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Selection of relevant features for classification of movements from single movement-related potentials using a genetic algorithm

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2 Author(s)
Yorn-Tov, E. ; Fac. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel ; Inbar, G.F.

Classification of movement-related potentials recorded from the scalp to their corresponding limb is a crucial task in brain-computer interfaces based on such potentials. This paper demonstrates how the features for such a task can be selected from a large bank of features using a genetic algorithm. We show that it is possible to differentiate between the movements of contralateral fingers with a classification accuracy of 77% using a small number of features (10-20) selected from a bank containing roughly 1000 features.

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Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE  (Volume:2 )

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