AIMC Topic: Electroencephalography

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Automatic Sleep Stage Classification using Marginal Hilbert Spectrum Features and a Convolutional Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
In this paper, we propose a novel method of automatic sleep stage classification based on single-channel electroencephalography (EEG). First, we use marginal Hilbert spectrum (MHS) to depict time-frequency domain features of five sleep stages of 30-s...
Humans Sleep Stages Neural Networks, Computer Electroencephalography Sleep
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Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
This paper introduces a simple approach combining deep learning and histogram contour processing for automatic detection of various types of artifact contaminating the raw electroencephalogram (EEG). The proposed method considers both spatial and tem...
Artifacts Humans Deep Learning Reproducibility of Results Electroencephalography
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EEG-based Depression Detection Using Convolutional Neural Network with Demographic Attention Mechanism.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Electroencephalography (EEG)-based depression detection has become a hot topic in the development of biomedical engineering. However, the complexity and nonstationarity of EEG signals are two biggest obstacles to this application. In addition, the ge...
Humans Neural Networks, Computer Depression Electroencephalography Attention Demography
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Machine Learning with Imbalanced EEG Datasets using Outlier-based Sampling.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Epilepsy is a neurological disorder which causes seizures in over 65 million people worldwide. Recently developed implantable therapeutic devices aim to prevent symptoms by applying acute electrical stimulation to the seizure-generating brain region ...
Seizures Machine Learning Humans Algorithms Electroencephalography Epilepsy
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Emotion Recognition with Refined Labels for Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
The traditional emotion classification framework usually fits all the features segments of the same trial to a fixed annotation. Considering the fact that emotion is a reaction to stimuli that lasts for varied periods, we argue that the indiscriminat...
Deep Learning Emotions Electroencephalography Seizures Humans Neural Networks, Computer
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Deep transfer learning for improving single-EEG arousal detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two deep transfer learning strategies for overcoming the channel mismatch problem for cases where two d...
Machine Learning Deep Learning Neural Networks, Computer Electroencephalography Arousal
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Automatic Identification of Brain Independent Components in Electroencephalography Data Collected while Standing in a Virtually Immersive Environment - A Deep Learning-Based Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Electroencephalography (EEG) is a commonly used method for monitoring brain activity. Automating an EEG signal processing pipeline is imperative to the exploration of real-time brain computer interface (BCI) applications. EEG analysis demands substan...
Algorithms Brain Electroencephalography Signal Processing, Computer-Assisted Deep Learning
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Partial directed coherence based graph convolutional neural networks for driving fatigue detection.

The Review of scientific instruments Jul 1, 2020
The mental state of a driver can be accurately and reliably evaluated by detecting the driver's electroencephalogram (EEG) signals. However, traditional machine learning and deep learning methods focus on the single electrode feature analysis and ign...
Computer Graphics Electrodes Automobile Driving Humans Neural Networks, Computer Electroencephalography Signal Processing, Computer-Assisted
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Automated human mind reading using EEG signals for seizure detection.

Journal of medical engineering & technology Jul 1, 2020
Epilepsy is one of the most occurring neurological disease globally emerged back in 4000 BC. It is affecting around 50 million people of all ages these days. The trait of this disease is recurrent seizures. In the past few decades, the treatments ava...
Humans Electroencephalography Seizures Neural Networks, Computer Brain
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Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG Statistics.

Neuroinformatics Jun 1, 2020
Electro- and magneto-encephalography are functional neuroimaging modalities characterised by their ability to quantify dynamic spatiotemporal activity within the brain. However, the visualisation techniques used to illustrate these effects are curren...
Magnetoencephalography Cross-Sectional Studies Brain Mapping Electroencephalography Machine Learning Signal Processing, Computer-Assisted Humans Brain
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