Round Cosine Transform Based Feature Extraction of Motor Imagery EEG Signals

2018 ◽  
pp. 511-515 ◽  
Author(s):  
R. B. Braga ◽  
C. D. Lopes ◽  
T. Becker
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Eeg Signals ◽  
Cosine Transform

scholarly journals Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network

2021 ◽  
Vol 15 ◽  
Author(s):  
Xiongliang Xiao ◽  
Yuee Fang
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Signal To Noise Ratio ◽  
Large Individual ◽  
Eeg Signal ◽  
Signal Recognition ◽  
Eeg Signals ◽  
Time Frequency ◽  
Morlet Wavelet Transform

Brain computer interaction (BCI) based on EEG can help patients with limb dyskinesia to carry out daily life and rehabilitation training. However, due to the low signal-to-noise ratio and large individual differences, EEG feature extraction and classification have the problems of low accuracy and efficiency. To solve this problem, this paper proposes a recognition method of motor imagery EEG signal based on deep convolution network. This method firstly aims at the problem of low quality of EEG signal characteristic data, and uses short-time Fourier transform (STFT) and continuous Morlet wavelet transform (CMWT) to preprocess the collected experimental data sets based on time series characteristics. So as to obtain EEG signals that are distinct and have time-frequency characteristics. And based on the improved CNN network model to efficiently recognize EEG signals, to achieve high-quality EEG feature extraction and classification. Further improve the quality of EEG signal feature acquisition, and ensure the high accuracy and precision of EEG signal recognition. Finally, the proposed method is validated based on the BCI competiton dataset and laboratory measured data. Experimental results show that the accuracy of this method for EEG signal recognition is 0.9324, the precision is 0.9653, and the AUC is 0.9464. It shows good practicality and applicability.

Research on the Physical Mechanism of Motor Imagery

2011 ◽  
Vol 66-68 ◽  
pp. 279-283
Author(s):  
Zhen Dong Mu ◽  
Jin Li Wang
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Time Domain ◽  
Brain Mapping ◽  
Physical Mechanism ◽  
Eeg Analysis ◽  
Imaginary Frequency ◽  
Eeg Signals ◽  
Mapping Analysis ◽  
Left And Right

The motor imagery to be widely used in BCI systems, the traditional focus on EEG analysis in feature extraction and classification, this paper of EEG from the left and right imaginary frequency domain, time domain and brain mapping analysis on the EEG, to analyze the characteristics of EEG signals about imagination.

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