Motor Imagery (MI) based Brain Computer Interface (BCI) has clinical applications such as rehabilitation or communication for patients who have lost motor functions. Accurate classification of motor-imagery based electroencephalography (EEG) is important in developing such BCI applications. We propose an image-based approach to design a convolutional neural network (CNN) to classify EEG signals. I...Show More
Brain-Computer Interface (BCI) provides an alternate channel of interaction for people with severe motor disabilities. The Common Spatial Pattern (CSP) algorithm is effective in extracting discriminative features from EEG data for motor imagery-based Brain-Computer Interface (BCI). CSP yields signal from various locations for better performance. In this study, we selected a subset of EEG channels ...Show More
Non-invasive brain computer interface (BCI) has been successfully used to control cursors, helicopters and robotic arms. However, this technology is not widely adopted by people with late-stage amyotrophic lateral sclerosis (ALS) due to poor effectiveness. In this study, we attempt to assess the cognitive state of a completely locked-in ALS subject, and her ability to use motor imagery-based BCI f...Show More
