The goal of this study was to investigate the influence of the brain-to-skull conductivity ratio (BSCR) on EEG source localization accuracy. In this study, we evaluated four BSCRs: 15, 20, 25, and 80, which were mainly discussed according to the literature. The scalp EEG signals were generated by BSCR-related forward computation for each cortical dipole source. Then, for each scalp EEG measurement, the source reconstruction was performed to identify the estimated dipole sources by the actual BSCR and the misspecified BSCRs. The estimated dipole sources were compared with the simulated dipole sources to evaluate EEG source localization accuracy. In the case of considering noise-free EEG measurements, the mean localization errors were approximately equal to zero when using actual BSCR. The misspecified BSCRs resulted in substantial localization errors which ranged from 2 to 16 mm. When considering noise-contaminated EEG measurements, the mean localization errors ranged from 8 to 18 mm despite the BSCRs used in the inverse calculation. The present results suggest that the localization accuracy is sensitive to the BSCR in EEG source reconstruction, and the source activity can be accurately localized when the actual BSCR and the EEG scalp signals with high signal-to-noise ratio (SNR) are used.
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http://dx.doi.org/10.1155/2013/459346 | DOI Listing |
Brain Sci
December 2024
College of Medicine, Medical University of South Carolina, Charleston, SC 29425, USA.
Background/objectives: Noninvasive brain stimulation (NIBS) can boost motor recovery after a stroke. Certain movement phases are more responsive to NIBS, so a system that auto-detects these phases would optimize stimulation timing. This study assessed the effectiveness of various machine learning models in identifying movement phases in hemiparetic individuals undergoing simultaneous NIBS and EEG recordings.
View Article and Find Full Text PDFHeliyon
January 2025
Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022, València, Spain.
Resting state electroencephalography (EEG) has proved useful in studying electrophysiological changes in neurodegenerative diseases. In many neuropathologies, microstate analysis of the eyes-closed (EC) scalp EEG is a robust and highly reproducible technique for assessing topological changes with high temporal resolution. However, scalp EEG microstate maps tend to underestimate the non-occipital or non-alpha-band networks, which can also be used to detect neuropathological changes.
View Article and Find Full Text PDFExplore (NY)
January 2025
Instituto de Neurociencia Avanzada de Barcelona (INAB), Barcelona, 08039, Spain.
Just as the brain of Albert Einstein is studied in an attempt to understand human intelligence or the bodies of elite athletes are examined to improve muscle strength, the study of people who claim to have spiritual experiences could enrich the investigation of the brain-mind relationship. Although mediumship with deceased people is widely extensively studied in spiritual experiences, we explored a mediumistic experience called "channeling" where the individual connects with a non-corporeal intelligence (NCI) source. To approach this kind of spiritual experience, we considered three hypotheses: the fraud hypothesis (i), the mental pathology hypothesis (ii), and the extrasensory perception hypothesis (iii).
View Article and Find Full Text PDFPLoS One
January 2025
Computer Engineering, CCSIT, King Faisal University, Al Hufuf, Kingdom of Saudi Arabia.
This paper presents a low-power, second-order composite source-follower-based filter architecture optimized for biomedical signal processing, particularly ECG and EEG applications. Source-follower-based filters are recommended in the literature for high-frequency applications due to their lower power consumption when compared to filters with alternative topologies. However, they are not suitable for biomedical applications requiring low cutoff frequencies as they are designed to operate in the saturation region.
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