Proceedings ArticleDOI
Classify Motor Imagery by a Novel CNN with Data Augmentation
Huang Weijian,Li Wang,Zhenxiong Yan,Yanjun Liu +3 more
- Vol. 2020, pp 192-195
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TLDR
This paper proposes a mixed-scale CNN architecture, and a data augmentation method is used to classify the EEG of motor imagery, which effectively solves the problems existing in the existing CNN-based motor imagery classification methods, and it improves the classification accuracy.Abstract:
The brain-computer interface (BCI) based on electroencephalography (EEG) converts the subject's intentions into control signals. For the BCI, the study of motor imagery has been widely used. In recent years, a classification method based on a convolutional neural network (CNNs) has been proposed. However, most of the existing methods use a single convolution scale on CNN, and another problem that affects the results is limited training data. To solve these problems, we propose a mixed-scale CNN architecture, and a data augmentation method is used to classify the EEG of motor imagery. After classifying the BCI competition IV dataset 2b, the average classification accuracy is 81.52%. Compared with the existing methods, our method has a better classification result. This method effectively solves the problems existing in the existing CNN-based motor imagery classification methods, and it improves the classification accuracy.read more
Citations
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Journal ArticleDOI
A classification method for EEG motor imagery signals based on parallel convolutional neural network
TL;DR: In this paper, a parallel convolutional neural network (PCNN) architecture is proposed to classify motor imagery signals, which achieves 83.0 ± 3.4% on BCI Competition IV dataset 2b, which outperforms the compared methods at least 5.2%.
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A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain-Computer Interfaces.
TL;DR: In this article, a review of DL-based short/zero-calibration methods for BCI is presented, which includes data augmentation (DA), increasing the number of training samples without acquiring additional data, and transfer learning (TL), taking advantage of representative knowledge obtained from one dataset to address the data insufficiency problem in other datasets.
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An Automatic Method for Epileptic Seizure Detection Based on Deep Metric Learning
TL;DR: In this paper , two one-dimensional convolutional embedding modules are proposed as a deep feature extractor, for single-channel and multichannel EEG signals respectively, and a deep metric learning model is detailed along with a stage-wise training strategy.
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Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface.
TL;DR: In this article, a cross-subject EEG classification framework with a generative adversarial networks (GANs) based method named common spatial GAN (CS-GAN), which used adversarial training between a generator and a discriminator to obtain high-quality data for augmentation.
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Motor Imagery EEG Recognition Based on Weight-Sharing CNN-LSTM Network
TL;DR: In this paper , a weighted shared two-dimensional convolutional CNN-LSTM network is proposed, which shares convolution kernels for feature maps of different channels of different EEG channels.
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