AIMC Topic: Electroencephalography

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Analysis of the impact of deep learning know-how and data in modelling neonatal EEG.

Scientific reports Nov 14, 2024
The performance gains achieved by deep learning models nowadays are mainly attributed to the usage of ever larger datasets. In this study, we present and contrast the performance gains that can be achieved via accessing larger high-quality datasets v...
Humans Infant, Newborn Deep Learning Electroencephalography Seizures
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Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A comprehensive review of the last decade (2013 - 2024).

Computer methods and programs in biomedicine Nov 12, 2024
BACKGROUND AND OBJECTIVES: Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) are progressive neurological disorders that significantly impair the cognitive functions, memory, and daily activities. They affect millions of individuals worldw...
Alzheimer Disease Deep Learning Humans Cognitive Dysfunction Diagnosis, Computer-Assisted Electroencephalography
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Recognizing and explaining driving stress using a Shapley additive explanation model by fusing EEG and behavior signals.

Accident; analysis and prevention Nov 12, 2024
Driving stress is a critical factor leading to road traffic accidents. Despite numerous studies that have been conducted on driving stress recognition, most of them only focus on accuracy improvement without taking model interpretability into account...
Automobile Driving Electroencephalography Accidents, Traffic Algorithms Brain Female Stress, Psychological Humans Fuzzy Logic Machine Learning Young Adult Decision Trees Adult Male
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Expert level of detection of interictal discharges with a deep neural network.

Epilepsia Nov 12, 2024
OBJECTIVE: Deep learning methods have shown potential in automating the detection of interictal epileptiform discharges (IEDs) in electroencephalography (EEG). We compared IED detection using our previously trained deep neural network with a group of...
Humans Sensitivity and Specificity Neural Networks, Computer Male Electroencephalography Female Epilepsy Deep Learning
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Predicting the Risk of Driving Under the Influence of Alcohol Using EEG-Based Machine Learning.

Computers in biology and medicine Nov 11, 2024
Driving under the influence of alcohol (DUIA) is closely associated with alcohol use disorder (AUD). Our previous study on machine learning (ML) algorithms revealed a very high accuracy of decision trees with neuropsychological features in predicting...
Algorithms Female Adult Male Middle Aged Humans Signal Processing, Computer-Assisted Electroencephalography Machine Learning Alcoholism Driving Under the Influence
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Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.

PLoS computational biology Nov 11, 2024
To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and so on. A promising approach to explaining this process has been to model electroencephalogram (EEG) ...
Attention Speech Perception Male Deep Learning Speech Computational Biology Models, Neurological Electroencephalography Female Young Adult Adult Brain Language Humans
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QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals.

Scientific reports Nov 9, 2024
The most cost-effective data collection method is electroencephalography (EEG), which obtains meaningful information about the brain. Therefore, EEG signal processing is crucial for neuroscience and machine learning (ML). Therefore, a new EEG stress ...
Humans Machine Learning Algorithms Male Stress, Psychological Electroencephalography Brain Female Adult Signal Processing, Computer-Assisted
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A hybrid local-global neural network for visual classification using raw EEG signals.

Scientific reports Nov 8, 2024
EEG-based brain-computer interfaces (BCIs) have the potential to decode visual information. Recently, artificial neural networks (ANNs) have been used to classify EEG signals evoked by visual stimuli. However, methods using ANNs to extract features f...
Brain-Computer Interfaces Photic Stimulation Humans Algorithms Neural Networks, Computer Electroencephalography Signal Processing, Computer-Assisted
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Anchoring temporal convolutional networks for epileptic seizure prediction.

Journal of neural engineering Nov 8, 2024
. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigate their impact or prevent them altogether. Current studies predominantly focus on short-term seizure predictions, which causes the prediction time to ...
Electroencephalography Humans Epilepsy Neural Networks, Computer Seizures Algorithms Time Factors
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Depression diagnosis: EEG-based cognitive biomarkers and machine learning.

Behavioural brain research Nov 6, 2024
Depression is a complex mental illness that has significant effects on people as well as society. The traditional techniques for the diagnosis of depression, along with the potential of nascent biomarkers especially EEG-based biomarkers, are studied....
Depression Cognition Electroencephalography Machine Learning Depressive Disorder Humans Biomarkers Brain
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