A Prediction Approach for Multichannel EEG Signals Modeling Using Local Wavelet SVM

• Published in: IEEE transactions on instrumentation and measurement Vol. 59; no. 5; pp. 1485 - 1492
• Main Authors: Minfen Shen, Lanxin Lin, Jialiang Chen, Chang, C.Q.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Approximation algorithms; Brain modeling; Electroencephalogram (EEG) signal; Electroencephalography; local prediction method; Optimization methods; Prediction methods; Predictive models; Signal processing; Signal processing algorithms; Spatiotemporal phenomena; support vector machine (SVM); Support vector machines; wavelet kernel
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2010.2040905

Accurate modeling of the multichannel electroencephalogram (EEG) signal is an important issue in clinical practice. In this paper, we propose a new local spatiotemporal prediction method based on support vector machines (SVMs). Combining with the local prediction method, the sequential minimal optimization (SMO) training algorithm, and the wavelet kernel function, a local SMO-wavelet SVM (WSVM) prediction model is developed to enhance the efficiency, effectiveness, and universal approximation capability of the prediction model. Both the spatiotemporal modeling from the measured time series and the details of the nonlinear modeling procedures are discussed. Simulations and experimental results with real EEG signals show that the proposed method is suitable for real signal processing and is effective in modeling the local spatiotemporal dynamics. This method greatly increases the computational speed and more effectively captures the local information of the signal.