Background: The feasibility of recording electroencephalography (EEG) at ultra-high static magnetic fields up to 9.4 T was recently demonstrated and is expected to be incorporated into functional magnetic resonance imaging (fMRI) studies at 9.4 T. Correction of the pulse artefact (PA) is a significant challenge since its amplitude is proportional to the strength of the magnetic field in which EEG is recorded.
New Method: We conducted a study in which different PA correction methods were applied to EEG data recorded inside a 9.4 T scanner in order to retrieve visual P100 and auditory P300 evoked potentials. We explored different PA reduction methods, including the optimal basis set (OBS) method as well as objective and subjective component rejection using independent component analysis (ICA).
Results: ICA followed by objective rejection of components is optimal for retrieving visual P100 and auditory P300 from EEG data recorded inside the scanner.
Comparison With Existing Methods: Previous studies suggest that OBS or OBS followed by ICA are optimal for retrieving evoked potentials at 3T. In our EEG data recorded at 9.4 T OBS performed alone was not fully optimal for the identification of evoked potentials. OBS followed by ICA was partially effective.
Conclusions: In this study ICA has been shown to be an important tool for correcting the PA in EEG data recorded at 9.4 T, particularly when automated rejection of components is performed.
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http://dx.doi.org/10.1016/j.jneumeth.2014.05.015 | DOI Listing |
Front Neurosci
January 2025
Center of Excellence in Intelligent Engineering Systems (CEIES), Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia.
Introduction: Excessive alcohol consumption negatively impacts physical and psychiatric health, lifestyle, and societal interactions. Chronic alcohol abuse alters brain structure, leading to alcohol use disorder (AUD), a condition requiring early diagnosis for effective management. Current diagnostic methods, primarily reliant on subjective questionnaires, could benefit from objective measures.
View Article and Find Full Text PDFFront Neurosci
January 2025
Department of Mathematics, University of Antwerp-Interuniversity Microelectronics Centre (imec), Antwerp, Belgium.
Introduction: The study of attention has been pivotal in advancing our comprehension of cognition. The goal of this study is to investigate which EEG data representations or features are most closely linked to attention, and to what extent they can handle the cross-subject variability.
Methods: We explore the features obtained from the univariate time series from a single EEG channel, such as time domain features and recurrence plots, as well as representations obtained directly from the multivariate time series, such as global field power or functional brain networks.
Int J Gen Med
January 2025
Shijiazhuang Rongkang Hospital of Traditional Chinese Medicine Co., Ltd., Internal Medicine, Shijiazhuang, 050000, People's Republic of China.
Background: Refractory epilepsy poses significant challenges in clinical management due to its resistance to standard antiepileptic therapies, necessitating the exploration of more effective treatment regimens. Lamotrigine, with its proven efficacy and tolerability, offers potential benefits when combined with traditional medications like valproate, though its comprehensive impact on clinical outcomes and neurological markers requires further study.
Objective: To analyze the improvement effect of combined application of lamotrigine on refractory epilepsy patients and its impact on patients' EEG and neurological function.
Cogn Neurodyn
December 2025
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, TamilNadu India.
Emotion recognition plays a crucial role in brain-computer interfaces (BCI) which helps to identify and classify human emotions as positive, negative, and neutral. Emotion analysis in BCI maintains a substantial perspective in distinct fields such as healthcare, education, gaming, and human-computer interaction. In healthcare, emotion analysis based on electroencephalography (EEG) signals is deployed to provide personalized support for patients with autism or mood disorders.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang China.
The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance.
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