AIMC Topic: Electroencephalography

Clear Filters Showing 1871 to 1880 of 2295 articles

[Localizing target for transcranial electrical stimulation in epilepsy patients combining scalp electroencephalogram and neural computational model].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
For patients with MRI-negative drug-resistant epilepsy, noninvasive localization of targets for transcranial electrical stimulation (tES) remains a clinical challenge. This study proposes a novel target localization approach that integrates electroen...

[A motor imagery decoding study integrating differential attention with a multi-scale adaptive temporal convolutional network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Motor imagery electroencephalogram (MI-EEG) decoding algorithms face multiple challenges. These include incomplete feature extraction, susceptibility of attention mechanisms to distraction under low signal-to-noise ratios, and limited capture of long...

DistillSleep: real-time, on-device, interpretable sleep staging from single-channel electroencephalogram.

Sleep
STUDY OBJECTIVES: Polysomnography (PSG) is the current gold standard for sleep staging but requires laboratory equipment, multiple sensors, and labor-intensive manual scoring. We developed DistillSleep, a single-channel electroencephalogram (EEG) fra...

Enhancing differentiation between unipolar and bipolar depression through integration of machine learning and electroencephalogram analysis.

Journal of affective disorders
To enhance the differentiation between unipolar depression (UPD) and bipolar depression (BPD), this study integrates machine learning and deep learning models with electroencephalography (EEG) data and clinical features. Utilizing Python for data pre...

Multiband EEG signatures decoded using machine learning for predicting rTMS treatment response in MDD.

Journal of affective disorders
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for major depression disorder (MDD), particularly for treatment-resistant cases. However, identifying translatable biomarkers predictive of treatment outcomes re...

Deep Learning for EEG-Based Visual Classification and Reconstruction: Panorama, Trends, Challenges and Opportunities.

IEEE transactions on bio-medical engineering
Deep learning has significantly enhanced the research on the emerging issue of Electroencephalogram (EEG)-based visual classification and reconstruction, which has gained a growth of attention and concern recently. To promote the research progress, a...

CLaI: Collaborative Learning and Inference for Low-Resolution Physiological Signals: Validation in Clinical Event Detection and Prediction.

IEEE transactions on bio-medical engineering
While machine learning (ML) techniques have been applied to detection and prediction tasks in clinical data, most methods rely on high-resolution data, which is not routinely available in most Intensive Care Units (ICUs), and perform poorly when face...

Hierarchical Dynamic Graph Convolutional Network With Interpretability for EEG-Based Emotion Recognition.

IEEE transactions on neural networks and learning systems
Graph convolutional networks (GCNs) have shown great prowess in learning topological relationships among electroencephalogram (EEG) channels for EEG-based emotion recognition. However, most existing GCN-only methods are designed with a single spatial...

Neural evidence for attentional resource allocation to postural control using brain-body imaging.

Behavioural brain research
OBJECTIVE: To examine whether bipedal stance (quiet standing) requires more attentional resources than sitting during a concurrent cognitive task.

Deep Learning-Augmented Sleep Spindle Detection for Acute Disorders of Consciousness: Integrating CNN and Decision Tree Validation.

IEEE transactions on bio-medical engineering
Sleep spindles, which are key biomarkers of non-rapid eye movement stage 2 sleep, play a crucial role in predicting outcomes for patients with acute disorders of consciousness (ADOC). However, several critical challenges remain in spindle detection: ...