Moreover, BCI research is also being carried out to detect in advance that a person is going to suffer from a seizure attack so that they can be informed in order to prevent accident or serious injuries 10, 11, 12. For example, BCI controlled wheel-chairs 2, 8, 9 are being developed to enable people with disabilities to maneuver around the house and perform basic tasks. īrain-computer interface (BCI) has become a hot topic of research as it is increasingly being used in gaming applications 1 and in stroke rehabilitation 2, 3, 4, 5, 6, 7 for translating the brain signals of the imagined task into intended movement of the limb that has been paralyzed. The improvements in the average misclassification rates are 3.09% and 2.07% for BCI Competition IV Dataset I and GigaDB dataset, respectively. OPTICAL showed significant improvement in the ability to accurately classify left and right-hand MI tasks on two publically available datasets. The regression based feature further boosts the performance of the proposed OPTICAL predictor. The features in the reduced dimensional plane after performing LDA are used as input to the support vector machine (SVM) classifier together with the regression based feature obtained from the LSTM network. On the other hand, linear discriminant analysis (LDA) is used to reduce the dimensionality of the CSP variance based features. Moreover, instead of using LSTM directly for classification, we use regression based output of the LSTM network as one of the features for classification. A sliding window approach is proposed to obtain the time-series input from the spatially filtered data, which becomes input to the LSTM network. We propose a new predictor, OPTICAL, that uses a combination of common spatial pattern (CSP) and long short-term memory (LSTM) network for obtaining improved MI EEG signal classification. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-time having high classification accuracy between different MI tasks. However, the ability to classify brain waves and its implementation in real-time is still limited. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable.
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