AIMC Topic: Electroencephalography

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Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing.

Artificial intelligence in medicine Feb 19, 2020
BACKGROUND AND OBJECTIVE: Multimodal data analysis and large-scale computational capability is entering medicine in an accelerative fashion and has begun to influence investigational work in a variety of disciplines. It is also informing us of therap...
Brain Humans Deep Learning Data Analysis Electroencephalography Epilepsy Magnetic Resonance Imaging
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Mental state space visualization for interactive modeling of personalized BCI control strategies.

Journal of neural engineering Feb 18, 2020
OBJECTIVE: Numerous studies in the area of BCI are focused on the search for a better experimental paradigm-a set of mental actions that a user can evoke consistently and a machine can discriminate reliably. Examples of such mental activities are mot...
Facial Expression Brain-Computer Interfaces Machine Learning Electroencephalography Mental Processes Humans
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Dynamic time warping-based transfer learning for improving common spatial patterns in brain-computer interface.

Journal of neural engineering Feb 18, 2020
OBJECTIVE: Common spatial patterns (CSP) is a prominent feature extraction algorithm in motor imagery (MI)-based brain-computer interfaces (BCIs). However, CSP is computed using sample-based covariance-matrix estimation. Hence, its performance deteri...
Machine Learning Databases, Factual Electroencephalography Humans Imagination Signal Processing, Computer-Assisted Brain-Computer Interfaces
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Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection.

IEEE journal of biomedical and health informatics Feb 11, 2020
Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous seizures, which affects about one percent of the worlds population. Most of the current seizure detection approaches strongly rely on patient history records a...
Deep Learning Electroencephalography Signal Processing, Computer-Assisted Seizures Brain Diagnosis, Computer-Assisted Humans Neural Networks, Computer
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Deep Learning in Physiological Signal Data: A Survey.

Sensors (Basel, Switzerland) Feb 11, 2020
Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially expl...
Humans Electroencephalography Databases, Factual Deep Learning Signal Processing, Computer-Assisted Electrocardiography Electromyography
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QuPWM: Feature Extraction Method for Epileptic Spike Classification.

IEEE journal of biomedical and health informatics Feb 7, 2020
Epilepsy is a neurological disorder ranked as the second most serious neurological disease known to humanity, after stroke. Inter-ictal spiking is an abnormal neuronal discharge after an epileptic seizure. This abnormal activity can originate from on...
Epilepsy Machine Learning Sensitivity and Specificity Signal Processing, Computer-Assisted Humans Brain Algorithms Electroencephalography
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The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method.

Neural networks : the official journal of the International Neural Network Society Jan 30, 2020
Recently, combining feature extraction and classification method of electroencephalogram (EEG) signals has been widely used in identifying mild cognitive impairment. However, it remains unclear which feature of EEG signals is best effective in assess...
Electroencephalography Cognitive Dysfunction Humans Diabetes Mellitus, Type 2 Neural Networks, Computer
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Directed EEG neural network analysis by LAPPS (p≤1) Penalized sparse Granger approach.

Neural networks : the official journal of the International Neural Network Society Jan 25, 2020
The conventional multivariate Granger Analysis (GA) of directed interactions has been widely applied in brain network construction based on EEG recordings as well as fMRI. Nevertheless, EEG is usually inevitably contaminated by strong noise, which ma...
Electroencephalography Motor Cortex Humans Neural Networks, Computer Female Male
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EEG based multi-class seizure type classification using convolutional neural network and transfer learning.

Neural networks : the official journal of the International Neural Network Society Jan 25, 2020
Recognition of epileptic seizure type is essential for the neurosurgeon to understand the cortical connectivity of the brain. Though automated early recognition of seizures from normal electroencephalogram (EEG) was existing, no attempts have been ma...
Humans Support Vector Machine Electroencephalography Seizures
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Machine learning validation of EEG+tACS artefact removal.

Journal of neural engineering Jan 24, 2020
OBJECTIVE: Electroencephalography (EEG) recorded during transcranial alternating current simulation (tACS) is highly desirable in order to investigate brain dynamics during stimulation, but is corrupted by large amplitude stimulation artefacts. Artef...
Humans Photic Stimulation Adult Electroencephalography Machine Learning Artifacts Transcranial Direct Current Stimulation Male Reproducibility of Results Memory, Short-Term Female Random Allocation Young Adult Psychomotor Performance
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