Abstract
A novel method for the discrimination between discrete states of brain consciousness is proposed, achieved through examination of nonlinear features within the electroencephalogram (EEG). To allow for real time modes of operation, a collaborative adaptive filtering architecture, using a convex combination of adaptive filters is implemented. The evolution of the mixing parameter within this structure is then used as an indication of the predominant nature of the EEG recordings. Simulations based upon a number of different filter combinations illustrate the suitability of this approach to differentiate between the coma and quasi-brain-death states based upon fundamental signal characteristics.
| Original language | English |
|---|---|
| Pages (from-to) | 36-43 |
| Number of pages | 8 |
| Journal | Neurocomputing |
| Volume | 76 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 15 Jan 2012 |
| Externally published | Yes |
Bibliographical note
Funding Information:We have proposed nonlinearity analysis of EEG signals as a potential tool for brain consciousness state identification and illustrated how the hybrid filter can be used for this purpose. By monitoring the evolution of the mixing parameter within a hybrid filter, it has been possible to gain insight into the fundamental signal nature, including nonlinearity, sparsity and complex circularity. Simulation results show great potential of the methodology and its application in signal nonlinearity tracking. Thus, this technique provides a robust feature to determine brain activities and has great potential in the development of a noninvasive test for QBD. Ling Li received the B.Eng. (Honors) degree from the Department of Electronic Information Engineering, Tianjin University, China in 2003, M.Sc. degree from the Department of Electrical and Electronic Engineering, the University of Liverpool in 2004. She then worked in a leading electronic testing company Forwessun International Ltd. as application engineer and was later promoted to development manager. Having received Ph.D. in 2011 at Department of Electrical and Electronic Engineering, Imperial College London, she works as a research associate at Department of Computing, Imperial College London, UK. She is now a lecturer at School of Computing, University of Kent, UK. Yili Xia received the B.Eng. (Honors) degree in Information Engineering from Southeast University, China, and M.Sc. (Distinction) degree in Communications and Signal Processing from Imperial College London, UK. He is currently pursuing his Ph.D. at the Department of Electronic and Electrical Engineering, Imperial College London. His research interests include nonlinear signal processing, adaptive filters, and complex-valued analysis. Beth Jelfs received the M.Eng. degree in Electronic & Software Engineering from the University of Leicester, UK, where she received the British Computer Society prize for top graduate 2005. In 2000 she worked as a test technician for Marconi Optical Components. She completed her Ph.D. in Electrical and Electronic Engineering at Imperial College London, UK, in 2010. Her current research interests include adaptive signal processing and signal modality characterisation. Jianting Cao received the M. Eng. and Ph.D. degrees from the Graduate School of Science and Technology, Chiba University, Japan, in 1993 and 1996, respectively. From 1983 to 1988, he worked as a Researcher at the Institute of Technology and Equipment in the Ministry of Geological and Mineral in China. From 1996 to 1998, he worked as a Researcher at the Brain Science Institute, RIKEN (The Institute of Physical and Chemical Research) in Japan. From 1998 to 2002, he worked as an Assistant, and a Lecturer at the Sophia University in Japan. From 2002 to 2007, he worked as an Associate Professor at the Saitama Institute of Technology in Japan. He is currently working as a Professor at the Department of Robotics, Saitama Institute of Technology, and a Visiting Research Scientist at the Brain Science Institute, RIKEN in Japan. He received the Best Paper Award from the Telecommunications Advancement Foundation (Japan) in 1996, received the Best Paper Award from the IEEE Circuits and System Society in 2005, and received the Best Paper Award from the Signal Processing Institute (Japan) in 2007. His research interests include blind signal processing, biomedical signal processing, neural networks and learning algorithms. Dr. Cao is a member of IEEE and IEICE (Japan). Danilo P. Mandic is a Professor in Signal Processing at Imperial College London. He has been working in the area of nonlinear adaptive signal processing and nonlinear dynamics. His publication record includes two research monographs (Recurrent Neural Networks for Prediction, and Complex Valued Nonlinear Adaptive Filters) with Wiley, an edited book on Signal Processing for Information Fusion (Springer 2007) and more than 200 publications in Signal and Image Processing. He has been a Member of the IEEE Technical Committee on Machine Learning for Signal Processing, Associate Editor for the IEEE Transactions on Circuits and Systems II, IEEE Transactions on Signal Processing, IEEE Transactions on Neural Networks and International Journal of Mathematical Modelling and Algorithms. Dr. Mandic has produced award winning papers and products resulting from his collaboration with Industry. He is a Senior Member of the IEEE and Member of the London Mathematical Society. Dr. Mandic has been a Guest Professor in KU Leuven Belgium, TUAT Tokyo, Japan and Westminster University UK, and Frontier Researcher in RIKEN Japan.
Keywords
- Collaborative adaptive filtering
- Coma
- EEG
- Quasi-brain-death (QBD)
- Signal nonlinearity
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence