Document Type : Review Article

Author

• Mai Mabrouk

MUST University

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 motor 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

Keywords

• Biomedical engineering
• EEG- Electroencephalography
• Biomedical Engineering Dept
• en
• BCI- Brain Computer Interface
• MUST University
• K- Means clustering
• Neural network
• PCA
• Wavelet