Finding the optimal time window for increased classification accuracy during motor imagery

Blanco-Mora, D. A.; Aldridge, A.; Jorge, C.; Vourvopoulos, A.; Figueiredo, P.; Bermúdez i Badia, S.

http://hdl.handle.net/10400.13/4448

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Abstract(s)

Motor imagery classification using electroencephalography is based on feature extraction over a length of time, and different configurations of settings can alter the performance of a classifier. Nevertheless, there is a lack of standardized settings for motor imagery classification. This work analyzes the effect of age on motor imagery training performance for two common spatial pattern-based classifier pipelines and various configurations of timing parameters, such as epochs, windows, and offsets. Results showed significant (p ≤ 0.01) inverse correlations between performance and feature quantity, as well as between performance and epoch/window ratio.

Keywords

Brain-computer interface BCI Motor imagery MI Lassification accuracy Common spatial pattern CSP Electroencephalography EEG Neurorehabilitation Stroke . Faculdade de Ciências Exatas e da Engenharia

URI

http://hdl.handle.net/10400.13/4448

Citation

Blanco-Mora, D. A., Aldridge, A., Vieira, C. J., Vourvopoulos, A., Figueiredo, P., & i Badia, S. B. (2021). Finding the optimal time window for increased classification accuracy during motor imagery. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, (pp. 144-151).