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Neural network based auto association and time-series prediction for biosignal processing in brain-computer interfaces

Coyle, Damien (2009) Neural network based auto association and time-series prediction for biosignal processing in brain-computer interfaces. IEEE Computational Intelligence Magazine, 4 (4). pp. 47-59. [Journal article]

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URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5294937&isnumber=5294921

DOI: 10.1109/MCI.2009.934560


Neural networks (NNs) can be deployed in many different ways in signal processing applications. This paper illustrates how neural networks are employed in a prediction based preprocessing framework, referred to as neural-time-series-prediction-preprocessing (NTSPP), in an electroencephalogram (EEG)-based brain-computer interface (BCI). NTSPP has been shown to increase feature separability by mapping the original EEG signals via time-series-prediction to a higher dimensional space. Preliminary results of a similar novel framework are also presented where, instead of using predictive NNs, auto-associative NNs are employed and features are extracted from the output of auto-associative NNs trained to specialize on EEG signals for particular brain states. The results show that this preprocessing framework referred to as auto-associative NN preprocessing (ANNP) also has the potential to improve the performance of BCIs. Both the NTSPP and ANNP are compared with and deployed in conjunction with the well know common spatial patterns (CSP) to produce a BCI system which significantly outperforms either approach operating independently and has the potential to produce good performances even with a lower number of EEG channels compared to a multichannel BCI. Multichannel BCIs normally perform better that 2-3 channel BCIs however reducing the number of EEG channels required can positively impact on the time needed to mount electrodes and minimize the obtrusiveness of the electrode montage for the user. It is also shown that NTSPP can improve the potential for employing existing BCI methods with minimal subject-specific parameter tuning to deploy the BCI autonomously. Results are presented with six different classification approaches including various statistical classifiers including Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and a Bayes classifier.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:4138
Deposited By: Prof Damien Coyle
Deposited On:04 Jan 2010 14:35
Last Modified:09 May 2016 10:49

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