Abstract :
A brain computer interface (BCI) records the activities of the brain and classifies it into different classes. BCIs can be
used by both severely motors disabled as well as healthy people to control devices. In this work, we have concentrated
on the development and application of a novel medical technology to measure the patient’s brain activity, translated it
with intelligent software, and used the translated signals to drive patient-specific effectors. In this work, we deal with
the EEG pattern recognition approach based on brain computer interfaces. Electroencephalographic (EEG) signals
produced by the brain are used as input to our BCI system. Both offline and online BCI approaches are introduced
where the offline approach was done using Dataset IA motor imagery EEG recordings, and the online approach was
done using our own BCI system. We have described our BCI system and its efficiency for moving the hands to right
or left online. First, the measurement of the EEG and the components of a BCI system are explained. Second, the data
acquisition system we developed is described in detail. Lastly, our BCI system, including all different techniques used
for artifact removal, feature extraction, and classification is presented. Our results give an ideal solution for people
with severe neuromuscular disorders, such as Amyotrophic Lateral Sclerosis (ALS) or spinal cord injury, people who
are totally paralyzed, or “locked-in”, helping them to have a communication channel with others.
