Two-phase classification: ANN and A-SVM classifiers on motor imagery BCI

Brain-Computer Interfaces (BCIs) based on Electroencephalograms (EEG) monitor mental activity with the ultimate objective of allowing people to communicate with computers only via their thoughts. Users must create precise cerebral activity patterns that the system uses as control signals to do this. A common activity used to elicit such signals is Motor Imagery (MI), in which certain signals are created in the sensorimotor cortex while imagining the movements. The three phases of the traditional EEG-BCI processing pipeline are preprocessing, feature extraction, and classification. We provide categorization advances and track performance gains in 4-class MI-based BCIs. In this study, 4-class MI events are produced via an illusory elevation of the left hand, right hand, feet, and tongue. Finally, a two-phase classification technique is provided with ANN classifiers being used in the first phase to discriminate between different pair-wise MI tasks. Secondly, an adaptive SVM classifier is used to assess the user's end task based on the weighted outputs of the classifiers. An adaptive classifier is one technique to maintain consistency in performance, reduce training time, and eliminate non-stationaries, all of which are required for efficient BCI performance. The suggested approach outperformed conventional two-stage classification algorithms on MI data, according to experimental findings. The average classification accuracy of this technique is 96% for datasets BCI competition IV 2a. This is a 4% improvement over the comparison approach.

Automated summary

This article introduces Brain-Computer Interfaces based on Electroencephalograms and some improvements in classification and performance. Experimental results show that the proposed method performs better on MI data.