A comparative analysis of masking empirical mode decomposition and a neural network with feed-forward and back propagation along with masking empirical mode decomposition to improve the classification performance for a reliable brain-computer interface - Jaipriya, D.; Sriharipriya, K. C. (Frontiers in Computational Neuroscience, 2022)
In general, extraction and classification are used in various fields like image processing, pattern recognition, signal processing, and so on. Extracting effective characteristics from raw electroencephalogram (EEG) signals is a crucial role of the brain-computer interface for motor imagery. Recently, there has been a great deal of focus on the motor imagery in the EEG signals, since they encode a person's intent to do an action. MI signals have been exploited by researchers to assist paralyzed people, and even move on their own with certain equipment, like wheelchairs. As a result, proper decoding is an important step required for the interconnection of the brain and the computer. EEG decoding is a challenging process because of poor SNR, complexity, and other reasons. However, choosing an appropriate method to extract the features to improve the performance of motor imagery recognition is still a research hotspot. In order to extract the features of EEG signal in the classification task, this paper proposes a Masking Empirical Mode Decomposition based Feed Forward Back Propagation Neural Network (MEMD-FFBPNN). The dataset consists of EEG signals which are first normalized using the minimax method and given as input to the MEMD, to extract the features and then given to the FFBPNN to classify the tasks. By using the confusion matrix, mean square error, and percentage accuracy, the proposed MEMD-FFBPNN method increases accuracy, which is recorded up to 99.9%. Thus, the proposed method gives better accuracy than the other conventional methods.