AIMC Topic: Electroencephalography

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EEG-Based Cross-Subject Driver Drowsiness Recognition With an Interpretable Convolutional Neural Network.

IEEE transactions on neural networks and learning systems
In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still challenging to design a calibration-free system, since EEG signals vary significantly among different subjects and recording sessions. Many efforts have bee...

Quantitative Electroencephalography Objectivity and Reliability in the Diagnosis and Management of Traumatic Brain Injury: A Systematic Review.

Clinical EEG and neuroscience
Persons with a history of traumatic brain injury (TBI) may exhibit short- and long-term cognitive deficits as well as psychiatric symptoms. These symptoms often reflect functional anomalies in the brain that are not detected by standard neuroimaging...

Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach.

Sensors (Basel, Switzerland)
A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drivers. In this research, a new approach was presented in order to detect multi-level driver fatigue. A ...

An ensemble deep-learning approach for single-trial EEG classification of vibration intensity.

Journal of neural engineering
. Single-trial electroencephalography (EEG) classification is a promising approach to evaluate the cognitive experience associated with haptic feedback. Convolutional neural networks (CNNs), which are among the most widely used deep learning techniqu...

A Deep Learning Model for Correlation Analysis between Electroencephalography Signal and Speech Stimuli.

Sensors (Basel, Switzerland)
In recent years, the use of electroencephalography (EEG) has grown as a tool for diagnostic and brain function monitoring, being a simple and non-invasive method compared with other procedures like histological sampling. Typically, in order to extrac...

Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes.

NeuroImage
Electromagnetic source imaging (ESI) offers unique capability of imaging brain dynamics for studying brain functions and aiding the clinical management of brain disorders. Challenges exist in ESI due to the ill-posedness of the inverse problem and th...

Machine learning and EEG can classify passive viewing of discrete categories of visual stimuli but not the observation of pain.

BMC neuroscience
Previous studies have demonstrated the potential of machine learning (ML) in classifying physical pain from non-pain states using electroencephalographic (EEG) data. However, the application of ML to EEG data to categorise the observation of pain ver...

Emotion recognition in EEG signals using deep learning methods: A review.

Computers in biology and medicine
Emotions are a critical aspect of daily life and serve a crucial role in human decision-making, planning, reasoning, and other mental states. As a result, they are considered a significant factor in human interactions. Human emotions can be identifie...

The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG.

Clinical EEG and neuroscience
Common misbehavior among children that prevents them from paying attention to tasks and interacting with their surroundings appropriately is attention-deficit/hyperactivity disorder (ADHD). Studies of children's behavior presently face a significant ...

Automatic identification of schizophrenia employing EEG records analyzed with deep learning algorithms.

Schizophrenia research
Electroencephalography is a method of detecting and analyzing electrical activity in the brain. This electrical activity can be recorded and processed to aid in the clinical diagnosis of mental disorders. In this study, a novel system for classifying...