Abstract
In recent years, the wearable devices is an essential trend of world, among them brain- computer interface(BCI) utilized electroencephalography (EEG) signal measurement be- comes more accessible and cheaper for users. 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. In this thesis, we follow the compress technique and detection mode in [1], and provide independent training for each user. At rst, we used EPOC headset to acquire EEG signals, and Emotiv o er API between EPOC device and PC. In order to detect eight direction eye movement and nd parameter for each user. In this thesis, we proposed two mode in our system, respectively training mode and de- tection mode. In training mode, we store the EEG signal that only Left/Right and Up/Down eye movement, and used Independent Component Analysis(ICA) to extract feature. Because of ICA is blind source algorithm, we used weighting selection to nd Left/Right and Up/Down weighting, and nd Max/Min value of eye movement signal. After training mode, we used Left/Right and Up/Down weighting and Max/Min value of eye movement signal to detect eight direction in real-time. We used Hidden Markov Model (HMM) to do eye movement direction classi cation, and combined Left/Right and Up/Down result to detect eight direction. Finally we can control eight direction of mouse to click monitor keyboard to key the words in the text le.