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Abstract
Self-paced Brain Computer Interfaces (BCIs) is a preponderant trend nowadays for the most natural human-machine interaction. There is an increasing need to deal with critical disorders that involve death of neurons namely Amyotrophic Lateral Sclerosis (ALS), or brainstem stroke. Translating motor imagery activities of the brain can help any patient who suffers from these severe conditions. The detection of motor imagery is followed by a classification process. Results can be sent to a computer program or a wheel chair or a mind controlled prosthesis. Translating motor imagery into a decision requires a number of parameters to be optimized. These parameters were found varying from subject to another. Reliable features have to be extracted that describe the Motor Imagery (MI) related activity properly