Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection
In the area of brain-computer interfaces (BCI), the detection of P300 is a very important technique and has a lot of applications. Although this problem has been studied for decades, it is still a tough problem in electroencephalography (EEG) signal processing owing to its high dimension features an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/1/39 |
| _version_ | 1797543182195490816 |
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| author | Hongpeng Liao Jianwu Xu Zhuliang Yu |
| author_facet | Hongpeng Liao Jianwu Xu Zhuliang Yu |
| author_sort | Hongpeng Liao |
| collection | DOAJ |
| description | In the area of brain-computer interfaces (BCI), the detection of P300 is a very important technique and has a lot of applications. Although this problem has been studied for decades, it is still a tough problem in electroencephalography (EEG) signal processing owing to its high dimension features and low signal-to-noise ratio (SNR). Recently, neural networks, like conventional neural networks (CNN), has shown excellent performance on many applications. However, standard convolutional neural networks suffer from performance degradation on dealing with noisy data or data with too many redundant information. In this paper, we proposed a novel convolutional neural network with variational information bottleneck for P300 detection. Wiht the CNN architecture and information bottleneck, the proposed network termed P300-VIB-Net could remove the redundant information in data effectively. The experimental results on BCI competition data sets show that P300-VIB-Net achieves cutting-edge character recognition performance. Furthermore, the proposed model is capable of restricting the flow of irrelevant information adaptively in the network from perspective of information theory. The experimental results show that P300-VIB-Net is a promising tool for P300 detection. |
| first_indexed | 2024-03-10T13:41:59Z |
| format | Article |
| id | doaj.art-b6c1db6849834a43a9e0b8c5dd96450b |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T13:41:59Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-b6c1db6849834a43a9e0b8c5dd96450b2023-11-21T03:00:40ZengMDPI AGEntropy1099-43002020-12-012313910.3390/e23010039Novel Convolutional Neural Network with Variational Information Bottleneck for P300 DetectionHongpeng Liao0Jianwu Xu1Zhuliang Yu2College of Automation Science and Technology, South China University of Technology, Guangzhou 510641, ChinaGuangzhou Galaxy Thermal Energy Incorporated Company, Guangzhou 510220, ChinaCollege of Automation Science and Technology, South China University of Technology, Guangzhou 510641, ChinaIn the area of brain-computer interfaces (BCI), the detection of P300 is a very important technique and has a lot of applications. Although this problem has been studied for decades, it is still a tough problem in electroencephalography (EEG) signal processing owing to its high dimension features and low signal-to-noise ratio (SNR). Recently, neural networks, like conventional neural networks (CNN), has shown excellent performance on many applications. However, standard convolutional neural networks suffer from performance degradation on dealing with noisy data or data with too many redundant information. In this paper, we proposed a novel convolutional neural network with variational information bottleneck for P300 detection. Wiht the CNN architecture and information bottleneck, the proposed network termed P300-VIB-Net could remove the redundant information in data effectively. The experimental results on BCI competition data sets show that P300-VIB-Net achieves cutting-edge character recognition performance. Furthermore, the proposed model is capable of restricting the flow of irrelevant information adaptively in the network from perspective of information theory. The experimental results show that P300-VIB-Net is a promising tool for P300 detection.https://www.mdpi.com/1099-4300/23/1/39variational information bottleneckconvolutional neural networkP300 signal detection |
| spellingShingle | Hongpeng Liao Jianwu Xu Zhuliang Yu Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection Entropy variational information bottleneck convolutional neural network P300 signal detection |
| title | Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection |
| title_full | Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection |
| title_fullStr | Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection |
| title_full_unstemmed | Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection |
| title_short | Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection |
| title_sort | novel convolutional neural network with variational information bottleneck for p300 detection |
| topic | variational information bottleneck convolutional neural network P300 signal detection |
| url | https://www.mdpi.com/1099-4300/23/1/39 |
| work_keys_str_mv | AT hongpengliao novelconvolutionalneuralnetworkwithvariationalinformationbottleneckforp300detection AT jianwuxu novelconvolutionalneuralnetworkwithvariationalinformationbottleneckforp300detection AT zhuliangyu novelconvolutionalneuralnetworkwithvariationalinformationbottleneckforp300detection |