Performance Analysis of Hybrid – BCI Signals Using CNN for Motor Movement Classification
R. Shelishiyah, Deepa Beeta Thiyam · Traitement du signal · 2024
The design of a hybrid brain-computer interface (BCI) system is an upgradation of the existing BCI systems.Contemporary studies that combine two modalities for a good BCI show that electroencephalogram (EEG) and functional near infra-red spectroscopy (fNIRS) were more convenient.Using this hybrid system various multi-class classification problems have been solved with better ease.The motor imagery and motor execution tasks performed for the Right/Left Arm and Hand were taken from CORE dataset which consists of 15 male subjects.In most cases, feature extraction was done after good pre-processing and channel selections to obtain good results.Deep learning methods like Convolutional Neural Networks and Thin ICA were used for feature extraction and classification of EEG signals.CNN was used for feature extraction and classification in fNIRS with minimal preprocessing and data augmentation.Comparison of performance was done with CNN and a combination of LSTM-CNN classifiers.The proposed CNN model showed 98.3% accuracy with minimal pre-processing and with no channel selection algorithms.Evaluation metrics like Accuracy, Precision, Recall, F1 score and confusion matrix are used to evaluate the classification accuracy.This concludes that the proposed CNN model can classify contralateral and ipsilateral data with lower computational load and with good accuracy.