Chen Ronghua, Li Dongmei and Zhang Milin, Tsinghua University, China

This paper presents the real-time implementation on FPGA of the wavelet-based denoising technique to remove the ocular artifact from the signal-channel EEG signal. The advantage of this method over conventional methods is that there is no need for the recording of the electrooculogram (EOG) signal itself. This approach papers both for eye blinks and eye movements. Discrete Wavelet Transform (DWT) is selected end the hard-thresholding is applied to the wavelet coefficients using the Statistical Threshold (ST) estimated in interested bands. This real-time architecture presents two characteristics: 1) quantization of the filter coefficients and the elimination of the multiplier to reduce the hard cost, and 2) symmetrical extension of the signal boundary to full reconstruction while the data volume is invariable. Experimental results show that proposed architecture efficiently removes the ocular artifact from EEG signal.

Wavelet transform, EEG, ocular artefact, hard-thresholding, denoising