AIMC Topic: Electroencephalography

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[Single-channel electroencephalogram signal used for sleep state recognition based on one-dimensional width kernel convolutional neural networks and long-short-term memory networks].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Dec 25, 2022
Aiming at the problem that the unbalanced distribution of data in sleep electroencephalogram(EEG) signals and poor comfort in the process of polysomnography information collection will reduce the model's classification ability, this paper proposed a ...
Neural Networks, Computer Sleep Algorithms Electroencephalography
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Artificial Intelligence-based Detection of Epileptic Discharges from Pediatric Scalp Electroencephalograms: A Pilot Study.

Acta medica Okayama Dec 1, 2022
We developed an artificial intelligence (AI) technique to identify epileptic discharges (spikes) in pediatric scalp electroencephalograms (EEGs). We built a convolutional neural network (CNN) model to automatically classify steep potential images int...
Humans Electroencephalography Adolescent Epilepsy Child Artificial Intelligence Scalp Pilot Projects
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An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation.

The Lancet. Digital health Dec 1, 2022
BACKGROUND: Electroencephalogram (EEG) monitoring is recommended as routine in newborn neurocritical care to facilitate early therapeutic decisions and outcome predictions. EEG's larger-scale implementation is, however, hindered by the shortage of ex...
Infant Deep Learning Brain Infant, Newborn Electroencephalography Sleep Humans Child Monitoring, Physiologic
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Forecasting macroscopic dynamics in adaptive Kuramoto network using reservoir computing.

Chaos (Woodbury, N.Y.) Oct 1, 2022
Forecasting a system's behavior is an essential task encountering the complex systems theory. Machine learning offers supervised algorithms, e.g., recurrent neural networks and reservoir computers that predict the behavior of model systems whose stat...
Electroencephalography Algorithms Forecasting Neural Networks, Computer Machine Learning
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Comparing the Usability of Alternative EEG Devices to Traditional Electrode Caps for SSVEP-BCI Controlled Assistive Robots.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings] Jul 1, 2022
Despite having the potential to improve the lives of severely paralyzed users, non-invasive Brain Computer Interfaces (BCI) have yet to be integrated into their daily lives. The widespread adoption of BCI-driven assistive technology is hindered by it...
Electrodes Evoked Potentials, Visual Robotics Brain-Computer Interfaces Humans Electroencephalography Evoked Potentials
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Classification of Seizure Termination Patterns using Deep Learning on intracranial 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, 2022
Seizure termination has received significantly less attention than initiation and propagation and consequently, remains a poorly understood phase of seizure evolution. Yet, its study may have a significant impact on the development of efficient inter...
Electroencephalography Epilepsy Humans Deep Learning Seizures Electrocorticography
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Classification of Tonic Pain Experience based on Phase Connectivity in the Alpha Frequency Band of the Electroencephalogram using Convolutional Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2022
The complexity of brain activity involved in the generation of the experience of pain makes it hard to identify neural markers able to predict pain states. The within and between subjects variability of pain hinders the predictive potential of machin...
Electroencephalography Pain Machine Learning Humans Neural Networks, Computer
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End-to-end Deep Learning of Polysomnograms for Classification of REM Sleep Behavior Disorder.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2022
Rapid eye movement (REM) sleep behavior disorder (RBD) is parasomnia and a prodromal manifestation of Parkinson's disease. The current diagnostic method relies on manual scoring of polysomnograms (PSGs), a procedure that is time and effort intensive,...
Humans Deep Learning Electroencephalography Electromyography Polysomnography REM Sleep Behavior Disorder
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EEG-GAT: Graph Attention Networks for Classification of Electroencephalogram (EEG) 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, 2022
Graph neural networks (GNN) are an emerging framework in the deep learning community. In most GNN applications, the graph topology of data samples is provided in the dataset. Specifically, the graph shift operator (GSO), which could be adjacency, gra...
Electroencephalography Polymers Electrodes Neural Networks, Computer Algorithms
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Mental arithmetic task classification with convolutional neural network based on spectral-temporal features 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, 2022
In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may benefit from BCIs as a means of communication and for the restoration of motor functions. Electroenc...
Electroencephalography Brain-Computer Interfaces Machine Learning Algorithms Neural Networks, Computer Humans
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