FPGA based filters for EEG pre-processing

The EEG pre-processing steps involve removing noise and artifacts from EEG. The noise from the main source like electro-oculogram, electrocardiogram, electromyogram and other sources should be eliminated to increase accuracy in classification. As these artifacts may be misinterpreted as originating from the brain, there is a need to minimize or remove them from recorded EEG signals. The artifacts are undesirable potentials of non-cerebral origin and eye blinking that contaminate the EEG signal. EEG artifacts originate from two sources namely, physiological and technical. Technical artifacts are mainly due to equipment malfunction; result from poor electrode contact or line interference. Offset, filter settings, or incorrect gain of the amplifier will cause distortion clipping or saturation of the recorded signals. Technical artifacts can be avoided through consistent monitoring, meticulous inspection of equipment and proper apparatus setup. Physiological artifacts arise from a variety of body activities that are either due to movements, skin resistance fluctuations or other bioelectrical potentials. Proper filters need to be designed to filter these artifacts. Moving average filter and Median filters are easy to implement and these filters acts as best pre-processing stage for noise removal. In this paper filters such as Moving average and Median filter are implemented in FPGA (Virtex-5) and compared in terms of area, power and delay. Though Moving average is fast when compared to Median filter. Median filter is the best for pre-processing since it occupies less area and power.