Deep learning denoising for EOG artifacts removal from EEG signals

• URL: http://arxiv.org/abs/2009.08809v1
• Date: Sat, 12 Sep 2020 23:28:12 GMT
• Title: Deep learning denoising for EOG artifacts removal from EEG signals
• Authors: Najmeh Mashhadi, Abolfazl Zargari Khuzani, Morteza Heidari, Donya Khaledyan
• Abstract summary: One of the most challenging issues in EEG denoising processes is removing the ocular artifacts. In this paper, we build and train a deep learning model to deal with this challenge and remove the ocular artifacts effectively. We proposed three different schemes and made our U-NET based models learn to purify contaminated EEG signals.
• Score: 0.5243460995467893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There are many sources of interference encountered in the electroencephalogram (EEG) recordings, specifically ocular, muscular, and cardiac artifacts. Rejection of EEG artifacts is an essential process in EEG analysis since such artifacts cause many problems in EEG signals analysis. One of the most challenging issues in EEG denoising processes is removing the ocular artifacts where Electrooculographic (EOG), and EEG signals have an overlap in both frequency and time domains. In this paper, we build and train a deep learning model to deal with this challenge and remove the ocular artifacts effectively. In the proposed scheme, we convert each EEG signal to an image to be fed to a U-NET model, which is a deep learning model usually used in image segmentation tasks. We proposed three different schemes and made our U-NET based models learn to purify contaminated EEG signals similar to the process used in the image segmentation process. The results confirm that one of our schemes can achieve a reliable and promising accuracy to reduce the Mean square error between the target signal (Pure EEGs) and the predicted signal (Purified EEGs).

Related papers

• ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals [0.10499611180329801]
  Artifact removal in electroencephalography (EEG) significantly impacts neuroscientific analysis and brain-computer interface (BCI) performance. This study presents an innovative EEG denoising model employing transformer architecture to adeptly capture the transient millisecond-scale dynamics characteristic of EEG signals. Our evaluations confirm that ART surpasses other deep-learning-based artifact removal methods, setting a new benchmark in EEG signal processing.
  arXiv  Detail & Related papers  (2024-09-11T15:05:40Z)
• EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive Network [11.491355463353731]
  We introduce the Retnet from natural language processing to EEG denoising. Direct application of Retnet to EEG denoising is unfeasible due to the one-dimensional nature of EEG signals. We propose the signal embedding method, transforming one-dimensional EEG signals into two dimensions for use as network inputs.
  arXiv  Detail & Related papers  (2024-03-20T15:04:21Z)
• hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications [3.031375888004876]
  Two main issues challenge the existing DL-based modeling methods for EEG. High variability between subjects and low signal-to-noise ratio make it difficult to ensure a good quality in the EEG data. We propose two variational autoencoder models, namely vEEGNet-ver3 and hvEEGNet, to target the problem of high-fidelity EEG reconstruction.
  arXiv  Detail & Related papers  (2023-11-20T15:36:31Z)
• CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion [49.1574468325115]
  A key aim in EEG analysis is to extract the underlying neural activation (content) as well as to account for the individual subject variability (style) Inspired by recent advancements in voice conversion technologies, we propose a novel contrastive split-latent permutation autoencoder (CSLP-AE) framework that directly optimize for EEG conversion.
  arXiv  Detail & Related papers  (2023-11-13T22:46:43Z)
• A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation [48.85731427874065]
  This paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2) to extract effective representations from EEG with limited labels. The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity. By modeling prior neural knowledge based on neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations.
  arXiv  Detail & Related papers  (2023-09-21T08:53:51Z)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• EOG Artifact Removal from Single and Multi-channel EEG Recordings through the combination of Long Short-Term Memory Networks and Independent Component Analysis [0.0]
  We present a novel methodology that combines a long short-term memory (LSTM)-based neural network with ICA to address the challenge of EOG artifact removal from EEG signals. Our approach aims to accomplish two primary objectives: 1) estimate the horizontal and vertical EOG signals from the contaminated EEG data, and 2) employ ICA to eliminate the estimated EOG signals from the EEG.
  arXiv  Detail & Related papers  (2023-08-25T13:32:28Z)
• Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography [51.45515911920534]
  A task-oriented self-supervised learning approach is proposed to train a more effective anomaly detector. A specific two branch convolutional neural network with larger kernels is designed as the feature extractor. The effectively designed and trained feature extractor has shown to be able to extract better feature representations from EEGs.
  arXiv  Detail & Related papers  (2022-07-04T13:15:08Z)
• Robust learning from corrupted EEG with dynamic spatial filtering [68.82260713085522]
  Building machine learning models using EEG recorded outside of the laboratory requires robust methods to noisy data and randomly missing channels. We propose dynamic spatial filtering (DSF), a multi-head attention module that can be plugged in before the first layer of a neural network. We tested DSF on public EEG data encompassing 4,000 recordings with simulated channel corruption and on a private dataset of 100 at-home recordings of mobile EEG with natural corruption.
  arXiv  Detail & Related papers  (2021-05-27T02:33:16Z)
• A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition [51.203579838210885]
  We propose a transferable attention neural network (TANN) for EEG emotion recognition. TANN learns the emotional discriminative information by highlighting the transferable EEG brain regions data and samples adaptively. This can be implemented by measuring the outputs of multiple brain-region-level discriminators and one single sample-level discriminator.
  arXiv  Detail & Related papers  (2020-09-21T02:42:30Z)
• Analysis of artifacts in EEG signals for building BCIs [0.42641920138420947]
  Brain-Computer Interface (BCI) is an essential mechanism that interprets the human brain signal. EEG signals are noisy owing to the presence of many artifacts, namely, eye blink, head movement, and jaw movement. We propose a practical BCI that uses the artifacts which has a low signal to noise ratio.
  arXiv  Detail & Related papers  (2020-09-18T23:03:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.