Elucidation of Visual Motor Imagery EEG Data Using Feature Streamlining with Machine Learning - Tiwari, Virendra Kumar; Singh, Priyanka; Keer, Vinita; Saxena, Gaurav Kishor; Dubey, Sandeep (Mathematics and Computer Science: Contemporary Developments Vol. 9, 2024)
Visual motor imagery (VMI) is a cognitive process where individuals visualize performing a motor task without actual movement. Electroencephalography (EEG) data is often used to analyze brain activity associated with VMI. However, the high dimensionality and complexity of EEG data pose challenges for effective analysis. This study focuses on streamlining EEG features using advanced machine-learning techniques to enhance the accuracy and efficiency of VMI classification. We applied feature selection and extraction methods to identify the most relevant features from EEG signals, followed by the implementation of various machine learning algorithms to classify VMI tasks. Our results demonstrate that feature streamlining significantly reduces computational load while maintaining high classification accuracy. The streamlined features, combined with optimized machine learning models, provide a robust framework for interpreting VMI-related EEG data, paving the way for improved brain-computer interface (BCI) systems. This approach has potential applications in neurorehabilitation, cognitive training, and enhancing user interaction in BCI technologies. Classifying motor imagery in the EEG is crucial for identifying serious illnesses. The diversity of the motor imagery EEG data hampered the rate of categorization. The motor imagery EEG classification rate is increased using the feature optimization procedure. A deep neural network-based classifier for motor imagery EEG categorization was suggested in this article. The design deep neural network is a three-layer neural network model that incorporates the teacher learning-based optimization and feature optimization technique. The EEG data's noise and artefacts are reduced using a teacher learning-based optimization technique, which also enhances the input vectors for DNN. The suggested approach has been tested on datasets from the third and fourth BCI competitions and has been simulated in MATLAB settings. The evaluation's findings indicate that the suggested technique is quite effective at compressing the current motor.