Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI With a Single Flicker Frequency.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Steady-state visual evoked response (SSVEP) brain-computer interfaces (BCIs) typically require multiple stimuli to encode control channels.
• Existing SSVEP BCIs rely on frequency, phase, or time domain encoding, limiting flexibility and user experience.

Purpose of the Study:

• To develop and evaluate a novel SSVEP BCI system capable of controlling multiple channels using only a single flicker stimulus.
• To investigate the feasibility of using scalp electroencephalography (EEG) response topographies to decode user attention location.

Main Methods:

• A single flicker stimulus was presented, and users directed their attention to different spatial positions.
• EEG data was recorded from healthy volunteers, and machine learning was used to classify the distinct SSVEP topographies corresponding to attention locations.
• The influence of control channels, trial length, and EEG channel selection on classification accuracy was analyzed.

Main Results:

• The novel SSVEP BCI successfully recognized 9 distinct targets with approximately 95% accuracy.
• An average information transfer rate (ITR) of 40.8 bits/min was achieved.
• Five EEG channels over parieto-occipital areas were found sufficient for reliable topography classification.

Conclusions:

• This single-stimulus SSVEP BCI approach, leveraging attention-based topography classification, offers a promising alternative to traditional multi-stimulus methods.
• The system demonstrates robust performance and a simple setup, making it suitable for widespread applications on computers.
• Optimizing trial length presents a significant opportunity to further enhance the information transfer rate (ITR).