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The noteworthy point in the advancement of Brain Computer Interface (BCI) research is not only to develop a new technology but also to adopt the easiest procedures since the expected beneficiaries are of disabled. The nature of the locked-in patients is that, they possess strong mental ability in thinking and understanding but they are extremely unable to express their views. Imagination is possible for almost all of the locked-in patients; hence a BCI which does not rely on finger movements or other muscle activity is definitely an added advantage in this arena. The objective of this paper is to identify and classify motor imaginary signals extracted from the left and right cortex of the human brain. This is realised by implementing an adaptive bandpass filter with the combination of frequency shifting and segmentation techniques. The signals are captured using Electro-Encephalogram (EEG) from the C3, C4, and Cz channels of the scalp electrodes and is pre-processed to expose the motor imaginary signals. The result of classification using a simple threshold articulates the effectiveness of our proposed technique. The best results were found in the latency range of 3 to 9 seconds of the imagination and this proves the existing neuro-science knowledge.

CLASSIFICATION OF MOTOR IMAGINARY TASKS USING ADAPTIVE RECURSIVE BANDPASS FILTER - Effective Classification for Motor Imaginary BCI