A Deep Learning Algorithm for Classifying Grasp Motions using Multi-session EEG Recordings - Partovi, Andy; Hosseini, Seyed Mehrshad; Soleymani, Milad; Liaghat, Kiana; Ziaee, Soroush; Fard, Erfan Habibi Panah; Vajdi, Sahand Sadeghpour; Goodarzy, Farhad (The ... International Winter Conference on Brain-Computer Interface, 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 results confirm the possibility of utilizing neural networks to decode motor movement intentions from scalp EEG in a complicated task.