AIMC Topic: Electroencephalography

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Investigating mental workload caused by NDRTs in highly automated driving with deep learning.

Traffic injury prevention Jan 19, 2024
OBJECTIVE: This study aimed to examine the impact of non-driving-related tasks (NDRTs) on drivers in highly automated driving scenarios and sought to develop a deep learning model for classifying mental workload using electroencephalography (EEG) sig...
Deep Learning Electroencephalography Accidents, Traffic Automobile Driving Workload Task Performance and Analysis Humans
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Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 19, 2024
Automatic identification of visual learning style in real time using raw electroencephalogram (EEG) is challenging. In this work, inspired by the powerful abilities of deep learning techniques, deep learning-based models are proposed to learn high-le...
Electroencephalography Artifacts Machine Learning Deep Learning Humans Neural Networks, Computer
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Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain-computer interface.

Journal of neural engineering Jan 17, 2024
Motor imagery (MI) brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been developed primarily for stroke rehabilitation, however, due to limited stroke data, current deep learning methods for cross-subject classification rely ...
Imagination Imagery, Psychotherapy Healthy Volunteers Brain-Computer Interfaces Humans Electroencephalography Deep Learning Algorithms Stroke
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Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice.

Sleep medicine reviews Jan 11, 2024
Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence tech...
Sleep Stages Electroencephalography Artificial Intelligence Sleep Humans Algorithms Deep Learning
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Evaluating Deep Learning Performance for P300 Neural Signal Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 11, 2024
P300 event-related potential (ERP) signals are useful neurological biomarkers, and their accurate classification is important when studying the cognitive functions in patients with neurological disorders. While many studies have proposed models for c...
Electroencephalography Brain-Computer Interfaces Event-Related Potentials, P300 Algorithms Deep Learning Humans Machine Learning
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Deep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series.

Journal of neuroscience methods Jan 10, 2024
BACKGROUND: Individuals in the early stages of Alzheimer's Disease (AD) are typically diagnosed with Mild Cognitive Impairment (MCI). MCI represents a transitional phase between normal cognitive function and AD. Electroencephalography (EEG) records c...
Neural Networks, Computer Time Factors Humans Alzheimer Disease Deep Learning Electroencephalography Cognitive Dysfunction
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The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization.

IEEE transactions on biomedical circuits and systems Jan 10, 2024
For depression diagnosis, traditional methods such as interviews and clinical scales have been widely leveraged in the past few decades, but they are subjective, time-consuming, and labor-consuming. With the development of affective computing and Art...
Electroencephalography Signal Processing, Computer-Assisted Humans Signal-To-Noise Ratio Algorithms Artificial Intelligence Depression
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Application of Hybrid DeepLearning Architectures for Identification of Individuals with Obsessive Compulsive Disorder Based on EEG Data.

Clinical EEG and neuroscience Jan 9, 2024
Obsessive-compulsive disorder (OCD) is a highly common psychiatric disorder. The symptoms of this condition overlap and co-occur with those of other psychiatric illnesses, making diagnosis difficult. The availability of biomarkers could be useful fo...
Middle Aged Deep Learning Adult Male Electroencephalography Obsessive-Compulsive Disorder Humans Neural Networks, Computer Female Young Adult Brain
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Towards Real-Time Sleep Stage Prediction and Online Calibration Based on Architecturally Switchable Deep Learning Models.

IEEE journal of biomedical and health informatics Jan 4, 2024
Despite the recent advances in automatic sleep staging, few studies have focused on real-time sleep staging to promote the regulation of sleep or the intervention of sleep disorders. In this paper, a novel network named SwSleepNet, that can handle bo...
Humans Sleep Calibration Sleep Stages Deep Learning Electroencephalography
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EEG-BCI-based motor imagery classification using double attention convolutional network.

Computer methods in biomechanics and biomedical engineering Jan 2, 2024
This article aims to improve and diversify signal processing techniques to execute a brain-computer interface (BCI) based on neurological phenomena observed when performing motor tasks using motor imagery (MI). The noise present in the original data,...
Humans Neural Networks, Computer Algorithms Brain-Computer Interfaces Imagination Electroencephalography Signal Processing, Computer-Assisted
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