Electroencephalography analysis is critical for brain computer interface research. The primary goal of brain-computer interface is to establish communication between impaired people and others via brain signals. The classification of multi-level mental activities using the brain-computer interface has recently become more difficult, which affects the accuracy of the classification. However, several deep learning-based techniques have attempted to identify mental tasks using multidimensional data. The hybrid capsule attention-based convolutional bidirectional gated recurrent unit model was introduced in this study as a hybrid deep learning technique for multi-class mental task categorization. Initially, the obtained electroencephalography data is pre-processed with a digital low-pass Butterworth filter and a discrete wavelet transform to remove disturbances. The spectrally adaptive common spatial pattern is used to extract characteristics from pre-processed electroencephalography data. The retrieved features were then loaded into the suggested classification model, which was used to extract the features deeply and classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using a dung beetle optimization approach. Finally, the proposed classifier is assessed for several types of mental task classification using the provided dataset. The simulation results are compared with the existing state-of-the-art techniques in terms of accuracy, precision, recall, etc. The accuracy obtained using the proposed approach is 97.87%, which is higher than that of the other existing methods.
Keywords: Bi-GRU; Discrete wavelet transform; Electroencephalography; attention mechanism; brain–computer Interface; capsule network; convolutional neural network.