Journal ArticleDOI
EEGANet: Removal of Ocular Artifacts From the EEG Signal Using Generative Adversarial Networks
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TLDR
In this paper , the authors proposed EEGANet, a framework based on generative adversarial networks (GANs) to address this issue as a data-driven assistive tool for ocular artifacts removal, which can be applied calibration-free without relying on the EOG channels or the eye blink detection algorithms.Abstract:
The elimination of ocular artifacts is critical in analyzing electroencephalography (EEG) data for various brain-computer interface (BCI) applications. Despite numerous promising solutions, electrooculography (EOG) recording or an eye-blink detection algorithm is required for the majority of artifact removal algorithms. This reliance can hinder the model's implementation in real-world applications. This paper proposes EEGANet, a framework based on generative adversarial networks (GANs), to address this issue as a data-driven assistive tool for ocular artifacts removal (source code is available at https://github.com/IoBT-VISTEC/EEGANet). After the model was trained, the removal of ocular artifacts could be applied calibration-free without relying on the EOG channels or the eye blink detection algorithms. First, we tested EEGANet's ability to generate multi-channel EEG signals, artifacts removal performance, and robustness using the EEG eye artifact dataset, which contains a significant degree of data fluctuation. According to the results, EEGANet is comparable to state-of-the-art approaches that utilize EOG channels for artifact removal. Moreover, we demonstrated the effectiveness of EEGANet in BCI applications utilizing two distinct datasets under inter-day and subject-independent schemes. Despite the absence of EOG signals, the classification performance of the signals processed by EEGANet is equivalent to that of traditional baseline methods. This study demonstrates the potential for further use of GANs as a data-driven artifact removal technique for any multivariate time-series bio-signal, which might be a valuable step towards building next-generation healthcare technology. read more
Citations
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Journal ArticleDOI
Automatic Eyeblink and Muscular Artifact Detection and Removal From EEG Signals Using k-Nearest Neighbor Classifier and Long Short-Term Memory Networks
TL;DR: In this paper , a robust method that can automatically detect and remove eyeblink and muscular artifacts from EEG using a k-nearest neighbor classifier and a long short-term memory (LSTM) network is proposed.
Journal ArticleDOI
Embedding decomposition for artifacts removal in EEG signals
TL;DR: DeepSeparator as discussed by the authors employs an encoder to extract and amplify the features in the raw EEG, a module called decomposer to extract the trend, detect and suppress artifact and a decoder to reconstruct the denoised signal.
Posted Content
Embedding Decomposition for Artifacts Removal in EEG Signals
TL;DR: DeepSeparator as discussed by the authors employs an encoder to extract and amplify the features in the raw EEG, a module called decomposer to extract the trend, detect and suppress artifact and a decoder to reconstruct the denoised signal.
Journal ArticleDOI
MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network
TL;DR: In this article , a multi-layer multi-resolution spatially pooled (MLMRS) network was proposed for signal reconstruction for EEG motion artifact removal, which outperformed all the existing state-of-the-art techniques in terms of average η improvement.
Journal ArticleDOI
Automated Ocular Artifacts Removal Framework Based on Adaptive Chirp Mode Decomposition
Shivam Sharma,Udit Satija +1 more
TL;DR: Comparison performance analysis demonstrates that the proposed framework outperforms the existing OAs removal techniques based on wavelet thresholding, variational mode decomposition (VMD), and Savitzky-Golay filter (SG-filter).
References
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EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.
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Proceedings ArticleDOI
Analyzing and Improving the Image Quality of StyleGAN
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Journal ArticleDOI
Brain Computer Interfaces, a Review
TL;DR: The state-of-the-art of BCIs are reviewed, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface.
Journal ArticleDOI
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces
Vernon J. Lawhern,Amelia J. Solon,Nicholas R. Waytowich,Nicholas R. Waytowich,Stephen M. Gordon,Chou P. Hung,Chou P. Hung,Brent J. Lance +7 more
TL;DR: This work introduces EEGNet, a compact convolutional neural network for EEG-based BCIs, and introduces the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI.