Research on the classification of EEG signals based on LM algorithm
Year: 2018
Type: article
Abstract: In order to realize the accurate classification of EEG signals based on motion imagination, the Levenberg Marquardt(LM) algorithm is proposed to replace the BP neural network classifier to improve the classification recognition rate. Based on the BCI2008 competition laboratory paradigm, we used Emotive Epoc+ to collect four kinds of motor imagery EEG signals. After filtering the signal to dryness, the main component analysis is used to extract the characteristic values of the collected signals, and then the LM algorithm and the BP neural network are used for classification and recognition respectively. Finally, the serial communication interface is designed based on the MATLAB GUI, and the feasibility of the algorithm is verified with the Arduino intelligent car link. The results show that the average training error is 5.630 6×10-7, the classification accuracy is 86%, and the BP algorithm is 0.001 4 and 56% respectively. Compared with the LM algorithm, the classification effect is good. During the verification process, the intelligent car operation is consistent with the algorithm identification direction, and runs well. This method is practical and feasible, which lays the foundation for further developing brain computer interface.
Authors: Zhao Dongdong, Song Hongjun, Xu Yuhu, Cui Dongyun, Wang Shuai, Ding Xiaoling
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4403588485