FEATURE EXTRACTION OF MOTOR IMAGERY EEG SIGNALS BASED ON MULTI-SCALE RECURRENCE PLOT AND SDA

2021 ◽  
Vol 36 (0) ◽  
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Recurrence Plot ◽  
Eeg Signals ◽  
Multi Scale

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.

scholarly journals EEG-Based 3D Visual Fatigue Evaluation Using CNN

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1208 ◽  
Author(s):  
Kang Yue ◽  
Danli Wang
Keyword(s):  
Feature Extraction ◽  
Temporal Structure ◽  
Visual Fatigue ◽  
Eeg Signals ◽  
Visual Evaluation ◽  
Time Frequency ◽  
Multi Scale ◽  
Temporal Features ◽  
Depth Analysis ◽  
Fatigue Evaluation

Visual fatigue evaluation plays an important role in applications such as virtual reality since the visual fatigue symptoms always affect the user experience seriously. Existing visual evaluation methods require hand-crafted features for classification, and conduct feature extraction and classification in a separated manner. In this paper, we conduct a designed experiment to collect electroencephalogram (EEG) signals of various visual fatigue levels, and present a multi-scale convolutional neural network (CNN) architecture named MorletInceptionNet to detect visual fatigue using EEG as input, which exploits the spatial-temporal structure of multichannel EEG signals. Our MorletInceptionNet adopts a joint space-time-frequency features extraction scheme in which Morlet wavelet-like kernels are used for time-frequency raw feature extraction and inception architecture are further used to extract multi-scale temporal features. Then, the multi-scale temporal features are concatenated and fed to the fully connected layer for visual fatigue evaluation using classification. In experiment evaluation, we compare our method with five state-of-the-art methods, and the results demonstrate that our model achieve overally the best performance better performance for two widely used evaluation metrics, i.e., classification accuracy and kappa value. Furthermore, we use input-perturbation network-prediction correlation maps to conduct in-depth analysis into the reason why the proposed method outperforms other methods. The results suggest that our model is sensitive to the perturbation of β (14–30 Hz) and γ (30–40 Hz) bands. Furthermore, their spatial patterns are of high correlation with that of the corresponding power spectral densities which are used as evaluation features traditionally. This finding provides evidence of the hypothesis that the proposed model can learn the joint time-frequency-space features to distinguish fatigue levels automatically.

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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