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

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 lumbrica...

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Bibliographic Details
Published inThe ... International Winter Conference on Brain-Computer Interface pp. 1 - 6
Main Authors Partovi, Andy, Hosseini, Seyed Mehrshad, Soleymani, Milad, Liaghat, Kiana, Ziaee, Soroush, Fard, Erfan Habibi Panah, Vajdi, Sahand Sadeghpour, Goodarzy, Farhad
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.02.2021
Subjects
Anomaly detection
BCI
Biological neural networks
Brain modeling
CNN
component
Deep learning
EEG decoding
Electroencephalography
Neural network
Scalp
Task analysis
Training data
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Summary: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.
ISSN:2572-7672
DOI:10.1109/BCI51272.2021.9385295