AIMC Topic: Electroencephalography
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Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury.
Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics
Jan 23, 2025
Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Characteristic EEG changes have been identified in diverse neurologic conditions including stroke, trauma, and anoxia, and the increased utilization of co...
Eeg Microstates and Balance Parameters for Stroke Discrimination: A Machine Learning Approach.
Brain topography
Jan 22, 2025
Electroencephalography microstates (EEG-MS) show promise to be a neurobiological biomarker in stroke. Thus, the aim of the study was to identify biomarkers to discriminate stroke patients from healthy individuals based on EEG-MS and clinical features...
[Application and considerations of artificial intelligence and neuroimaging in the study of brain effect mechanisms of acupuncture and moxibustion].
Zhongguo zhen jiu = Chinese acupuncture & moxibustion
Jan 21, 2025
Electroencephalography (EEG) and magnetic resonance imaging (MRI), as neuroimaging technologies, provided objective and visualized technical tools for analyzing the brain effect mechanisms of acupuncture and moxibustion from the perspectives of brain...
TFTL: A Task-Free Transfer Learning Strategy for EEG-Based Cross-Subject and Cross-Dataset Motor Imagery BCI.
IEEE transactions on bio-medical engineering
Jan 21, 2025
OBJECTIVE: Motor imagery-based brain-computer interfaces (MI-BCIs) have been playing an increasingly vital role in neural rehabilitation. However, the long-term task-based calibration required for enhanced model performance leads to an unfriendly use...
Enhancing Domain Diversity of Transfer Learning-Based SSVEP-BCIs by the Reconstruction of Channel Correlation.
IEEE transactions on bio-medical engineering
Jan 21, 2025
OBJECTIVE: The application of transfer learning, specifically pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has been demonstrated to effectively improve the classification perform...
Deep Clustering for Epileptic Seizure Detection.
IEEE transactions on bio-medical engineering
Jan 21, 2025
UNLABELLED: Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, which are often unpredictable and increase mortality and morbidity risks.
Model-agnostic meta-learning for EEG-based inter-subject emotion recognition.
Journal of neural engineering
Jan 21, 2025
. Developing an efficient and generalizable method for inter-subject emotion recognition from neural signals is an emerging and challenging problem in affective computing. In particular, human subjects usually have heterogeneous neural signal charact...
Machine Learning-Based Diagnosis of Chronic Subjective Tinnitus With Altered Cognitive Function: An Event-Related Potential Study.
Ear and hearing
Jan 20, 2025
OBJECTIVES: Due to the absence of objective diagnostic criteria, tinnitus diagnosis primarily relies on subjective assessments. However, its neuropathological features can be objectively quantified using electroencephalography (EEG). Despite the exis...
Diagnosing Epilepsy with Normal Interictal EEG Using Dynamic Network Models.
Annals of neurology
Jan 16, 2025
OBJECTIVE: Whereas a scalp electroencephalogram (EEG) is important for diagnosing epilepsy, a single routine EEG is limited in its diagnostic value. Only a small percentage of routine EEGs show interictal epileptiform discharges (IEDs) and overall mi...
Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.
PloS one
Jan 16, 2025
In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, de...
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