Conference Proceedings

A Deep Learning Algorithm for Classifying Grasp Motions using Multi-session EEG Recordings

A Partovi, SM Hosseini, M Soleymani, K Liaghat, S Ziaee, EHP Fard, SS Vajdi, F Goodarzy

9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021 | IEEE | Published : 2021


The classification of motor imagery tasks using scalp EEG signals is a complicated procedure in BCI especially when the task comprises multiple gestures of the same hand. In this paper, we present a classification method to distinguish three grasp motion classes (cylindrical, spherical, and lumbrical) of one hand over two-day training sessions in 15 subjects in a public dataset. We have developed Two ensemble methods consisting of (anomaly detection + fully connected neural network) and (anomaly detection + convolutional neural network) to classify grasp motion and have achieved more than 80% classification accuracy in 3 subjects and an average accuracy of 57% among the full cohort. Our resu..

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