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A neurophysiological approach to spatial filter selection for adaptive brain-computer interfaces.

James D Bennett¹, Sam E John¹, David B Grayden¹

¹Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia.

Journal of Neural Engineering

|December 18, 2020

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Classifying common spatial patterns (CSP) in electroencephalography (EEG) improves brain-computer interface (BCI) performance. Adapting CSP filters to evolving brain patterns is crucial for BCI decoder design and user training.

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Common Spatial Patterns (CSP) is effective for extracting features from electroencephalography (EEG) for Brain-Computer Interfaces (BCI).
Expert neurophysiological assessment is typically required to select appropriate CSP filters.
Automating CSP pattern classification can enhance BCI performance.

Purpose of the Study:

To identify, analyze, and automatically classify prototypical CSP patterns.
To improve motor imagery state prediction in BCIs by classifying CSP patterns.
To develop adaptive spatial filtering schemes for BCI decoders.

Main Methods:

Utilized four publicly available EEG datasets for a data-driven approach.
Employed cluster analysis to identify recurring CSP patterns.

Keywords:

EEG brain–computer interface common spatial patterns sensorimotor rhythms

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Developed a convolutional neural network for CSP pattern classification and evaluated adaptive filtering schemes.

Main Results:

Established distinct classes of neurophysiologically probable and improbable CSP patterns.
Found that discarding improbable CSP filters can decrease BCI decoder performance.
Demonstrated that adaptive CSP techniques can restore feature separability as spatial patterns evolve.

Conclusions:

Considering and reporting CSP spatial filter activation patterns is important for both online and offline BCI studies.
Spatial filter adaptation is critical for BCI decoder design, especially in online settings.
Adaptive CSP techniques can enhance user training for stable brain pattern development.