AIMC Topic: Electroencephalography
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Facial expression recognition based on Electroencephalogram and facial landmark localization.
Technology and health care : official journal of the European Society for Engineering and Medicine
Jan 1, 2019
BACKGROUND: Facial expression recognition plays an essential role in affective computing, mental illness diagnosis and rehabilitation. Therefore, facial expression recognition has attracted more and more attention over the years.
A Multi-View Deep Learning Framework for EEG Seizure Detection.
IEEE journal of biomedical and health informatics
Jan 1, 2019
The recent advances in pervasive sensing technologies have enabled us to monitor and analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to prevent serious outcomes caused by epileptic seizures. To avoid manual visual in...
The Decision Decoding ToolBOX (DDTBOX) - A Multivariate Pattern Analysis Toolbox for Event-Related Potentials.
Neuroinformatics
Jan 1, 2019
In recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG...
Expert-level sleep scoring with deep neural networks.
Journal of the American Medical Informatics Association : JAMIA
Dec 1, 2018
OBJECTIVES: Scoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb movements. Attempts to automate this process have been hampered by the com...
Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy.
Neurosurgery
Oct 1, 2018
BACKGROUND: Interictal epileptiform discharges are an important biomarker for localization of focal epilepsy, especially in patients who undergo chronic intracranial monitoring. Manual detection of these pathophysiological events is cumbersome, but i...
Application of a brain-computer interface for person authentication using EEG responses to photo stimuli.
Journal of integrative neuroscience
Aug 15, 2018
In this paper, a personal authentication system that can effectively identify individuals by generating unique electroencephalogram signal features in response to self-face and non-self-face photos is presented. To achieve performance stability, a se...
Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
This study presents a novel, deep, fully convolutional architecture which is optimized for the task of EEG-based neonatal seizure detection. Architectures of different depths were designed and tested; varying network depth impacts convolutional recep...
Ensemble learning based on overlapping clusters of subjects to predict microsleep states from EEG.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
Microsleeps are brief and involuntary instances of complete loss of sleep-related consciousness. We present a novel approach of creating overlapping clusters of subjects and training of an ensemble classifier to enhance the prediction of microsleep s...
Deep Learning Enabled Automatic Abnormal EEG Identification.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
In hospitals, physicians diagnose brain-related disorders such as epilepsy by analyzing electroencephalograms (EEG). However, manual analysis of EEG data requires highly trained clinicians or neurophysiologists and is a procedure that is known to hav...
Hardware Implementation of a Performance and Energy-optimized Convolutional Neural Network for Seizure 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, 2018
We present for the first time a μW-power convolutional neural network for seizure detection running on a low-power microcontroller. On a dataset of 22 patients a median sensitivity of 100% is achieved. With a false positive rate of 20.7 fp/h and a sh...
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