Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
July 1, 2019
Minji Lee, Benjamin Baird, Olivia Gosseries et al.
4 citations
Cortical networks show differences in functional integration and segregation across states of consciousness, but not in overall connectivity. In the beta frequency band, functional integration during wakefulness exceeded that during NREM sleep. In the theta band, functional segregation (transitivity and clustering coefficient) was stronger in NREM sleep without conscious experience than in wakefulness or REM sleep, while the opposite pattern appeared in the beta band. No significant differences in the weighted phase lag index were found among wakefulness, REM sleep with conscious experience, NREM sleep with conscious experience, and NREM sleep without conscious experience. These findings may relate to cortical bistability and contribute to understanding neural correlates of consciousness.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
July 1, 2024
Tamas Madl
2 citations
Meditation's benefits are increasingly recognized, but the brain's electrical activity during meditative states is not fully understood. Existing markers have limited predictive accuracy, suggesting important information is missing. This work converts EEG time series into scale-free networks using horizontal visibility graphs, which distinguish deterministic from random systems and model new aspects of brain oscillations. The authors introduce a class of network-based predictors that outperform popular spectral and nonlinear features like complexity or entropy. These predictors show statistical significance for several meditation types, using data from highly skilled meditators, and are suitable for real-time analysis and applications such as neurofeedback.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
July 1, 2025
H Sid-Ahmed, J Alayrangues, L Langar et al.
Hypnosis alters consciousness and is used for pain management, but assessing hypnotic trance relies on subjective signs. Using magnetoencephalography (MEG) and an auditory oddball paradigm, brain signals were recorded from 20 healthy subjects during critical consciousness, hypnotic trance, and distraction. Feature extraction and classification models were tested; EEGNet performed best, achieving 70% and 84% ROC-AUC in distinguishing hypnotic trance from critical consciousness and distraction, respectively, with predictions every 4 seconds after a 19-minute training session. This method offers real-time, objective assessment of hypnotic trance and could be adapted for clinical use with electroencephalography (EEG).
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
July 1, 2025
Matin Beiramvand, Reijo Koivula, Tarmo Lipping
Analyzing 29 EEG recordings from participants playing Tetris, a method using three entropy-based features (Slope, Distribution, and Spectral Entropy) extracted via Discrete Wavelet Transform and classified with a Random Forest model achieved 93% accuracy with random sampling and 82% with leave-one-subject-out cross-validation. The findings suggest this low-channel, consumer-device approach is promising for detecting the flow state in real-life settings.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
July 1, 2022
Zhian Liu, Lichengxi Si, Tianyu Wang et al.
By converting high-density electroencephalogram (EEG) signals recorded from the scalp into cortical signals using source estimation, researchers examined how propofol alters consciousness. In 20 healthy adults, they filtered alpha-band activity and calculated pairwise orthogonal power envelope connectivity (PEC) across 68 brain regions. A statistical method (LASSO) identified the fewest PECs needed to distinguish baseline from moderate sedation. Most of those PECs involved regions of the default mode network, and changes in thalamocortical and frontal-parietal connectivity matched those seen with direct neuroimaging. A classifier based on the selected PECs achieved over 70% accuracy in distinguishing the two states, suggesting this approach could aid future anesthesia depth monitoring.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
July 1, 2019
M Nardelli, U Faraguna, G Grandi et al.
Dream recall is linked to higher complexity in cardiovascular control during REM sleep. Researchers recorded electrocardiogram and arterial blood pressure from eight healthy subjects during REM sleep before awakening. Recordings were split into those with and without dream recall. Sample Entropy showed no statistical difference between groups, but multiscale complexity analysis using Distribution Entropy and Fuzzy Entropy revealed that higher cardiovascular complexity is associated with a dreaming experience.