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A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system

Scientific reports, ISSN: 2045-2322, Vol: 15, Issue: 1, Page: 1360-null
2025
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Article Description

The Hybrid-Brain Computer Interface (BCI) has shown improved performance, especially in classifying multi-class data. Two non-invasive BCI modules are combined to achieve an improved classification which are Electroencephalogram (EEG) and functional Near Infra-red Spectroscopy (fNIRS). Classifying contralateral and ipsilateral motor movements is found challenging among the other mental activity signals. The current work focuses on the performance of deep learning methods like - Convolutional Neural Networks (CNN) and Bidirectional Long-Short term memory (Bi-LSTM) in classifying a four-class motor execution of Right Hand, Left Hand, Right Arm and Left Arm taken from the CORE dataset. The model performance was evaluated using metrics such as Accuracy, F1 - score, Precision, Recall, AUC and ROC curve. The CNN and Hybrid CNN models have resulted in 98.3% and 99% accuracy respectively.

Bibliographic Details

R. Shelishiyah; Deepa Beeta Thiyam; M. Jehosheba Margaret; N. M. Masoodhu Banu

Springer Science and Business Media LLC

Multidisciplinary

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