AIMC Topic: Electroencephalography

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Of Pilots and Copilots: The Evolving Role of Artificial Intelligence in Clinical Neurophysiology.

The Neurodiagnostic journal Feb 25, 2025
Artificial intelligence (AI) is revolutionizing clinical neurophysiology (CNP), particularly in its applications to electroencephalography (EEG), electromyography (EMG), and polysomnography (PSG). AI enhances diagnostic accuracy and efficiency while ...
Neurophysiology Electroencephalography Electromyography Polysomnography Humans Artificial Intelligence
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Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification.

Sensors (Basel, Switzerland) Feb 25, 2025
Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (MI) serving as a key paradigm for decoding imagined movements. Efficient feature extraction from raw ...
Signal Processing, Computer-Assisted Humans Movement Neural Networks, Computer Brain-Computer Interfaces Imagination Algorithms Electroencephalography
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Electroencephalogram (EEG) Based Fuzzy Logic and Spiking Neural Networks (FLSNN) for Advanced Multiple Neurological Disorder Diagnosis.

Brain topography Feb 24, 2025
Neurological disorders are a major global health concern that have a substantial impact on death rates and quality of life. accurately identifying a number of diseases Due to inherent data uncertainties and Electroencephalogram (EEG) pattern overlap,...
Neural Networks, Computer Algorithms Brain Fuzzy Logic Electroencephalography Signal Processing, Computer-Assisted Humans Nervous System Diseases
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Unsupervised learning from EEG data for epilepsy: A systematic literature review.

Artificial intelligence in medicine Feb 21, 2025
BACKGROUND AND OBJECTIVES: Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, whose neurophysiological signature is altered electroencephalographic (EEG) activity. The use of artificial intelligence (AI) methods on EEG...
Humans Epilepsy Algorithms Unsupervised Machine Learning Electroencephalography
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Machine learning based seizure classification and digital biosignal analysis of ECT seizures.

Scientific reports Feb 21, 2025
While artificial intelligence has received considerable attention in various medical fields, its application in the field of electroconvulsive therapy (ECT) remains rather limited. With the advent of digital seizure collection systems, the developmen...
Electroconvulsive Therapy Adult Male Middle Aged Electroencephalography Seizures Signal Processing, Computer-Assisted Machine Learning Humans Female Aged
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System for Predicting Neurological Outcomes Following Cardiac Arrest Based on Clinical Predictors Using a Machine Learning Method: The Neurological Outcomes After Cardiac Arrest Method.

Neurocritical care Feb 20, 2025
BACKGROUND: A multimodal approach may prove effective for predicting clinical outcomes following cardiac arrest (CA). We aimed to develop a practical predictive model that incorporates clinical factors related to CA and multiple prognostic tests usin...
Female Aged Male Machine Learning Prognosis Heart Arrest Glasgow Coma Scale Middle Aged Electroencephalography Outcome Assessment, Health Care Humans
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Geometric neural network based on phase space for BCI-EEG decoding.

Journal of neural engineering Feb 19, 2025
The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision. This is particularly true for Brain-computer interface (BCI), where the brain activity ...
Male Deep Learning Electroencephalography Brain-Computer Interfaces Neural Networks, Computer Algorithms Adult Humans
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A novel deep learning model combining 3DCNN-CapsNet and hierarchical attention mechanism for EEG emotion recognition.

Neural networks : the official journal of the International Neural Network Society Feb 18, 2025
Emotion recognition plays a key role in the field of human-computer interaction. Classifying and predicting human emotions using electroencephalogram (EEG) signals has consistently been a challenging research area. Recently, with the increasing appli...
Electroencephalography Attention Humans Deep Learning Neural Networks, Computer Emotions
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Predicting Treatment Response of Repetitive Transcranial Magnetic Stimulation in Major Depressive Disorder Using an Explainable Machine Learning Model Based on Electroencephalography and Clinical Features.

Biological psychiatry. Cognitive neuroscience and neuroimaging Feb 18, 2025
Major depressive disorder (MDD) is highly heterogeneous in response to repetitive transcranial magnetic stimulation (rTMS), and identifying predictive biomarkers is essential for personalized treatment. However, most prior research studies have used ...
Humans Depressive Disorder, Major Treatment Outcome Support Vector Machine Electroencephalography Transcranial Magnetic Stimulation Machine Learning Adult Female Male Middle Aged
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Machine learning classification of active viewing of pain and non-pain images using EEG does not exceed chance in external validation samples.

Cognitive, affective & behavioral neuroscience Feb 18, 2025
Previous research has demonstrated that machine learning (ML) could not effectively decode passive observation of neutral versus pain photographs by using electroencephalogram (EEG) data. Consequently, the present study explored whether active viewin...
Young Adult Adult Male Machine Learning Female Evoked Potentials Facial Recognition Pain Perception Electroencephalography Humans Pain Facial Expression
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