AIMC Topic: Electroencephalography

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Electrophysiological Signatures of Hierarchical Learning.

Cerebral cortex (New York, N.Y. : 1991) Jan 22, 2022
Human perception and learning is thought to rely on a hierarchical generative model that is continuously updated via precision-weighted prediction errors (pwPEs). However, the neural basis of such cognitive process and how it unfolds during decision-...
Reversal Learning Bayes Theorem Brain Electroencephalography Deep Learning Humans
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Physiological Assessment of Delirium Severity: The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).

Critical care medicine Jan 1, 2022
OBJECTIVES: Delirium is a common and frequently underdiagnosed complication in acutely hospitalized patients, and its severity is associated with worse clinical outcomes. We propose a physiologically based method to quantify delirium severity as a to...
Adult Male Middle Aged Female Aged Severity of Illness Index Length of Stay Hospital Mortality Hospitals Comorbidity Humans Prognosis Retrospective Studies Machine Learning Academic Medical Centers Confusion Patient Acuity Image Processing, Computer-Assisted Delirium Electroencephalography
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A deep learning algorithm based on 1D CNN-LSTM for automatic sleep staging.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2022
BACKGROUND: Sleep staging is an important part of sleep research. Traditional automatic sleep staging based on machine learning requires extensive feature extraction and selection.
Humans Neural Networks, Computer Electroencephalography Sleep Algorithms Sleep Stages Deep Learning
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Deep learning-based EEG analysis: investigating P3 ERP components.

Journal of integrative neuroscience Dec 30, 2021
The neural processing of incoming stimuli can be analysed from the electroencephalogram (EEG) through event-related potentials (ERPs). The P3 component is largely investigated as it represents an important psychophysiological marker of psychiatric di...
Electroencephalography Event-Related Potentials, P300 Deep Learning Models, Theoretical Humans
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[The accuracy and influencing factors of sleep staging based on single-channel EEG via a deep neural network].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Dec 7, 2021
To investigate theaccuracy of artificial intelligence sleep staging model in patients with habitual snoring and obstructive sleep apnea hypopnea syndrome (OSAHS) based on single-channel EEG collected from different locations of the head. The clinic...
Neural Networks, Computer Artificial Intelligence Humans Middle Aged Young Adult Electroencephalography Retrospective Studies Adult Sleep Stages Sleep Female Male
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Irrelevant Robot Signals in a Categorization Task Induce Cognitive Conflict in Performance, Eye Trajectories, the N2 Component of the EEG Signal, and Frontal Theta Oscillations.

Journal of cognitive neuroscience Dec 6, 2021
Understanding others' nonverbal behavior is essential for social interaction, as it allows, among others, to infer mental states. Although gaze communication, a well-established nonverbal social behavior, has shown its importance in inferring others'...
Cues Electroencephalography Reaction Time Cognition Robotics Humans
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Investigation of Machine Learning and Deep Learning Approaches for Detection of Mild Traumatic Brain Injury from Human Sleep Electroencephalogram.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Traumatic Brain Injury (TBI) is a highly prevalent and serious public health concern. Most cases of TBI are mild in nature, yet some individuals may develop following-up persistent disability. The pathophysiologic causes for those with persistent pos...
Sleep Deep Learning Humans Brain Concussion Electroencephalography Machine Learning
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Demonstrating the Viability of Mapping Deep Learning Based EEG Decoders to Spiking Networks on Low-powered Neuromorphic Chips.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Accurate and low-power decoding of brain signals such as electroencephalography (EEG) is key to constructing brain-computer interface (BCI) based wearable devices. While deep learning approaches have progressed substantially in terms of decoding accu...
Electroencephalography Algorithms Brain-Computer Interfaces Deep Learning Neural Networks, Computer
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Reduction of the ERP Measurement Time by a Weighted Averaging 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 Nov 1, 2021
In clinical examination, event-related potentials (ERPs) are estimated by averaging across multiple responses, which suppresses background EEG. However, acquiring the number of responses needed for this process is time consuming. We therefore propose...
Deep Learning Electroencephalography Evoked Potentials Reaction Time
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EEG-based Emotion Recognition Using Graph Convolutional Network with Learnable Electrode Relations.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Emotion recognition based on electroencephalography (EEG) plays a pivotal role in the field of affective computing, and graph convolutional neural network (GCN) has been proved to be an effective method and made considerable progress. Since the adjac...
Electrodes Brain Emotions Neural Networks, Computer Electroencephalography
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