Authors:
Padma Polash Paul
1
;
Howard Leung
1
;
David A. Peterson
2
;
Terrence J. Sejnowski
3
and
Howard Poizner
2
Affiliations:
1
City University of Hong Kong, Hong Kong
;
2
Institute for Neural Computation, University of California, United States
;
3
Institute for Neural Computation, University of California; The Computational Neurobiology Lab, The Salk Institute, United States
Keyword(s):
Electroencephalography, Temporal based Prediction, Frequency based Prediction, Artificial Neural Network.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Informatics in Control, Automation and Robotics
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
;
Signal Processing, Sensors, Systems Modeling and Control
;
Time and Frequency Response
;
Time-Frequency Analysis
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.