Amplitude Modulation Depth Coding Method for SSVEP-based Brain-computer Interfaces

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) face limitations in instruction set size due to restricted frequency resources.
• Increasing the number of stimuli in SSVEP-BCIs can lead to user discomfort due to expanded flickering areas.

Purpose of the Study:

• To propose and evaluate a novel Amplitude Modulation Depth Coding (AMDC) method for SSVEP-BCIs.
• To enhance coding efficiency and user experience in large-scale command SSVEP-BCI systems.

Main Methods:

• Developed an Amplitude Shift Keying (ASK) technique to dynamically modulate stimulus luminance levels.
• Assigned unique binary sequences to stimuli, utilizing two modulation depths per carrier frequency.
• Conducted experiments to analyze time-frequency responses and evaluate a 36-target AMDC paradigm for user experience and classification performance.

Main Results:

• The AMDC paradigm achieved an average classification accuracy of 81.7 ± 12.6% and an information transfer rate (ITR) of 45.4 ± 11.5 bits/min.
• Significantly reduced flicker perception and improved user comfort compared to traditional SSVEP stimuli.
• Demonstrated improved coding efficiency for single frequencies.

Conclusions:

• The proposed AMDC method offers a promising solution for increasing the scale of SSVEP-BCI systems.
• This approach enhances both communication efficiency and user comfort, paving the way for more advanced BCI applications.