Unorthodox Approach to Classify EEG Signals for Upper Limb Prosthesis Application

Brain computer interface (BCI) is targeted for decoding the EEG (Electroencephalogram) signals that the human brain generates which are beneficial for the paraplegic patients. These EEG signals are slow cortical potentials that are directly recorded from scalp thus cortical neuronal activity is explored via non-invasive electrodes. These EEG signals are then further utilized for performing various operations which the paraplegic patients are unable to perform. This research article presents a novel architecture of classification of four finger movements (thumb movement, index finger movement, middle and index finger combined movement and fist movement) of the right hand on the basis of EEG (Electroencephalogram) data of the movements. The presented architecture utilizes Guided filter for reduction of noise (artefacts) from EEG signals alpha and beta band (8-30 Hz). As this band contains the maximum information of movement in terms of motor imagery. Rank Transform is employed as feature extraction approach for further enhancement of processed EEG signals. Two stage Logistic Regression classifier is finally employed for classification of movements using processed EEG signals. The experimental results demonstrate the accuracy, robustness and computational complexity of the proposed approach and have significant improvement as compared with recent architectures for EEG classification.