Abstract
Brain-computer interfaces have been proposed for stroke rehabilitation, but there are some impeding factors for them to be translated into clinical practice. One of them is the need for calibration. In this study it was investigated if subject-independent calibration is possible for detecting movement-related potentials associated with hand movements, and what the optimal number of movement epochs is to maximize the detection performance. Twelve healthy subjects performed 100 palmar grasps while continuous EEG was recorded. Template matching was performed between movement and idle epochs. 72 ± 10% of all epochs were correctly classified using the subject-independent approach while 78 ± 9% of the epochs were correctly classified using the individualized approach. The highest classification accuracies were obtained when using 54 ± 23 movement epochs for calibration. In conclusion, it is possible to use a subject-independent approach for detecting movement-related cortical potentials, but the performance is slightly lower compared to individualized calibration.
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This work was funded by VELUX FONDEN (project no. 22357).
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Jochumsen, M. (2022). Subject-Independent Detection of Movement-Related Cortical Potentials and Classifier Adaptation from Single-Channel EEG. In: Torricelli, D., Akay, M., Pons, J.L. (eds) Converging Clinical and Engineering Research on Neurorehabilitation IV. ICNR 2020. Biosystems & Biorobotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-030-70316-5_13
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DOI: https://doi.org/10.1007/978-3-030-70316-5_13
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