An Adaptive EEG Signal Filter Based on Discrete Wavelet Transform and Least Mean Square

Electroencephalogram (EEG) contains a lot of physiological characteristics and disease information, and the existence of various artifacts is a major obstacle in EEG analysis. Artifacts are mostly generated by the unconscious eye activity and muscle activity of the human body and the most serious effect is the strong electrooculogram (EOG) generated by eye movement. The existing removal of EOG requires various pretreatment and calibration procedures, which are inconvenient and time-consuming. Therefore, for complex EOG artifacts, we proposed a method combining the discrete wavelet transform (DWT) and the least mean square (LMS) adaptive filter. The EOG component decomposed by DWT is used as the reference signal of the adaptive filter to estimate EOG artifacts. The experimental results show that the average mean square error (MSE) of the subjects is reduced by 79.58% and the average signal-to-noise ratio (SNR) is increased by 143.6S%. We also implement the proposed filtering algorithm in hardware and finally form a mature EEG filtering system for the brain-computer interface.