Key result
Single-channel signal decomposition using detector-kernel networks improves EEG classification accuracy in a P300 speller.
Why the study?
Within a single trial, it remains difficult to accurately extract and detect the P300 component for brain-computer interfaces.
A novel detector-kernel network approach for single-channel signal decomposition improves P300 detection accuracy for brain-computer interfaces.
Event-related potentials (ERPs), an electroencephalogram (EEG) component, are elicited by various sensory, cognitive, and motor events. P300, one of the ERPs, is widely utilized for brain-computer interfaces (BCIs) for its robustness. However, within a single trial, it is still difficult to accurately extract and detect the P300 component. This paper presents an approach to extract time-locked components such as P300 by single-channel signal decomposition. Our proposed method is based on detector-kernel networks, which generate decomposed signals by convolving short waves called kernels to outputs of the detector network. We modify the networks to be suitable for decomposing time-locked components by generating decomposed signals as amplitude-modulated kernels. To validate our decomposition method, we conducted an EEG classification experiment in a P300 speller. Our method improved the accuracy of the classification, showing the effectiveness of the proposed method.
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Kotani et al. (2024) studied Brain-computer interfaces (BCIs) / P300 detection. Detector-kernel networks for single-channel signal decomposition was evaluated on Accuracy of EEG classification in a P300 speller. Single-channel signal decomposition using detector-kernel networks improved the accuracy of EEG classification in a P300 speller.
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