AIMC Topic: Electroencephalography

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Significance of gender, brain region and EEG band complexity analysis for Parkinson's disease classification using recurrence plots and machine learning algorithms.

Physical and engineering sciences in medicine
Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogram (EEG) plays an important role in diagnosing PD as it offers a non-invasive continuous assessment ...

Beyond averaging: A transformer approach to decoding event related brain potentials.

NeuroImage
The objective of this study is to assess the potential of a transformer-based deep learning approach applied to event-related brain potentials (ERPs) derived from electroencephalographic (EEG) data. Traditional methods involve averaging the EEG signa...

Machine learning-based algorithm of drug-resistant prediction in newly diagnosed patients with temporal lobe epilepsy.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVES: To develop a predicted algorithm for drug-resistant epilepsy (DRE) in newly diagnosed temporal lobe epilepsy (TLE) patients.

The 'Sandwich' meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding.

Journal of neural engineering
. Machine learning has enhanced the performance of decoding signals indicating human behaviour. Electroencephalography (EEG) brainwave decoding, as an exemplar indicating neural activity and human thoughts non-invasively, has been helpful in neural a...

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
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
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
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
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
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
UNLABELLED: Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, which are often unpredictable and increase mortality and morbidity risks.