Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space

Computational Intelligence and Neuroscience
Ioannis XygonakisPanagiotis D Bamidis

Abstract

Brain-Computer Interface (BCI) is a rapidly developing technology that aims to support individuals suffering from various disabilities and, ultimately, improve everyday quality of life. Sensorimotor rhythm-based BCIs have demonstrated remarkable results in controlling virtual or physical external devices but they still face a number of challenges and limitations. Main challenges include multiple degrees-of-freedom control, accuracy, and robustness. In this work, we develop a multiclass BCI decoding algorithm that uses electroencephalography (EEG) source imaging, a technique that maps scalp potentials to cortical activations, to compensate for low spatial resolution of EEG. Spatial features were extracted using Common Spatial Pattern (CSP) filters in the cortical source space from a number of selected Regions of Interest (ROIs). Classification was performed through an ensemble model, based on individual ROI classification models. The evaluation was performed on the BCI Competition IV dataset 2a, which features 4 motor imagery classes from 9 participants. Our results revealed a mean accuracy increase of 5.6% with respect to the conventional application method of CSP on sensors. Neuroanatomical constraints and prior neurophysiolog...Continue Reading

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Citations

Dec 9, 2020·Entropy·Luisa Velasquez-MartinezGermán Castellanos-Dominguez
Nov 25, 2019·Biomedical Physics & Engineering Express·Madiha TariqMilan Simic
Dec 19, 2020·Journal of Neural Engineering·James D BennettAnthony N Burkitt
Apr 20, 2021·Frontiers in Neuroscience·Sai Kalyan Ranga Singanamalla, Chin-Teng Lin

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Methods Mentioned

BETA
imaging techniques
feature extraction

Software Mentioned

iso2mesh
Fieldtrip script
OpenMEEG
Brainstorm

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