AIMC Topic: Electroencephalography

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Brain Age Prediction/Classification through Recurrent Deep Learning with Electroencephalogram Recordings of Seizure Subjects.

Sensors (Basel, Switzerland) Oct 23, 2022
With modern population growth and an increase in the average lifespan, more patients are becoming afflicted with neurodegenerative diseases such as dementia and Alzheimer's. Patients with a history of epilepsy, drug abuse, and mental health disorders...
Brain Deep Learning Child Electroencephalography Humans Seizures Alzheimer Disease
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Considerate motion imagination classification method using deep learning.

PloS one Oct 20, 2022
In order to improve the classification accuracy of motion imagination, a considerate motion imagination classification method using deep learning is proposed. Specifically, based on a graph structure suitable for electroencephalography as input, the ...
Brain-Computer Interfaces Imagination Deep Learning Electroencephalography Algorithms
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A deep learning based model using RNN-LSTM for the Detection of Schizophrenia from EEG data.

Computers in biology and medicine Oct 19, 2022
Normal life can be ensured for schizophrenic patients if diagnosed early. Electroencephalogram (EEG) carries information about the brain network connectivity which can be used to detect brain anomalies that are indicative of schizophrenia. Since deep...
Algorithms Electroencephalography Deep Learning Schizophrenia Humans Machine Learning
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Deep learning for automated epileptiform discharge detection from scalp EEG: A systematic review.

Journal of neural engineering Oct 19, 2022
Automated interictal epileptiform discharge (IED) detection has been widely studied, with machine learning methods at the forefront in recent years. As computational resources become more accessible, researchers have applied deep learning (DL) to IED...
Scalp Neural Networks, Computer Reproducibility of Results Deep Learning Electroencephalography
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Detecting the locus of auditory attention based on the spectro-spatial-temporal analysis of EEG.

Journal of neural engineering Oct 17, 2022
. Auditory attention decoding (AAD) determines which speaker the listener is focusing on by analyzing his/her EEG. Convolutional neural network (CNN) was adopted to extract spectro-spatial-feature (SSF) from short-time-interval of EEG to detect audit...
Humans Neural Networks, Computer Male Electroencephalography Female
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Automated Analysis of Sleep Study Parameters Using Signal Processing and Artificial Intelligence.

International journal of environmental research and public health Oct 14, 2022
An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as other signal processing applications. Therefore, the denoising of the contaminated signals is inevitable to ensure a reliable analysis of the EEG sign...
Signal Processing, Computer-Assisted Sleep Sleep Stages Algorithms Electroencephalography Artificial Intelligence
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Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network.

Computational intelligence and neuroscience Oct 13, 2022
Humans experience a variety of emotions throughout the course of their daily lives, including happiness, sadness, and rage. As a result, an effective emotion identification system is essential for electroencephalography (EEG) data to accurately refle...
Electroencephalography Humans Neural Networks, Computer Entropy Emotions Receptor for Advanced Glycation End Products
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Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks.

IEEE transactions on biomedical circuits and systems Oct 12, 2022
During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal...
Oxides Seizures Electroencephalography Epilepsy Humans Neural Networks, Computer
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Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis.

Sensors (Basel, Switzerland) Oct 7, 2022
An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronoun...
Electroencephalography Signal Processing, Computer-Assisted Support Vector Machine Discriminant Analysis Artifacts Brain-Computer Interfaces Algorithms
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Leveraging Deep Learning Techniques to Improve P300-Based Brain Computer Interfaces.

IEEE journal of biomedical and health informatics Oct 4, 2022
Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain and an external device. One of the most popular protocols for BCI is based on the extraction of the so-called P300 wave from electroencephalography (EEG...
Neural Networks, Computer Algorithms Humans Brain-Computer Interfaces Event-Related Potentials, P300 Deep Learning Electroencephalography
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