A Deep Neural Network for SSVEP-based Brain-Computer Interfaces

2021 ◽  
pp. 1-1
Author(s):  
Osman Berke Guney ◽  
Muhtasham Oblokulov ◽  
Huseyin Ozkan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

scholarly journals Deep Neural Network with Joint Distribution Matching for Cross-Subject Motor Imagery Brain-Computer Interfaces

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xianghong Zhao ◽  
Jieyu Zhao ◽  
Cong Liu ◽  
Weiming Cai
Keyword(s):  
Neural Network ◽  
Motor Imagery ◽  
Joint Distribution ◽  
Deep Neural Network ◽  
Brain Computer Interfaces ◽  
Target Domain ◽  
Common Space ◽  
Computer Interfaces ◽  
Intermediate Layers ◽  
Distribution Matching

Motor imagery brain-computer interfaces (BCIs) have demonstrated great potential and attract world-spread attentions. Due to the nonstationary character of the motor imagery signals, costly and boring calibration sessions must be proceeded before use. This prevents them from going into our realistic life. In this paper, the source subject’s data are explored to perform calibration for target subjects. Model trained on source subjects is transferred to work for target subjects, in which the critical problem to handle is the distribution shift. It is found that the performance of classification would be bad when only the marginal distributions of source and target are made closer, since the discriminative directions of the source and target domains may still be much different. In order to solve the problem, our idea comes that joint distribution adaptation is indispensable. It makes the classifier trained in the source domain perform well in the target domain. Specifically, a measure for joint distribution discrepancy (JDD) between the source and target is proposed. Experiments demonstrate that it can align source and target data according to the class they belong to. It has a direct relationship with classification accuracy and works well for transferring. Secondly, a deep neural network with joint distribution matching for zero-training motor imagery BCI is proposed. It explores both marginal and joint distribution adaptation to alleviate distribution discrepancy across subjects and obtain effective and generalized features in an aligned common space. Visualizations of intermediate layers illustrate how and why the network works well. Experiments on the two datasets prove the effectiveness and strength compared to outstanding counterparts.

scholarly journals Denoising Autoencoder-Based Feature Extraction to Robust SSVEP-Based BCIs

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5019
Author(s):  
Yeou-Jiunn Chen ◽  
Pei-Chung Chen ◽  
Shih-Chung Chen ◽  
Chung-Min Wu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Neural Network ◽  
Evoked Potential ◽  
Noise Suppression ◽  
Denoising Autoencoder ◽  
Practical Applications ◽  
Computer Interfaces ◽  
Recognition Result ◽  
Lateral Sclerosis

For subjects with amyotrophic lateral sclerosis (ALS), the verbal and nonverbal communication is greatly impaired. Steady state visually evoked potential (SSVEP)-based brain computer interfaces (BCIs) is one of successful alternative augmentative communications to help subjects with ALS communicate with others or devices. For practical applications, the performance of SSVEP-based BCIs is severely reduced by the effects of noises. Therefore, developing robust SSVEP-based BCIs is very important to help subjects communicate with others or devices. In this study, a noise suppression-based feature extraction and deep neural network are proposed to develop a robust SSVEP-based BCI. To suppress the effects of noises, a denoising autoencoder is proposed to extract the denoising features. To obtain an acceptable recognition result for practical applications, the deep neural network is used to find the decision results of SSVEP-based BCIs. The experimental results showed that the proposed approaches can effectively suppress the effects of noises and the performance of SSVEP-based BCIs can be greatly improved. Besides, the deep neural network outperforms other approaches. Therefore, the proposed robust SSVEP-based BCI is very useful for practical applications.

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