AIMC Topic: Electroencephalography

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Toward calibration-free motor imagery brain-computer interfaces: a VGG-based convolutional neural network and WGAN approach.

Journal of neural engineering Jul 31, 2024
Motor imagery (MI) represents one major paradigm of Brain-computer interfaces (BCIs) in which users rely on their electroencephalogram (EEG) signals to control the movement of objects. However, due to the inter-subject variability, MI BCIs require re...
Calibration Brain-Computer Interfaces Movement Male Deep Learning Electroencephalography Imagination Humans Young Adult Adult Neural Networks, Computer Female
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Multitask learning of a biophysically-detailed neuron model.

PLoS computational biology Jul 31, 2024
The human brain operates at multiple levels, from molecules to circuits, and understanding these complex processes requires integrated research efforts. Simulating biophysically-detailed neuron models is a computationally expensive but effective meth...
Machine Learning Brain Computer Simulation Action Potentials Electroencephalography Computational Biology Models, Neurological Humans Neural Networks, Computer Neurons Magnetoencephalography Membrane Potentials
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Automated detection of tonic seizures using wearable movement sensor and artificial neural network.

Epilepsia Jul 30, 2024
Although several validated wearable devices are available for detection of generalized tonic-clonic seizures, automated detection of tonic seizures is still a challenge. In this phase 1 study, we report development and validation of an artificial neu...
Seizures Accelerometry Movement Child, Preschool Child Electroencephalography Female Young Adult Adult Adolescent Wearable Electronic Devices Male Middle Aged Humans Neural Networks, Computer Prospective Studies
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SQI-DOANet: electroencephalogram-based deep neural network for estimating signal quality index and depth of anaesthesia.

Journal of neural engineering Jul 30, 2024
. Monitoring the depth of anaesthesia (DOA) during surgery is of critical importance. However, during surgery electroencephalography (EEG) is usually subject to various disturbances that affect the accuracy of DOA. Therefore, accurately estimating no...
Deep Learning Electroencephalography Signal Processing, Computer-Assisted Anesthesia Humans Neural Networks, Computer
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DGSD: Dynamical graph self-distillation for EEG-based auditory spatial attention detection.

Neural networks : the official journal of the International Neural Network Society Jul 26, 2024
Auditory Attention Detection (AAD) aims to detect the target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional conv...
Auditory Perception Electroencephalography Attention Humans Acoustic Stimulation Neural Networks, Computer Algorithms Brain
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Detection of Unfocused EEG Epochs by the Application of Machine Learning Algorithm.

Sensors (Basel, Switzerland) Jul 25, 2024
Electroencephalography (EEG) is a non-invasive method used to track human brain activity over time. The time-locked EEG to an external event is known as event-related potential (ERP). ERP can be a biomarker of human perception and other cognitive pro...
Adult Signal Processing, Computer-Assisted Humans Brain Evoked Potentials Male Female Machine Learning Electroencephalography Young Adult Algorithms
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Study on the classification of sleep stages in EEG signals based on DoubleLinkSleepCLNet.

Sleep & breathing = Schlaf & Atmung Jul 24, 2024
PURPOSE: The classification of sleep stages based on Electroencephalogram (EEG) changes has significant implications for evaluating sleep quality and sleep status. Most polysomnography (PSG) systems have a limited number of channels and do not achiev...
Humans Polysomnography Sleep Stages Deep Learning Electroencephalography Signal Processing, Computer-Assisted Neural Networks, Computer Adult Male
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Adaptive node feature extraction in graph-based neural networks for brain diseases diagnosis using self-supervised learning.

NeuroImage Jul 24, 2024
Electroencephalography (EEG) has demonstrated significant value in diagnosing brain diseases. In particular, brain networks have gained prominence as they offer additional valuable insights by establishing connections between EEG signal channels. Whi...
Signal Processing, Computer-Assisted Supervised Machine Learning Brain Diseases Humans Neural Networks, Computer Brain Male Electroencephalography Female Adult
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How the Degree of Anthropomorphism of Human-like Robots Affects Users' Perceptual and Emotional Processing: Evidence from an EEG Study.

Sensors (Basel, Switzerland) Jul 24, 2024
Anthropomorphized robots are increasingly integrated into human social life, playing vital roles across various fields. This study aimed to elucidate the neural dynamics underlying users' perceptual and emotional responses to robots with varying leve...
Adult Male Emotions Brain Humans Robotics Female Electroencephalography Young Adult Evoked Potentials
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Insights from EEG analysis of evoked memory recalls using deep learning for emotion charting.

Scientific reports Jul 24, 2024
Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during...
Humans Female Young Adult Emotions Electroencephalography Mental Recall Adult Male Deep Learning
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