The aim of this study was to find distinctions of the EEG signal in female depression. Experiments were carried out on two groups of 18 female volunteers each: a group of patients with depressive disorder who were not on medication and a group of control subjects. Patients who had Hamilton depression rating scores higher than 14 were selected. Resting EEG was recorded for the duration of 30 min. Spectral asymmetry (SA) of the EEG spectrum was estimated as relative difference in the selected higher and lower EEG frequency band power. Calculated SA values were positive for depressive and negative for healthy subjects (except for 2-3 subjects). The values behaved similarly in all EEG channels and brain hemispheres. Differences in SA between depressive and control groups were significant in all EEG channels. Dependence of SA on EGG signal length appeared not to be identical for depressive and healthy subjects. Our results suggest that SA based on balance between the powers of the higher and the lower EEG frequency bands seems to enable characterization of the EEG in depression.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1515/BMT.2010.011 | DOI Listing |
Comput Methods Biomech Biomed Engin
January 2025
Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
The conversion of a person's intentions into device commands through the use of brain-computer interface (BCI) is a feasible communication method for individuals with nervous system disorders. While common spatial pattern (CSP) is commonly used for feature extraction in BCIs, it has limitations. It is known for its susceptibility to noise and tendency to overfit.
View Article and Find Full Text PDFiScience
January 2025
Cognitive Neuroimaging Unit U992, CNRS, INSERM, CEA, DRF/Institut Joliot, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France.
The need for attention to enable statistical learning is debated. Testing individuals with impaired consciousness offers valuable insight, but very few studies have been conducted due to the difficulties inherent in such studies. Here, we examined the ability of patients with varying levels of disorders of consciousness (DOC) to extract statistical regularities from an artificial language composed of randomly concatenated pseudowords by measuring frequency tagging in EEG.
View Article and Find Full Text PDFiScience
January 2025
Laboratory for Neuroengineering, Department of Health Science and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland.
Our brain combines sensory inputs to create a univocal perception, enhanced when stimuli originate from the same location. Following amputation, distorted body representations may disrupt visuo-tactile integration at the amputated leg. We aim to unveil the principles guiding optimal and cognitive-efficient visuo-tactile integration at both intact and amputated legs.
View Article and Find Full Text PDFiScience
January 2025
Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium.
Post-comatose disorders of consciousness (DoC) represent persistent neurological conditions with limited therapeutic options and a poor prognosis. Recent works advocate for exploring the effects of psychedelics to enhance brain complexity in DoC and ameliorate their consciousness. We investigated sub-anesthetic concentration of the atypical psychedelic ketamine for treating post-comatose prolonged DoC through a double-blind, placebo-controlled, cross-over trial involving three adult patients.
View Article and Find Full Text PDFIn this paper, we propose simultaneous and sequential hybrid brain-computer interfaces (hBCIs) that incorporate electroencephalography (EEG) and electromyography (EMG) signals to classify drivers' hard braking, soft braking, and normal driving intentions to better assist driving for the first time. The simultaneous hBCIs adopt a feature-level fusion strategy (hBCI-FL) and classifier-level fusion strategies (hBCIs-CL). The sequential hBCIs include the hBCI-SE1, where EEG signals are prioritized to detect hard braking, and hBCI-SE2, where EMG signals are prioritized to detect hard braking.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!