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We present a novel method for idle and work states classification in brain computer interface (BCI) based on steady-state visual evoked potentials (SSVEP). Canonical correlation analysis (CCA) and maximum contrast combination (MCC) are used to extract features of electroencephalogram (EEG) signals. The correlation coefficients from CCA and SNR from MCC were classified by a linear classifier. Then...
A motor imagery based brain-computer interface (BCI) translates the subject's motor intention into a control signal. For this BCI system, most algorithms are based on power changes of mu and beta rhythms. In this paper, we employ the measurement of phase synchrony to investigate the activities of the supplementary motor area (SMA) and primary motor area (M1) during left/right hand movement imagery...
A motor imagery based brain-computer interface (BCI) translates the subject's motor intention into a control signal. For this BCI system, most algorithms are based on power changes of mu and beta rhythms. In this paper, we employ the measurement of phase synchrony to investigate the activities of the supplementary motor area (SMA) and primary motor area (M1) during left/right hand movement imagery...
A brain-computer interface (BCI) based on motor imagery (MI) translates the subject's motor intention into a control signal through classifying the electroencephalogram (EEG) patterns of different imagination tasks, e.g. hand and foot movements. Characteristic EEG spatial patterns make MI tasks substantially discriminable. Multi-channel EEGs are usually necessary for spatial pattern identification...
A new algorithm based on hidden Markov models (HMM) to discriminate single trial electroencephalogram (EEG) between two conditions of finger movement task is proposed. Firstly, multi-channel EEG signals of single trial are filtered in both frequency and spatial domains. The pass bands of the two filters in frequency domain are 0~3 Hz and 8~30 Hz respectively, and the spatial filters are designed by...
EEG-based brain computer interface (BCI) provides a completely new communication channel between human brain and computer. Classification of EEG signals is a difficult task, especially when the classification has to be preformed on a single-trial EEG to continuously control a device. Event related desynchronization (ERD) has proven to be induced on the contralateral sensorimotor area during imagination...
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