AIMC Topic: Electroencephalography

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Frame-based versus robot-assisted stereo-electro-encephalography for drug-resistant epilepsy.

Acta neurochirurgica Feb 16, 2024
BACKGROUND: Stereoelectroencephalography (SEEG) is an effective presurgical invasive evaluation for drug-resistant epilepsies. The introduction of robotic devices provides a simplified, accurate, and safe alternative to the conventional SEEG techniqu...
Electrodes, Implanted Robotics Electroencephalography Stereotaxic Techniques Drug Resistant Epilepsy Humans Epilepsy
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Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal.

Physical and engineering sciences in medicine Feb 15, 2024
In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) pa...
Electroencephalography Signal Processing, Computer-Assisted Deep Learning Treatment Outcome Selective Serotonin Reuptake Inhibitors Adult Male Middle Aged Depressive Disorder, Major Neural Networks, Computer Female Humans
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DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Feb 13, 2024
Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects millions of people worldwide. Making early and accurate diagnosis of it is very meaningful. Recently, EEG, a non-invasive technique of recording spontaneous elec...
Electroencephalography Depressive Disorder, Major Deep Learning Humans Brain
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Machine learning using brain activity can capture the state of mind when a person is preoccupied by the unpleasant sensory experience of itch.

Journal of the European Academy of Dermatology and Venereology : JEADV Feb 5, 2024
Machine Learning Brain Humans Electroencephalography Female Adult Male Pruritus
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Self-Supervised Learning for Electroencephalography.

IEEE transactions on neural networks and learning systems Feb 5, 2024
Decades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with conventional statistical techniques. However, even the most advanced machine learning ...
Neural Networks, Computer Machine Learning Electroencephalography Supervised Machine Learning
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Scale based entropy measures and deep learning methods for analyzing the dynamical characteristics of cardiorespiratory control system in COVID-19 subjects during and after recovery.

Computers in biology and medicine Feb 1, 2024
COVID-19, known as Coronavirus Disease 2019 primarily targets the respiratory system and can impact the cardiovascular system, leading to a range of cardiorespiratory complications. The current forefront in analyzing the dynamical characteristics of ...
Deep Learning Humans Electroencephalography Artificial Intelligence Female COVID-19 Male Entropy
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Unraveling motor imagery brain patterns using explainable artificial intelligence based on Shapley values.

Computer methods and programs in biomedicine Jan 30, 2024
BACKGROUND AND OBJECTIVE: Motor imagery (MI) based brain-computer interfaces (BCIs) are widely used in rehabilitation due to the close relationship that exists between MI and motor execution (ME). However, the underlying brain mechanisms of MI remain...
Movement Humans Imagination Artificial Intelligence Electroencephalography Algorithms Brain-Computer Interfaces Brain
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Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.

Sensors (Basel, Switzerland) Jan 29, 2024
The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various appli...
Signal Processing, Computer-Assisted Neural Networks, Computer Interior Design and Furnishings Electroencephalography
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Navigation Learning Assessment Using EEG-Based Multi-Time Scale Spatiotemporal Compound Model.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 29, 2024
This study presents a novel method to assess the learning effectiveness using Electroencephalography (EEG)-based deep learning model. It is difficult to assess the learning effectiveness of professional courses in cultivating students' ability object...
Algorithms Brain Cognition Electroencephalography Neural Networks, Computer Humans Machine Learning
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Reservoir Computing With Dynamic Reservoir using Cascaded DNA Memristors.

IEEE transactions on biomedical circuits and systems Jan 26, 2024
This article proposes molecular and DNA memristors where the state is defined by a single output variable. In past molecular and DNA memristors, the state of the memristor was defined based on two output variables. These memristors cannot be cascaded...
Electroencephalography Neural Networks, Computer Time Factors
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