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P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
Published on: September 8, 2023
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Functional Brain Connectivity as a New Feature for P300 Speller.
Aya Kabbara1,2, Mohamad Khalil1,2, Wassim El-Falou1,2
1Department of electrical and computer engineering, ULFG1, Tripoli, Lebanon.
Plos One
|January 12, 2016
Summary
This study introduces phase synchrony, a measure of brain connectivity, for Brain Computer Interfaces (BCI). It improves P300 speller performance by detecting functional relationships between brain regions during visual stimuli.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Brain Computer Interfaces (BCI) often overlook functional connectivity between brain regions.
- Existing feature extraction methods in BCI may not fully capture complex neural interactions.
Purpose of the Study:
- To introduce functional connectivity, specifically phase locking value (PLV), for characterizing evoked responses (ERPs).
- To evaluate the efficacy of PLV in a P300 speller BCI system.
- To compare PLV-based methods against established P300 speller techniques.
Main Methods:
- Analysis of electroencephalographic (EEG) signals from ten subjects.
- Quantification of functional connectivity using phase locking value (PLV).
- Comparison with peak picking, area, time/frequency features, xDAWN, and SWLDA.
Main Results:
- Phase synchrony provides significant information for P300 speller classification.
- High synchronization observed in target trials, low in non-target trials.
- Phase synchrony outperforms some existing methods, especially with more trials.
Conclusions:
- Functional connectivity, quantified by PLV, is a valuable feature for P300 speller BCI.
- Combining PLV with classical features enhances P300 speller performance.
- PLV-based approaches show competitive or superior results compared to state-of-the-art methods.
