Abstract
Due to the rapid evolvement of current medical equipments, electroencephalography (EEG) signal measurement becomes more accessible and cheaper for users. Thus, the development of brain-computer interface (BCI) becomes a hot research topic in recent years. BCI is commonly used for medical usage or entertainment applications. For EEG signal acquisition, wireless portable devices are preferred due to their convenience. In the wireless scenario, power and bandwidth become important issues. Therefore, in this thesis we propose the compressive sensing aided EEG classification system to reduce power consumption and communications bandwidth for portable devices. We apply proper preprocessing for EEG signals to acquire features, and then use hidden Markov model (HMM) to do eye movement direction classification. In addition, we added compressive sensing in the front end to improve transmission efficiency and merge the pre-processing operation into the compression algorithm to reduce the computational complexity. We also do system optimization to achieve better detection performance. A special sensing matrix is developed to achieve better detection performance and lower computation cost. Since the EEG sampling rate is not high, we use a processor-based hardware architecture to design the reconstruction algorithm for compressive sensing to save hardware cost. The hardware blocks are verified on an FPGA board and incorporated with a PC to form a real time BCI system. With the proposed system, users with a EEG headset can control the arrow keys on the computer by moving their eyeballs toward directions of left/right and up/down.