  The Brain-Computer Interface system is a profoundly developing area of
experimentation for Motor activities which plays vital role in decoding
cognitive activities. Classification of Cognitive-Motor Imagery activities from
EEG signals is a critical task. Hence proposed a unique algorithm for
classifying left/right-hand movements by utilizing Multi-layer Perceptron
Neural Network. Handcrafted statistical Time domain and Power spectral density
frequency domain features were extracted and obtained a combined accuracy of
96.02%. Results were compared with the deep learning framework. In addition to
accuracy, Precision, F1-Score, and recall was considered as the performance
metrics. The intervention of unwanted signals contaminates the EEG signals
which influence the performance of the algorithm. Therefore, a novel approach
was approached to remove the artifacts using Independent Components Analysis
which boosted the performance. Following the selection of appropriate feature
vectors that provided acceptable accuracy. The same method was used on all nine
subjects. As a result, intra-subject accuracy was obtained for 9 subjects
94.72%. The results show that the proposed approach would be useful to classify
the upper limb movements accurately.
