficiency, overcoming limitations of existing frequency-based coding schemes.
• This study highlights DFBC's potential for advancing practical SSVEP BCI applications.