AIMC Topic: Electroencephalography
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Implantation accuracy and operative variables in robot-assisted stereoelectroencephalography.
Journal of neurosurgery
May 26, 2023
OBJECTIVE: The stereoelectroencephalography (SEEG) procedure provides a unique 3D overview of the seizure-onset zone. Although the success of SEEG relies on the accuracy of depth electrode implantation, few studies have investigated how different imp...
Accuracy of Depth Electrodes is Not Time-Dependent in Robot-Assisted Stereoelectroencephalography in a Pediatric Population.
Operative neurosurgery (Hagerstown, Md.)
May 22, 2023
BACKGROUND AND OBJECTIVES: Robot-assisted stereoelectroencephalography (sEEG) is steadily supplanting traditional frameless and frame-based modalities for minimally invasive depth electrode placement in epilepsy workup. Accuracy rates similar to gold...
Performance of a Convolutional Neural Network Derived From PPG Signal in Classifying Sleep Stages.
IEEE transactions on bio-medical engineering
May 19, 2023
Automatic sleep stage classification is vital for evaluating the quality of sleep. Conventionally, sleep is monitored using multiple physiological sensors that are uncomfortable for long-term monitoring and require expert intervention. In this study,...
Identification of attention deficit hyperactivity disorder with deep learning model.
Physical and engineering sciences in medicine
May 16, 2023
This article explores the detection of Attention Deficit Hyperactivity Disorder, a neurobehavioral disorder, from electroencephalography signals. Due to the unstable behavior of electroencephalography signals caused by complex neuronal activity in th...
Supervised deep learning with vision transformer predicts delirium using limited lead EEG.
Scientific reports
May 16, 2023
As many as 80% of critically ill patients develop delirium increasing the need for institutionalization and higher morbidity and mortality. Clinicians detect less than 40% of delirium when using a validated screening tool. EEG is the criterion standa...
Evaluation of interpretability for deep learning algorithms in EEG emotion recognition: A case study in autism.
Artificial intelligence in medicine
May 13, 2023
Current models on Explainable Artificial Intelligence (XAI) have shown a lack of reliability when evaluating feature-relevance for deep neural biomarker classifiers. The inclusion of reliable saliency-maps for obtaining trustworthy and interpretable ...
Deep learning on independent spatial EEG activity patterns delineates time windows relevant for response inhibition.
Psychophysiology
May 12, 2023
Inhibitory control processes are an important aspect of executive functions and goal-directed behavior. However, the mostly correlative nature of neurophysiological studies was not able to provide insights which aspects of neural dynamics can best pr...
Deep learning applied to EEG source-data reveals both ventral and dorsal visual stream involvement in holistic processing of social stimuli.
Scientific reports
May 5, 2023
Perception of social stimuli (faces and bodies) relies on "holistic" (i.e., global) mechanisms, as supported by picture-plane inversion: perceiving inverted faces/bodies is harder than perceiving their upright counterpart. Albeit neuroimaging evidenc...
MaskSleepNet: A Cross-Modality Adaptation Neural Network for Heterogeneous Signals Processing in Sleep Staging.
IEEE journal of biomedical and health informatics
May 4, 2023
Deep learning methods have become an important tool for automatic sleep staging in recent years. However, most of the existing deep learning-based approaches are sharply constrained by the input modalities, where any insertion, substitution, and dele...
Deep Learning With Convolutional Neural Networks for Motor Brain-Computer Interfaces Based on Stereo-Electroencephalography (SEEG).
IEEE journal of biomedical and health informatics
May 4, 2023
OBJECTIVE: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its applicati...
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