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.

[1]  Alan W. L. Chiu,et al.  Non-Invasive BCI for the Decoding of Intended Arm Reaching Movement in Prosthetic Limb Control , 2012 .

[2]  Bruce G. Colpitts,et al.  Efficient implementation of FIR filters based on a novel common subexpression elimination algorithm , 2010, CCECE 2010.

[3]  E. Basar.  EEG-brain dynamics: Relation between EEG and Brain evoked potentials , 1980 .

[4]  P. Hazemann,et al.  Handbook of Electroencephalography and Clinical Neurophysiology , 1975 .

[5]  W. Klimesch.  EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[6]  Arjon Turnip,et al.  P300 detection using nonlinear independent component analysis , 2013, 2013 3rd International Conference on Instrumentation Control and Automation (ICA).

[7]  D. O. Walter.  A posteriori "Wiener filtering" of average evoked responses. , 1968, Electroencephalography and clinical neurophysiology.

[8]  D. J. Doyle,et al.  Some comments on the use of Wiener filtering for the estimation of evoked potentials. , 1975, Electroencephalography and clinical neurophysiology.

[9]  Piotr J. Durka,et al.  Single evoked potential reconstruction by means of wavelet transform , 2004, Biological Cybernetics.

[10]  R.Quian Quiroga,et al.  Obtaining single stimulus evoked potentials with wavelet denoising , 2000, nlin/0006027.

[11]  O. Bertrand,et al.  Time-frequency digital filtering based on an invertible wavelet transform: an application to evoked potentials , 1994, IEEE Transactions on Biomedical Engineering.

[12]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[13]  E. Basar,et al.  Detection of P300 Waves in Single Trials by the Wavelet Transform (WT) , 1999, Brain and Language.

[14]  S P Fitzgibbon,et al.  Removal of EEG Noise and Artifact Using Blind Source Separation , 2007, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.