AIMC Topic: Electroencephalography

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Introducing chaos behavior to kernel relevance vector machine (RVM) for four-class EEG classification.

PloS one Jun 29, 2018
This paper addresses a chaos kernel function for the relevance vector machine (RVM) in EEG signal classification, which is an important component of Brain-Computer Interface (BCI). The novel kernel function has evolved from a chaotic system, which is...
Female Humans Male Support Vector Machine Models, Theoretical Signal Processing, Computer-Assisted Electroencephalography
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Transductive Joint-Knowledge-Transfer TSK FS for Recognition of Epileptic EEG Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jun 25, 2018
Intelligent recognition of electroencephalogram (EEG) signals is an important means to detect seizure. Traditional methods for recognizing epileptic EEG signals are usually based on two assumptions: 1) adequate training examples are available for mod...
Female Young Adult Male Child, Preschool Child Infant Adolescent Artificial Intelligence Machine Learning Algorithms Humans Signal Processing, Computer-Assisted Transfer, Psychology Models, Theoretical Support Vector Machine Fuzzy Logic Electroencephalography Epilepsy Seizures
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Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection.

International journal of neural systems Jun 24, 2018
The aim of this study was to develop methods for detecting the nonstationary periodic characteristics of neonatal electroencephalographic (EEG) seizures by adapting estimates of the correlation both in the time (spike correlation; SC) and time-freque...
Electroencephalography Seizures Algorithms Humans Time Factors Infant, Newborn Support Vector Machine
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Automated detection of electroencephalography artifacts in human, rodent and canine subjects using machine learning.

Journal of neuroscience methods Jun 23, 2018
BACKGROUND: Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation.
Artifacts Brain Waves Animals ROC Curve Rats Signal Processing, Computer-Assisted Dogs Rats, Sprague-Dawley Electroencephalography Humans Machine Learning
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EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces.

Journal of neural engineering Jun 22, 2018
OBJECTIVE: Brain-computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given...
Neural Networks, Computer Algorithms Female Humans Middle Aged Adolescent Electroencephalography Brain-Computer Interfaces Young Adult Adult Male Evoked Potentials, Visual Movement Event-Related Potentials, P300
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Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture.

Scientific reports Jun 11, 2018
Familiarity of marketing stimuli may affect consumer behaviour at a peri-perceptual processing level. The current study introduces a method for deep learning of electroencephalogram (EEG) data using a spiking neural network (SNN) approach that reveal...
Perception Nerve Net Recognition, Psychology Young Adult Adult Male Action Potentials Humans Electroencephalography Deep Learning Brain Models, Neurological Female
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EEG-Based Biometrics: Challenges And Applications.

Computational intelligence and neuroscience Jun 7, 2018
Biometric Identification Humans Biometry Artificial Intelligence Electroencephalography
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Estimating Brain Connectivity With Varying-Length Time Lags Using a Recurrent Neural Network.

IEEE transactions on bio-medical engineering Jun 1, 2018
OBJECTIVE: Computer-aided estimation of brain connectivity aims to reveal information propagation in brain automatically, which has great potential in clinical applications, e.g., epilepsy foci diagnosis. Granger causality is an effective tool for di...
Nerve Net Electroencephalography Epilepsy Signal Processing, Computer-Assisted Humans Brain Models, Statistical Neural Networks, Computer Algorithms
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Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy.

Journal of neural engineering Jun 1, 2018
OBJECTIVE: An ability to map seizure-generating brain tissue, i.e. the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy. A widely-adopted practi...
Humans Artificial Intelligence Female Adult Electroencephalography Seizures Supervised Machine Learning Epilepsies, Partial Male Computer Systems Cohort Studies
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Epileptic seizure anticipation and localisation of epileptogenic region using EEG signals.

Journal of medical engineering & technology May 25, 2018
Electric activity of brain gets disturbed prior to epileptic seizure onset. Early prediction of an upcoming seizure can help to increase effectiveness of antiepileptic drugs. The scalp electroencephalogram signals contain information about the dynami...
Brain Reproducibility of Results Electroencephalography Neural Networks, Computer Machine Learning Humans Algorithms Epilepsy Seizures
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