Convolutional denoising autoencoder based SSVEP signal enhancement to SSVEP-based BCIs

Chuang, Chia Chun; Lee, Chien-Ching; Yeng, Chia Hong; So, Edmund Cheung; Lin, Bertrand M.T.; Chen, Yeou-Jiunn

doi:10.1007/s00542-019-04654-2

Cited by 6 publications

(8 citation statements)

References 29 publications

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“…This demonstrates that the embedding feature with denoising characteristics is highly effective for suppressing noise. To detail the characteristics of the proposed SSVEP-based BCI with MTL, the architecture of the DAE approach [12] is selected. In this approach, the encoder and decoder networks are trained, and then the weights of the encoder network are fixed.…”

Section: Experimental Results Of Classification Taskmentioning

confidence: 99%

“…ALS hinders the normal functioning of the motor neurons that control voluntary muscles, causing progressive weakness, muscle twitching, and stiff muscles [ 9 , 10 , 11 , 12 , 13 ]. The abilities to swallow, walk, move hands, and speak are severely degraded.…”

Section: Introductionmentioning

confidence: 99%

“…SSVEP-based BCIs with a single channel have been developed and used to help people with ALS [ 6 , 12 , 15 ]. However, the performance of these systems is always degraded in practical applications due to the noisy SSVEP signal.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces

Chuang

Lee

et al. 2022

Sensors

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Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain–computer interface (SSVEP-based BCI), which can help people communicate with others. To ease the operation of the input interface, a single channel-based SSVEP-based BCI is selected. To increase the practicality of SSVEP-based BCI, multi-task learning is adopted to develop the neural network-based intelligent system, which can suppress the noise components and obtain a high level of accuracy of classification. Thus, denoising and classification tasks are selected in multi-task learning. The experimental results show that the proposed multi-task learning can effectively integrate the advantages of denoising and discriminative characteristics and outperform other approaches. Therefore, multi-task learning with denoising and classification tasks is very suitable for developing an SSVEP-based BCI for practical applications. In the future, an augmentative and alternative communication interface can be implemented and examined for helping people with ALS communicate with others in their daily lives.

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Section: Experimental Results Of Classification Taskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces

Chuang

Lee

et al. 2022

Sensors

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“…In the above formula, the gain factor is expressed as γ, which has the control function of filter output, and the value is usually the reciprocal of kurtosis coefficient [26,27]. At this time, the output result of the filter is the enhanced low-power communication signal of the Internet of Things, which completes the enhancement of the low-power communication signal of the Internet of Things.…”

Section: Nonlocal Mean Denoising and Enhancement Methods Of Low-power...mentioning

confidence: 99%

Low-Power Communication Signal Enhancement Method of Internet of Things Based on Nonlocal Mean Denoising

Mingchuan

Liu

2022

Wireless Communications and Mobile Computing

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In order to improve the transmission effect of low-power communication signal of Internet of Things and compress the enhancement time of low-power communication signal, this paper designs a low-power communication signal enhancement method of Internet of Things based on nonlocal mean denoising. Firstly, the residual of one-dimensional communication layer is preprocessed by convolution core to obtain the residual of one-dimensional communication layer. Then, according to the two classification recognition methods, the noise reduction signal feature recognition of the low-power communication signal of the Internet of Things is realized, the nonlocal mean noise reduction algorithm is used to remove the low-power communication signal of the Internet of Things, and the weight value between similar blocks is calculated according to the European distance method. Finally, the low-power communication signal enhancement of the Internet of Things is realized by the nonlocal mean value denoising method. The experimental results show that the communication signal enhancement time overhead of this method is low, which is always less than 2.6 s. The lowest bit error rate after signal enhancement is about 1%, and the signal-to-noise ratio is up to 18 dB, which shows that this method can achieve signal enhancement.

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“…Deep learning-based approaches have recently reported excellent results in noise filtering [20]- [23], particularly the well-known denoising algorithm based on the denoising autoencoder (DAE) [24]- [31]. Xiong et al first eliminated the noise in the ECG by DWT, and the remaining noise was further removed using a deep neural network (DNN)-DAE [24].…”

Section: Introductionmentioning

confidence: 99%

Lightweight Denoising Autoencoder Design for Noise Removal in Electrocardiography

et al. 2022

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This study proposes two denoising autoencoder (DAE) models with discrete cosine transform (DCT) and discrete wavelet transform (DWT), namely DCT-DAE and DWT-DAE, to remove electrode motion artifacts in noisy electrocardiography (ECG). Initially, the discrete cosine transform and discrete wavelet transform efficiently removed the high-frequency noise. The six encoder layers then retain important ECG features while the six decoder layers reconstruct the clean ECG. To improve the denoising performance, two network layers, the residual block and pixel adjustment, are added to the encoder and decoder layers to solve the vanishing gradient and improve subtle feature extraction. The proposed methods were applied to 66,000 real-recorded noisy ECG fragments. The experimental result indicates that DWT-DAE and DCT-DAE can improve the signal-to-noise ratio by 25.29 and 25.13 dB on average when the input signal-to-noise ratio is -6 dB.

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Convolutional denoising autoencoder based SSVEP signal enhancement to SSVEP-based BCIs

Cited by 6 publications

References 29 publications

Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces

Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces

Low-Power Communication Signal Enhancement Method of Internet of Things Based on Nonlocal Mean Denoising

Lightweight Denoising Autoencoder Design for Noise Removal in Electrocardiography

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