Lan-Xin Lin

Bio: Lan-Xin Lin is an academic researcher. The author has contributed to research in topics: Support vector machine & Artificial neural network. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

A Novel Approach for Analyzing EEG Signal Based on SVM

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.

Novel joint algorithm based on EEG in complex scenarios.

Abstract: At present, in the field of electroencephalogram (EEG) signal recognition, the classification and recognition in complex scenarios with more categories of EEG signals have gained more attention. Ba...

EEG signal analysis and processing based prediction of epileptic seizures and research progress

Abstract: Epilepsy is a common intrinsic brain pathology. Predicting an impending epileptic seizure has obvious clinical importance.This paper studies the history of prediction of epileptic seizures based on EEG signal and summaries the applications of time-domain, frequency-domain, non-linear dynamics and intelligent analysis technology on seizure prediction. [

EEG Characteristics Extraction and Classification Based on R-CSP and PSO-SVM

Abstract: In order to improve the EEG recognition accuracy and real-time performance, 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 in this paper. Firstly, the Regularization Common Spatial Pattern (R-CSP) was used for EEG feature extraction. Secondly, the penalty factor and the kernel function were optimized by the proposed PSO algorithm. Finally, the constructed SVM classifiers were trained and tested by the two class EEG data of right foot and right hand movements. The experimental results show that the recognition rate for EEG classification of the PSO-SVM is average 2.2% higher than the non-parameter-optimized SVM classifier, and it is significantly higher than the traditional LDA classifier, which proves the feasibility and higher accuracy of the algorithm.