AIMC Topic: Electroencephalography

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Continuous real-time detection and management of comprehensive mental states using wireless soft multifunctional bioelectronics.

Biosensors & bioelectronics
Quantitatively measuring human mental states that profoundly affect cognition, behavior, and recovery would revolutionize personalized digital healthcare. Detecting fatigue, stress, and sleep is particularly important due to their interdependence: pe...

Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis.

Scientific reports
The analysis of cognitive patterns through brain signals offers critical insights into human cognition, including perception, attention, memory, and decision-making. However, accurately classifying these signals remains a challenge due to their inher...

Understanding the Spatio-Temporal Coupling of Spikes and Spindles in Focal Epilepsy Through a Network-Level Computational Model.

International journal of neural systems
The electrophysiological findings have shown that epileptiform spikes triggering sleep spindles within 1[Formula: see text]s across multiple channels are commonly observed during sleep in focal epilepsy (FE). Such spatio-temporal couplings of spikes ...

Multimodal machine learning for deception detection using behavioral and physiological data.

Scientific reports
Deception detection is crucial in domains like national security, privacy, judiciary, and courtroom trials. Differentiating truth from lies is inherently challenging due to many complex, diversified behavioural, physiological and cognitive aspects. T...

A Novel Fusion Framework Combining Graph Embedding Class-Based Convolutional Recurrent Attention Network with Brown Bear Optimization Algorithm for EEG-Based Parkinson's Disease Recognition.

Journal of molecular neuroscience : MN
Parkinson's disease recognition (PDR) involves identifying Parkinson's disease using clinical evaluations, imaging studies, and biomarkers, focusing on early symptoms like tremors, rigidity, and bradykinesia to facilitate timely treatment. However, d...

Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion.

Scientific reports
Many traffic accidents occur nowadays as a result of drivers not paying enough attention or being vigilant. We call this driver sleepiness. This results in numerous unfavourable circumstances that negatively impact people's life. The identification o...

Machine Learning-Based localization of the epileptogenic zone using High-Frequency oscillations from SEEG: A Real-World approach.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
INTRODUCTION: Localizing the epileptogenic zone (EZ) using Stereo EEG (SEEG) is often challenging through manual analysis. Even methods based on signal analysis have limitations in identifying the EZ, particularly in patients with neocortical epileps...

Dual-pathway EEG model with channel attention for virtual reality motion sickness detection.

Journal of neuroscience methods
BACKGROUND: Motion sickness has been a key factor affecting user experience in Virtual Reality (VR) and limiting the development of the VR industry. Accurate detection of Virtual Reality Motion Sickness (VRMS) is a prerequisite for solving the proble...

Explainable multiscale temporal convolutional neural network model for sleep stage detection based on electroencephalogram activities.

Journal of neural engineering
Humans spend a significant portion of their lives in sleep (an essential driver of body metabolism). Moreover, as sleep deprivation could cause various health complications, it is crucial to develop an automatic sleep stage detection model to facilit...

The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvements over conventional statistical techniques. However, deep learning models can underperform if tra...