AIMC Topic: Electroencephalography

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EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels.

Sensors (Basel, Switzerland) Jul 27, 2021
Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data have been drawing attention thanks to their capability in countering the effect of deceptive external expressions of humans, like faces or speeches. Emotion recognit...
Electrodes Emotions Humans Arousal Electroencephalography Neural Networks, Computer
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FLDNet: Frame-Level Distilling Neural Network for EEG Emotion Recognition.

IEEE journal of biomedical and health informatics Jul 27, 2021
Based on the current research on EEG emotion recognition, there are some limitations, such as hand-engineered features, redundant and meaningless signal frames and the loss of frame-to-frame correlation. In this paper, a novel deep learning framework...
Neural Networks, Computer Emotions Electroencephalography Humans
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Predicting subclinical psychotic-like experiences on a continuum using machine learning.

NeuroImage Jul 22, 2021
Previous studies applying machine learning methods to psychosis have primarily been concerned with the binary classification of chronic schizophrenia patients and healthy controls. The aim of this study was to use electroencephalographic (EEG) data a...
Auditory Perception Electroencephalography Humans Adult Acoustic Stimulation Psychotic Disorders Adolescent Female Male Machine Learning Asymptomatic Diseases Proof of Concept Study Predictive Value of Tests Young Adult
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A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram.

Journal of neural engineering Jul 21, 2021
Interictal epileptiform discharges (IEDs) are an important and widely accepted biomarker used in the diagnosis of epilepsy based on scalp electroencephalography (EEG). Because the visual detection of IEDs has various limitations, including high time ...
Electroencephalography Epilepsy Humans Deep Learning Scalp
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Automated Annotation of Epileptiform Burden and Its Association with Outcomes.

Annals of neurology Jul 20, 2021
OBJECTIVE: This study was undertaken to determine the dose-response relation between epileptiform activity burden and outcomes in acutely ill patients.
Humans Female Aged Male Artificial Intelligence Retrospective Studies Seizures Cost of Illness Treatment Outcome Middle Aged Electroencephalography Cohort Studies
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End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax.

Journal of neural engineering Jul 20, 2021
To develop an efficient, embedded electroencephalogram (EEG) channel selection approach for deep neural networks, allowing us to match the channel selection to the target model, while avoiding the large computational burdens of wrapper approaches in ...
Electroencephalography Attention Neural Networks, Computer
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Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network.

Sensors (Basel, Switzerland) Jul 19, 2021
The paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spiking Neural Networks (SNN) architecture that enhances the model's explainability while learning from streaming spatiotemporal brain data (STBD) in an inc...
Neural Networks, Computer Brain Deep Learning Electroencephalography Humans Neurons
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A deep learning algorithm for sleep stage scoring in mice based on a multimodal network with fine-tuning technique.

Neuroscience research Jul 17, 2021
Sleep stage scoring is important to determine sleep structure in preclinical and clinical research. The aim of this study was to develop an automatic sleep stage classification system for mice with a new deep neural network algorithm. For the purpose...
Sleep Stages Electroencephalography Sleep Deep Learning Mice Algorithms Animals
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Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.

International journal of neural systems Jul 16, 2021
Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free ...
Scalp Electroencephalography Epilepsy Humans Neural Networks, Computer Deep Learning
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Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications.

Sensors (Basel, Switzerland) Jul 6, 2021
The utilization of robotic systems has been increasing in the last decade. This increase has been derived by the evolvement in the computational capabilities, communication systems, and the information systems of the manufacturing systems which is re...
Electroencephalography Automation Industry Emotions Robotics Humans
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