AIMC Topic: Electroencephalography

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SLES: A Novel CNN-based Method for Sensor Reduction in P300 Speller.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
A Brain Computer Interface (BCI) character speller allows human-beings to directly spell characters using eye-gazes, thereby building communication between the human brain and a computer. Current popular BCI character speller systems employ a large n...
Algorithms Self-Help Devices Electroencephalography Language Fixation, Ocular Brain-Computer Interfaces Humans Event-Related Potentials, P300 Neural Networks, Computer
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Reconstructing Degree of Forearm Rotation from Imagined movements for BCI-based Robot Hand Control.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Brain-computer interface (BCI) is an important tool for rehabilitation and control of an external device (e.g., robot arm or home appliances). Fully reconstruction of upper limb movement from brain signals is one of the critical issues for intuitive ...
Electroencephalography Hand Rotation Humans Robotics Forearm Brain-Computer Interfaces Imagination Movement
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Novel Automatic Epilepsy Detection Method Multi-weight Transition 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, 2019
The automatic diagnosis of epilepsy using Electroencephalogram (EEG) signals had always been an important research direction. A novel automatic epilepsy detection method based on multi-weight transition network was proposed in this paper. The epilept...
Seizures Signal Processing, Computer-Assisted Electroencephalography Epilepsy Algorithms Support Vector Machine Humans
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A convolutional neural network based framework for classification of seizure types.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Epileptic seizures are caused by a disturbance in the electrical activity of the brain and classified as many different types of epileptic seizures based on the characteristics of EEG and other parameters. Till now research has been conducted to clas...
Epilepsy Seizures Brain Neural Networks, Computer Humans Electroencephalography Algorithms
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Epileptic States Recognition Using Transfer 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, 2019
Automatic recognition of electroencephalogram (EEG) signals plays a major role in epilepsy diagnosis and assessment. However, the recognition accuracy of conventional methods is usually not satisfactory because of the inconsistent distribution of tra...
Machine Learning Algorithms Electroencephalography Humans Sensitivity and Specificity Epilepsy
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Detection of Epileptic Seizures using Unsupervised Learning Techniques for Feature Extraction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Automatic epileptic seizure prediction from EEG (electroencephalogram) data is a challenging problem. This is due to the complex nature of the signal itself and of the generated abnormalities. In this paper, we investigate several deep network archit...
Epilepsy Seizures Unsupervised Machine Learning Electroencephalography Humans Support Vector Machine
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Extremely Reduced Data Sets Indicate Optimal Stimulation Parameters for Classification in Brain-Computer Interfaces.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
The time between the onset of subsequent auditory or visual stimuli - also known as stimulus onset asynchrony (SOA) - determines many of the event-related potential characteristics of the resulting evoked brain signals. Specifically, the SOA value in...
Electroencephalography Brain-Computer Interfaces Photic Stimulation Decision Making Data Interpretation, Statistical Evoked Potentials Machine Learning Brain Humans
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Improved A-phase Detection of Cyclic Alternating Pattern 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 Jul 1, 2019
In recent years, machine learning algorithms have become increasingly popular for analyzing biomedical signals. This includes the detection of cyclic alternating pattern (CAP) in electroencephalography recordings. Here, we investigate the performance...
Neural Networks, Computer Algorithms Deep Learning Humans Electroencephalography
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2D Wavelet Scalogram Training of Deep Convolutional Neural Network for Automatic Identification of Micro-Scale Sharp Wave Biomarkers in the Hypoxic-Ischemic EEG of Preterm Sheep.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
We have recently demonstrated that micro-scale Sharp waves in the first few hours EEG of asphyxiated preterm fetal sheep models are the reliable prognostic biomarkers for Hypoxic-Ischemic Encephalopathy (HIE). Higher number of sharp waves within the ...
Hypoxia-Ischemia, Brain Signal Processing, Computer-Assisted Sheep Wavelet Analysis Hypothermia, Induced Biomarkers Animals, Newborn Animals Neural Networks, Computer Electroencephalography
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Classification of Perceived Human Stress using Physiological Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals. These include electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG). We co...
Neural Networks, Computer Algorithms Stress, Psychological Humans Electroencephalography Support Vector Machine Bayes Theorem Signal Processing, Computer-Assisted Galvanic Skin Response
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