Optimization of a hardware/software coprocessing platform for EEG eyeblink detection and removal

The feasibility of implementing a real-time system for removing eyeblink artifacts from electroencephalogram (EEG) recordings utilizing a hardware/software coprocessing platform was investigated. A software based wavelet and independent component analysis (ICA) eyeblink detection and removal process was extended to enable variation in its processing parameters. Exploiting the efficiency of hardware and the reconfigurability of software, it was ported to a field programmable gate array (FPGA) development platform which was found to be capable of implementing the revised algorithm, although not in real-time. The implemented hardware and software solution was applied to a collection of both simulated and clinically acquired EEG data with known artifact and waveform characteristics to assess its speed and accuracy. Configured for optimal accuracy in terms of minimal false positives and negatives as well as maintaining the integrity of the underlying EEG, especially when encountering EEG waveform patterns with an appearance similar to eyeblink artifacts, the system was capable of processing a 10 second EEG epoch in an average of 123 seconds. Configured for efficiency, but with diminished accuracy, the system required an average of 34 seconds. Varying the ICA contrast function showed that the gaussian nonlinearity provided the best combination of reliability and accuracy, albeit with a long execution time. The cubic nonlinearity was fast, but unreliable, while the hyperbolic tangent contrast function frequently diverged. It is believed that the utilization of programmable logic with increased logic capacity and processing speed may enable this approach to achieve the objective of real-time operation.

[1]  Scott Hauck,et al.  Reconfigurable computing: a survey of systems and software , 2002, CSUR.

[2]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[3]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[4]  Alan V. Oppenheim,et al.  Discrete-time Signal Processing. Vol.2 , 2001 .

[5]  Baoqiang Wang,et al.  A New FastICA Algorithm with Symmetric Orthogonalization , 2006, 2006 International Conference on Communications, Circuits and Systems.

[6]  Frank Vahid,et al.  The Softening of Hardware , 2003, Computer.

[7]  J. Frost,et al.  Context-based automated detection of epileptogenic sharp transients in the EEG: elimination of false positives , 1989, IEEE Transactions on Biomedical Engineering.

[8]  Truong Q. Nguyen,et al.  Wavelets and filter banks , 1996 .

[9]  Christopher C. Doss,et al.  FPGA-based implementation of a robust IEEE-754 exponential unit , 2004, 12th Annual IEEE Symposium on Field-Programmable Custom Computing Machines.

[10]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[11]  T. Gasser,et al.  The transfer of EOG activity into the EEG for eyes open and closed. , 1985, Electroencephalography and clinical neurophysiology.

[12]  V. Krishnaveni,et al.  Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients , 2006, Journal of neural engineering.

[13]  Dmitriy Bekker.  Hardware and software optimization of fourier transform infrared spectrometry on hybrid-FPGAs , 2007 .

[14]  V. Samar,et al.  Wavelet Analysis of Neuroelectric Waveforms: A Conceptual Tutorial , 1999, Brain and Language.

[15]  Kuo-Kai Shyu,et al.  FPGA Implementation of FastICA based on Floating-Point Arithmetic Design for Real-Time Blind Source Separation , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[16]  Jimy Pesin,et al.  Detection and removal of eyeblink artifacts from EEG using wavelet analysis and independent component analysis , 2007 .

[17]  M. Van Gils,et al.  Signal processing in prolonged EEG recordings during intensive care , 1997, IEEE Engineering in Medicine and Biology Magazine.

[18]  F.L. Fontaine,et al.  A Reconfigurable FPGA-based 16-Channel Front-End for MRI , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[19]  Sergio Omar Martinez-Chapa,et al.  Hardware/software implementation of a discrete cosine transform algorithm using SystemC , 2005, 2005 International Conference on Reconfigurable Computing and FPGAs (ReConFig'05).

[20]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[21]  E. Waterhouse,et al.  New horizons in ambulatory electroencephalography , 2003, IEEE Engineering in Medicine and Biology Magazine.

[22]  L. Vigon,et al.  Quantitative evaluation of techniques for ocular artefact filtering of EEG waveforms , 2000 .