AIMC Topic: Electroencephalography

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An Evaluation of Hybrid Deep Learning Models for Classifying Multiple Lower Limb Actions.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2023
Brain-computer Interfaces (BCIs) interpret electroencephalography (EEG) signals and translate them into control commands for operating external devices. The motor imagery (MI) paradigm is popular in this context. Recent research has demonstrated that...
Humans Lower Extremity Electroencephalography Imagination Neural Networks, Computer Deep Learning
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Machine learning classifiers for electrode selection in the design of closed-loop neuromodulation devices for episodic memory improvement.

Cerebral cortex (New York, N.Y. : 1991) Jun 20, 2023
Successful neuromodulation approaches to alter episodic memory require closed-loop stimulation predicated on the effective classification of brain states. The practical implementation of such strategies requires prior decisions regarding electrode im...
Humans Electroencephalography Memory, Episodic Brain Random Forest Support Vector Machine Mental Recall Unsupervised Machine Learning Electrodes Cluster Analysis Brain-Computer Interfaces
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Modulating hierarchical learning by high-definition transcranial alternating current stimulation at theta frequency.

Cerebral cortex (New York, N.Y. : 1991) Apr 4, 2023
Considerable evidence highlights the dorsolateral prefrontal cortex (DLPFC) as a key region for hierarchical (i.e. multilevel) learning. In a previous electroencephalography (EEG) study, we found that the low-level prediction errors were encoded by f...
Deep Learning Bayes Theorem Electroencephalography Humans Transcranial Direct Current Stimulation Reversal Learning
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[Identifying Depressive Disorder With Sleep Electroencephalogram Data: A Study Based on Deep Learning].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition Mar 1, 2023
OBJECTIVE: To explore the effectiveness of using deep learning network combined Vision Transformer (ViT) and Transformer to identify patients with depressive disorder on the basis of their sleep electroencephalogram (EEG) signals.
Electroencephalography Depressive Disorder Sleep Stages Humans Deep Learning Sleep, REM
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Auditory stimulation and deep learning predict awakening from coma after cardiac arrest.

Brain : a journal of neurology Feb 13, 2023
Assessing the integrity of neural functions in coma after cardiac arrest remains an open challenge. Prognostication of coma outcome relies mainly on visual expert scoring of physiological signals, which is prone to subjectivity and leaves a considera...
Deep Learning Electroencephalography Heart Arrest Coma Humans Acoustic Stimulation Prognosis
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A Self-Interpretable Deep Learning Model for Seizure Prediction Using a Multi-Scale Prototypical Part Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 1, 2023
The epileptic seizure prediction (ESP) method aims to timely forecast the occurrence of seizures, which is crucial to improving patients' quality of life. Many deep learning-based methods have been developed to tackle this issue and achieve significa...
Dogs Epilepsy Animals Computer Simulation Humans Seizures Deep Learning Predictive Value of Tests Quality of Life Sensitivity and Specificity Algorithms Electroencephalography
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A Developed LSTM-Ladder-Network-Based Model for Sleep Stage Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 1, 2023
Sleep staging is crucial for diagnosing sleep-related disorders. The heavy and time-consuming task of manual staging can be released by automatic techniques. However, the automatic staging model would have a relatively poor performance when working o...
Humans Neural Networks, Computer Electroencephalography Sleep Sleep Stages Learning
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A Low-Complexity Brain-Computer Interface for High-Complexity Robot Swarm Control.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 1, 2023
A brain-computer interface (BCI) is a system that allows a human operator to use only mental commands in controlling end effectors that interact with the world around them. Such a system consists of a measurement device to record the human user's bra...
Robotics Electroencephalography Brain-Computer Interfaces Feedback User-Computer Interface Humans Algorithms
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[COLUMN: Robot Assisted SEEG].

No shinkei geka. Neurological surgery Jan 1, 2023
Humans Robotics Stereotaxic Techniques Retrospective Studies Electroencephalography Drug Resistant Epilepsy
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Classification of EEG signals related to real and imagery knee movements using deep learning for brain computer interfaces.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2023
BACKGROUND: Non-invasive Brain-Computer Interface (BCI) uses an electroencephalogram (EEG) to obtain information on brain neural activity. Because EEG can be contaminated by various artifacts during the collection process, it has primarily evolved in...
Movement Imagination Neural Networks, Computer Algorithms Electroencephalography Humans Brain-Computer Interfaces Deep Learning
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