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.

scholarly journals Far-field super-resolution ghost imaging with a deep neural network constraint

2022 ◽  
Vol 11 (1) ◽  
Author(s):  
Fei Wang ◽  
Chenglong Wang ◽  
Mingliang Chen ◽  
Wenlin Gong ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Model ◽  
Deep Neural Network ◽  
Super Resolution ◽  
Far Field ◽  
Ghost Imaging ◽  
Practical Applications ◽  
Sampling Ratio ◽  
Network Output ◽  
Single Pixel

AbstractGhost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing. However, GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image, imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit. Furthermore, the physical model imposes a constraint to the network output, making it effectively interpretable. We experimentally demonstrate the proposed GI technique by imaging a flying drone, and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling ratio. We believe that this study provides a new framework for GI, and paves a way for its practical applications.

scholarly journals Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings

2020 ◽  
Vol 131 ◽  
pp. 109980 ◽  
Author(s):  
X.J. Luo ◽  
Lukumon O. Oyedele ◽  
Anuoluwapo O. Ajayi ◽  
Olugbenga O. Akinade ◽  
Hakeem A. Owolabi ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Extraction ◽  
Energy Consumption ◽  
Deep Neural Network ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

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 Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Zhuofu Deng ◽  
Binbin Wang ◽  
Heng Guo ◽  
Chengwei Chai ◽  
Yanze Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Quantile Regression ◽  
Electricity Market ◽  
Deep Neural Network ◽  
Load Forecasting ◽  
Practical Applications ◽  
Load Pattern ◽  
Probabilistic Load ◽  
Reliability And Robustness ◽  
Quantile Loss

Residential load forecasting is important for many entities in the electricity market, but the load profile of single residence shows more volatilities and uncertainties. Due to the difficulty in producing reliable point forecasts, probabilistic load forecasting becomes more popular as a result of catching the volatility and uncertainty by intervals, density, or quantiles. In this paper, we propose a unified quantile regression deep neural network with time-cognition for tackling this challenging issue. At first, a convolutional neural network with multiscale convolution is devised for extracting more behavioral features from the historical load sequence. In addition, a novel periodical coding method marks the model to enhance its ability of capturing regular load pattern. Then, features generated from both subnetworks are fused and fed into the forecasting model with an end-to-end manner. Besides, a globally differentiable quantile loss function constrains the whole network for training. At last, forecasts of multiple quantiles are directly generated in one shot. With ablation experiments, the proposed model achieved the best results in the AQS, AACE, and inversion error, and especially the average of the AACE is grown by 34.71%, 75.22%, and 32.44% compared with QGBRT, QCNN, and QLSTM, respectively, indicating that our method has excellent reliability and robustness rather than the state-of-the-art models obviously. Meanwhile, great performances of efficient time response demonstrate that our proposed work has promising prospects in practical applications.

scholarly journals Acoustic Feature Extraction with Interpretable Deep Neural Network for Neurodegenerative Related Disorder Classification

2020 ◽  
Author(s):  
Yilin Pan ◽  
Bahman Mirheidari ◽  
Zehai Tu ◽  
Ronan O’Malley ◽  
Traci Walker ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Neural Network ◽  
Acoustic Feature ◽  
Related Disorder
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