Progress in neuro-psychopharmacology & biological psychiatry
Nov 14, 2025
BACKGROUND: Obsessive-compulsive disorder (OCD) is characterized by disruptions in large-scale brain networks. However, the role of high-frequency neural synchrony in these abnormalities remains unclear. Elucidating frequency-specific alterations may...
High-throughput analysis of EEG data has significantly contributed to understanding neural dynamics in Alzheimer's disease diagnosis. However, the complexity and high dimensionality of EEG signals pose challenges for traditional classification method...
Virtual reality (VR) technologies can induce realistic emotions in controlled experimental settings, offering unprecedented opportunities to study how the human brain processes emotions under real-world conditions. The integration of VR experiences w...
The heterogeneity of psychotic disorders leads to instability in subjectively defined diagnoses. This study used a machine learning framework termed common orthogonal basis extraction (COBE) to decompose electroencephalography-based functional connec...
Biomedical physics & engineering express
Nov 14, 2025
. This study explores a more reliable method for measuring nociceptive pain induced by laser stimuli from electroencephalography (EEG) signals, addressing the limitations of fixed pain scales by incorporating inter-individual variability in subjectiv...
Electroencephalography (EEG) records the spontaneous electrical activity in the brain. Despite the growing application of deep learning in EEG decoding, traditional methods still rely heavily on supervised learning, which is often limited by task spe...
Crossmodal interactions involve crosstalk between different cortical areas and dynamic recruitment of regions, which is crucial for integrating sensory information into a coherent percept. Despite their significance, the dynamic cortical networks und...
Electroencephalography (EEG) enables the investigation of olfactory perception through neuronal electrical activity. Decoding dynamic oscillatory changes in sensory-cognitive processing is critical to understanding odor-induced brain responses. First...
Electroencephalogram (EEG) signals play a critical role in advancing brain-computer interface (BCI) systems, particularly for detecting motor imagery (MI) movements. However, analysing large volume of EEG datasets faces some challenges due to redunda...
Machine learning (ML) offers great potential in healthcare, especially in the analysis of complex physiological signals like electroencephalography (EEG). EEG recordings hold valuable insights into neurological function and can aid in diagnosing vari...
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