Proceedings ArticleDOI
A Novel Approach for Analyzing EEG Signal Based on SVM
Min-Fen Shen,Qiong Zhang,Lan-Xin Lin,Lisha Sun +3 more
- pp 310-313
TLDR
The local method is presented for improving the speed of the prediction of EEG signals and the experimental results show that the training of the local-SVM obtains a good behavior.Abstract:
Accurate modeling of Electroencephalography (EEG) signals is an important problem in clinical diagnosis of brain diseases. The method using support vectors machine (SVM) based on the structure risk minimization provides us an effective way of learning machine. But solving the quadratic programming problem for training SVM becomes a bottle-neck of using SVM because of the long time of SVM training. In this paper, a local-SVM method is proposed for modeling EEG signals. The local method is presented for improving the speed of the prediction of EEG signals. The experimental results show that the training of the local-SVM obtains a good behavior.read more
Citations
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Journal ArticleDOI
Novel joint algorithm based on EEG in complex scenarios.
TL;DR: A novel EEG signal-processing joint method based on the joint fast Fourier transform (FFT) and support vector machine (SVM) methods was effective in a complex scenario for multiclass EEG signal recognition.
Journal Article
EEG signal analysis and processing based prediction of epileptic seizures and research progress
TL;DR: The history of prediction of epileptic seizures based on EEG signal is studied and the applications of time-domain, frequency- domain, non-linear dynamics and intelligent analysis technology on seizure prediction are summaries.
Book ChapterDOI
EEG Characteristics Extraction and Classification Based on R-CSP and PSO-SVM
TL;DR: In this article, a classification and recognition method for optimizing the penalty factor C and kernel parameter g of Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO) algorithm is proposed.
References
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