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Electroencephalography (EEG) signals are a valuable source of information for investigating brain activity and different types of brain-related disease diagnoses. However, EEG signals are often contaminated by various kinds of noises/artifacts. Several methods have been proposed for EEG reconstruction/denoising to facilitate signal analysis, but such algorithms often fail when the EEG contains extreme artifacts. This paper presents a novel method for reconstructing EEG signals using a variant of the variational autoencoder (VAE) called beta-VAE. Through extensive evaluation of our model on the DEAP dataset, we show that the β-VAE architecture learns a compressed representation of the EEG signal in an unsupervised manner, and the reconstructed signal contains less artifact. We compare our proposed method with different baselines and state-of-the-art techniques for EEG signal denoising, demonstrating significantly reduced reconstruction error under artificially induced noise. The results suggest that our approach has great potential for improving the analysis and understanding of EEG signals in clinical and research settings.
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http://dx.doi.org/10.1109/EMBC53108.2024.10782962 | DOI Listing |
Sci Rep
March 2025
Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India.
Brain-Computer Interface (BCI) is a versatile technique to offer better communication system for people affected by the locked-in syndrome (LIS).In the current decade, there has been a growing demand for improved care and services for individuals with neurodegenerative diseases. To address this barrier, the current work is designed with four states of BCI for paralyzed persons using Welch Power Spectral Density (W-PSD).
View Article and Find Full Text PDFActa Psychol (Amst)
March 2025
Key Laboratory for Artificial Intelligence and Cognitive Neuroscience of Language, Xi'an International Studies University, Xi'an 710121, China.
Emotion is crucial for the quality of daily life. Recent findings suggest that the cooperation and integration of multiple brain regions are essential for effective emotion processing. Additionally, network reconfiguration has been observed during various cognitive tasks.
View Article and Find Full Text PDFJ Mol Neurosci
March 2025
Department of Physics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Science (SIMATS), Thandalam, Chennai, 602105, India.
Parkinson's disease recognition (PDR) involves identifying Parkinson's disease using clinical evaluations, imaging studies, and biomarkers, focusing on early symptoms like tremors, rigidity, and bradykinesia to facilitate timely treatment. However, due to noise, variability, and the non-stationary nature of EEG signals, distinguishing PD remains a challenge. Traditional deep learning methods struggle to capture the intricate temporal and spatial dependencies in EEG data, limiting their precision.
View Article and Find Full Text PDFSci Rep
March 2025
Department of Electrical and Computer Engineering, Bahir Dar University, Bahir Dar, Ethiopia.
Many traffic accidents occur nowadays as a result of drivers not paying enough attention or being vigilant. We call this driver sleepiness. This results in numerous unfavourable circumstances that negatively impact people's life.
View Article and Find Full Text PDFNeuropsychologia
March 2025
Department of Psychology, University of Bath, Claverton Down Road, Bath, Somerset, BA2 7AY, UK.
The fast periodic visual stimulation oddball paradigm (FPVS-oddball) is an electroencephalography (EEG) marker of discrimination between two classes of frequency tagged stimuli (standards and oddballs). Here, we probe low-level visual function using FPVS-oddball, with a view to its future use as a sensitive diagnostic marker of visuoperceptual cognitive impairment. Thirty participants (21 (±5) years, 7 males) completed five FPVS-oddball conditions that implicitly measured their ability to discriminate an oddball line orientation (1°,5°,10°,30°,80°), from a standard vertical line, as well as an equiprobable control condition.
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