AIMC Topic: Electroencephalography

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Deep Learning End-to-End Approach for the Prediction of Tinnitus based on EEG Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Tinnitus is attributed by the perception of a sound without any physical source causing the symptom. Symptom profiles of tinnitus patients are characterized by a large heterogeneity, which is a major obstacle in developing general treatments for this...
Tinnitus Sound Humans Brain Deep Learning Electroencephalography
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A Semi-Supervised Few-Shot Learning Model for Epileptic Seizure Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
In the past decade, the rapid development of machine learning has dramatically improved the performance of epileptic detection with Electroencephalography (EEG). However, only a small amount of labeled epileptic data is available for training because...
Supervised Machine Learning Neural Networks, Computer Electroencephalography Epilepsy Seizures Humans
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Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activity.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Machine learning methods, such as deep learning, show promising results in the medical domain. However, the lack of interpretability of these algorithms may hinder their applicability to medical decision support systems. This paper studies an interpr...
Autism Spectrum Disorder Brain Humans Electroencephalography Deep Learning Emotions
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Automatic Detection of EEG Epileptiform Abnormalities in Traumatic Brain Injury using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Traumatic brain injury (TBI) is a sudden injury that causes damage to the brain. TBI can have wide-ranging physical, psychological, and cognitive effects. TBI outcomes include acute injuries, such as contusion or hematoma, as well as chronic sequelae...
Humans Epilepsy, Post-Traumatic Electroencephalography Deep Learning Seizures Brain Injuries, Traumatic
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Insights of 3D Input CNN in EEG-based Emotion Recognition.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Electroencephalogram (EEG) signals have shown to be a good source of information for emotion recognition algorithms in Human-Brain interaction applications. In this paper, a reproducible framework is proposed for classifying human emotions based on E...
Electroencephalography Brain Emotions Humans Neural Networks, Computer Algorithms
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LightSleepNet: A Lightweight Deep Model for Rapid Sleep Stage Classification with Spectrograms.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Deep learning has achieved unprecedented success in sleep stage classification tasks, which starts to pave the way for potential real-world applications. However, due to its enormous size, deployment of deep neural networks is hindered by high cost a...
Neural Networks, Computer Sleep Sleep Stages Deep Learning Electroencephalography
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[An anesthesia depth computing method study based on wavelet transform and artificial neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Oct 25, 2021
General anesthesia is an essential part of surgery to ensure the safety of patients. Electroencephalogram (EEG) has been widely used in anesthesia depth monitoring for abundant information and the ability of reflecting the brain activity. The paper p...
Humans Neural Networks, Computer Electroencephalography Wavelet Analysis Algorithms Anesthesia, General
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Towards Interpretable Machine Learning in EEG Analysis.

Studies in health technology and informatics Sep 21, 2021
In this paper a machine learning model for automatic detection of abnormalities in electroencephalography (EEG) is dissected into parts, so that the influence of each part on the classification accuracy score can be examined. The most successful setu...
Signal Processing, Computer-Assisted Wavelet Analysis Support Vector Machine Electroencephalography Neural Networks, Computer Algorithms Machine Learning
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Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning.

Mathematical biosciences and engineering : MBE Aug 20, 2021
Most studies on drug addiction degree are made based on statistical scales, addicts' account, and subjective judgement of rehabilitation doctors. No objective, quantified evaluation has been made. This paper uses devises the synchronous bimodal signa...
Algorithms Substance-Related Disorders Electroencephalography Neural Networks, Computer Humans Machine Learning
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Histograms of Frequency-Intensity Distribution Deep Learning to Predict the Seizure Liability of Drugs in Electroencephalography.

Toxicological sciences : an official journal of the Society of Toxicology Aug 3, 2021
Detection of seizures as well as that of seizure auras is effective in improving the predictive accuracy of seizure liability of drugs. Whereas electroencephalography has been known to be effective for the detection of seizure liability, no establish...
Rats Artificial Intelligence Electroencephalography Animals Seizures Deep Learning Pharmaceutical Preparations
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