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COMBINING TEMPORAL AND FREQUENCY BASED
PREDICTION FOR EEG SIGNALS
Padma Polash Paul, Howard Leung
Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong
David A. Peterson
1
, Terrence J. Sejnowski
1,2
, Howard Poizner
1
Institute for Neural Computation, University of California, San Diego, U.S.A.
1
The Computational Neurobiology Lab, The Salk Institute, La Jolla, U.S.A.
2
Keywords: Electroencephalography, Temporal based Prediction, Frequency based Prediction, Artificial Neural
Network.
Abstract: This paper presents a novel approach for electroencephalogram (EEG) signal prediction. It combines
temporal and frequency based prediction to achieve a good final prediction result. Artificial neural networks
are used as the predictive model for signals both in the temporal and frequency domain. In frequency based
prediction, the amplitude and the phase of the frequency response are predicted separately. Experiments
were conducted on the prediction of EEG data recorded from 13 subjects. Eight performance measures were
used to evaluate the performance of our proposed method. Experiment results show that the proposed
combined prediction method gives the overall best performance compared with the temporal based
prediction alone and the frequency based prediction alone.
1 INTRODUCTION
Time series prediction problem has wide range of
research interest due to its diverse potential
applications such as electroencephalogram (EEG)
signal analysis, financial data prediction, and
environmental monitoring. To measure brain
activity, non-invasive EEG is one of the most
important bio-signals and many researchers are
working on EEG signal prediction.
Researchers have used time series prediction
methods to check the linearity of EEG signals. They
found that nonlinear properties are present in EEG
signals and that some data are not predictable using
linear stochastic system (Robert A. Stêpieñ, 2002). It
was found that EEG recordings from subjects with
schizophrenia contain some degree of determinism
(low order chaotic), but are not completely
deterministic and contain properties of nonlinearity
(Ying-Jie Li, 2005). The linear EEG model cannot
perfectly describe the spontaneous EEG that
displays nonlinear phenomena (Ou Bai, 2000).
Time series prediction methods were also
applied to find the occurrence of seizures from the
EEG of epilepsy patients (Florian Mormann, 2007).
EEG time series prediction also has been used to
extract features for motor imagery task classification
in Brain Computer Interfaces (Stefan Cososchi,
2006). EEG time series prediction pre-processing
shows better performance compared with Common
Spatial Pattern (Damien Coyle, 2008). From
previous research, it is clear that EEG time series
prediction has a high impact on medical and
engineering applications.
Different algorithms for EEG signal prediction
have been proposed to enhance the predictive
model’s convergence performance in the time
domain, such as Least Square Support Vector
Machine (LS-SVM), Support Vector Regression
(SVR), Neuro-Fuzzy System, recurrent or time delay
network, and feature selection methods such as
mutual information based feature selection.
(Nicholas I., 2009) Researchers also combine
Principal Component Analysis (PCA) (Paul Cristea,
2008), Kernel PCA and SVM (Qisong Chen, 2008),
Independent Component Analysis (ICA) (Juan M.
Gorriz, 2003), for feature selection purpose in the
time domain. Future EEG signal prediction is
29
Polash Paul P., Leung H., A. Peterson D., J. Sejnowski T. and Poizner H. (2010).
COMBINING TEMPORAL AND FREQUENCY BASED PREDICTION FOR EEG SIGNALS.
In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing, pages 29-36
DOI: 10.5220/0002696800290036
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