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

Abstract

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.

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning

“…The deep ConvNet is basically the same as shallow ConvNet but with a deep CNN instead of an ANN for classification, and they have been successfully applied for many brain state classification tasks such as depression recognition ( Li et al, 2019 ), drowsiness recognition ( Chen et al, 2021 ), and eye states classification ( Han et al, 2022 ). Therefore, in this paper, we use deep and shallow ConvNets as the deep learning approaches and compare them with SVM classification ( Shen et al, 2010 ; Dai et al, 2013 , 2017 ) using CSP ( Koles et al, 1990 ) and FBCSP as feature extraction methods.…”