Deep Convolutional Autoencoder for EEG Noise Filtering

Leite, Niago Moreira Nobre; Pereira, Eanes Torres; Gurjão, Edmar C.; Veloso, Luciana

doi:10.1109/bibm.2018.8621080

Cited by 38 publications

(23 citation statements)

References 15 publications

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“…Artifact removal Future work will pursue this aspect of artifact analysis as well, with state-of-the-art techniques such as denoising autoencoders [79,80].…”

Section: Discussionmentioning

confidence: 99%

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

et al. 2021

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Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials.

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“…Artifact removal Future work will pursue this aspect of artifact analysis as well, with state-of-the-art techniques such as denoising autoencoders [79,80].…”

Section: Discussionmentioning

confidence: 99%

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

et al. 2021

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“…Denoising of EEG based on neural networks started to be investigated mainly due to its usability in real-time and, of course, high accuracy. For example, Leite et al [ 139 ] proposed a deep convolutional auto-encoder for eye-blinking and jaw-clenching artifacts elimination, which proved the superiority to traditional filtering methods. The elimination of ocular artifacts (OAs) using a deep learning network (DLN) was investigated in [ 140 ].…”

Section: Electroencephalographymentioning

confidence: 99%

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

Martínek

Ládrová

Šidiková

et al. 2021

Sensors

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As it was mentioned in the previous part of this work (Part I)—the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work—various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

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“…EEG signals and noise:Most of the studies reviewed here use conventional artifacts removal techniques likewise: visual inspection,notch filter, low,band and high pass filter to sanitize the polluted EEG signals.These conventional approaches cannot filter the signals accurately and signals still carry a lot of noise, which yield a negative impact on depression detection/ classification accuracy [183]. Suggestion: The signal pre-processing or filtering can be automated by using some deep learning approaches.…”

Section: Data Availabilitymentioning

confidence: 99%

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

Yasin¹,

Hussain²,

Aslan³

et al. 2020

Preprint

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A summary of clinical background of MDD and BD, Brain structure and EEG based brain activity,• EEG experimental protocols study for mental disorders diagnosis,• An extensive study of state-of-the-art shallow and deep neural networks models for EEG based clinical depression detection,• An extensive study of neural networks based approaches for Biploar Disorder diagnosis using EEG signals,• A discussion of state-of-the-art methods limitations and giving valuable recommendations for future research

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Deep Convolutional Autoencoder for EEG Noise Filtering

Cited by 38 publications

References 15 publications

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

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