Blanco-Mora, D. A.Aldridge, A.Jorge, C.Vourvopoulos, A.Figueiredo, P.Bermúdez i Badia, S.2022-07-212022-07-212021Blanco-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).http://hdl.handle.net/10400.13/4448Motor 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.engBrain-computer interfaceBCIMotor imageryMILassification accuracyCommon spatial patternCSPElectroencephalographyEEGNeurorehabilitationStroke.Faculdade de Ciências Exatas e da Engenhariaconference object10.5220/0010316101440151