Key result
The proposed deep convolutional neural network with STFT and CMWT preprocessing achieved a classification accuracy of 0.9324 and an AUC of 0.9464 for motor imagery EEG signal recognition.
Why the study?
Motor imagery EEG feature extraction and classification face low accuracy and efficiency due to low signal-to-noise ratio and large individual differences.
Does a deep convolution network-based recognition method improve the accuracy and precision of motor imagery EEG signal recognition?
Does a deep convolution network-based recognition method improve the accuracy and precision of motor imagery EEG signal recognition?
Absolute Event Rate: 0.9324% vs 0.9135%
A deep convolution neural network using STFT and CMWT preprocessing achieves high accuracy (0.9324) and precision (0.9653) in recognizing motor imagery EEG signals.
May support CNN-based motor imagery EEG decoding in brain-computer interfaces; leaves open clinical translation pending validation.
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.
No takes yet. Share an insight, caveat, or question.
Xiao et al. (2021) studied Motor imagery (EEG signal recognition) (n=8,600). Deep Convolutional Neural Network with STFT and CMWT preprocessing vs. Existing CNN methods (Lawhern et al., Li, Wei et al.) was evaluated on Classification accuracy. The proposed deep convolutional neural network with STFT and CMWT preprocessing achieved a classification accuracy of 0.9324 and an AUC of 0.9464 for motor imagery EEG signal recognition.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: