Key result
EEG-based thought identification using deep neural networks achieves ~97% maximum classification accuracy.
Why the study?
Electroencephalogram-based message depiction assists paralysed individuals with neurological disorders to communicate with the outside world in real time.
A novel EEG-based thought identification system using Empirical Mode Decomposition and deep neural networks achieved high classification accuracy, demonstrating potential as a communication interface for patients with severe neurological disorders.
Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based message depiction is gaining importance as it assists the paralysed person to communicate. In the proposed method a novel approach of feature extraction is done by Empirical Mode Decomposition on non- stationary & non-linear kind of EEG signal. EMD helps in the effective time frequency analysis by disintegrating the EEG signal in the form of six Intrinsic Mode Functions with help of the frequency components. In all nine features are extracted from the decomposed IMFs so as to predict the states or messages of the patient. The above computed features are then served to the Deep Neural Network to perform the classification. The performance of suggested method is studied through applying it to the acquired database generated by the designed hardware as well as also in real time message depiction. The maximum classification accuracy 97% for the acquired database & 85% in real time are obtained respectively by comparative analysis. The command messages generated from the proposed system helps the person suffering from neurological disorder to establish the communication link with the outside world in an efficient way. Thus, the proposed novel method shows better performance in real time message depiction purpose as related to other existing methods.
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Agrawal et al. (2024) studied Neurological disorders (n=8). EEG-based thought identification system using EMD and DNN was evaluated on Classification accuracy. The proposed EEG-based thought identification system using Empirical Mode Decomposition and a Deep Neural Network achieved a maximum classification accuracy of 97% on the acquired database and 85% in real-time.
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