AIMC Topic: Electroencephalography
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Rigid Cranial Fixation for Robot-Assisted Stereoelectroencephalography in Toddlers: Technical Considerations.
Operative neurosurgery (Hagerstown, Md.)
Jun 1, 2020
BACKGROUND: Stereoelectroencephalography (sEEG) using depth electrodes has become a mainstay of pediatric epilepsy surgery. This technique relies on rigid cranial fixation using skull pins, which forms the basis for accurate stereotactic navigation. ...
Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning: A Data-Repurposing Approach.
Anesthesia and analgesia
May 1, 2020
BACKGROUND: Brain monitors tracking quantitative brain activities from electroencephalogram (EEG) to predict hypnotic levels have been proposed as a labor-saving alternative to behavioral assessments. Expensive clinical trials are required to validat...
Robot-assisted stereoelectroencephalography exploration of the limbic thalamus in human focal epilepsy: implantation technique and complications in the first 24 patients.
Neurosurgical focus
Apr 1, 2020
OBJECTIVE: Despite numerous imaging studies highlighting the importance of the thalamus in a patient's surgical prognosis, human electrophysiological studies involving the limbic thalamic nuclei are limited. The objective of this study was to evaluat...
Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques.
Journal of integrative neuroscience
Mar 30, 2020
Electroencephalography is the recording of brain electrical activities that can be used to diagnose brain seizure disorders. By identifying brain activity patterns and their correspondence between symptoms and diseases, it is possible to give an accu...
Motor imagery classification using geodesic filtering common spatial pattern and filter-bank feature weighted support vector machine.
The Review of scientific instruments
Mar 1, 2020
In recent years, Brain Computer Interface (BCI) based on motor imagery has been widely used in the fields of medicine, active safe systems for automobiles, entertainment, and so on. Motor imagery relevant electroencephalogram (EEG) signals are weak, ...
A TrAdaBoost Method for Detecting Multiple Subjects' N200 and P300 Potentials Based on Cross-Validation and an Adaptive Threshold.
International journal of neural systems
Mar 1, 2020
Traditional training methods need to collect a large amount of data for every subject to train a subject-specific classifier, which causes subjects fatigue and training burden. This study proposes a novel training method, TrAdaBoost based on cross-va...
Robot-Assisted Insular Depth Electrode Implantation Through Oblique Trajectories: 3-Dimensional Anatomical Nuances, Technique, Accuracy, and Safety.
Operative neurosurgery (Hagerstown, Md.)
Mar 1, 2020
BACKGROUND: The insula is a deep cortical structure that has renewed interest in epilepsy investigation. Invasive EEG recordings of this region have been challenging. Robot-assisted stereotactic electroencephalography has improved feasibility and saf...
Electroencephalogram-Based Emotion Recognition Using a Particle Swarm Optimization-Derived Support Vector Machine Classifier.
Critical reviews in biomedical engineering
Jan 1, 2020
We sort human emotions using Russell's circumplex model of emotion by classifying electroencephalogram (EEG) signals from 25 subjects into four discrete states, namely, happy, sad, angry, and relaxed. After acquiring signals, we use a standard databa...
A natural evolution optimization based deep learning algorithm for neurological disorder classification.
Bio-medical materials and engineering
Jan 1, 2020
BACKGROUND: A neurological disorder is one of the significant problems of the nervous system that affects the essential functions of the human brain and spinal cord. Monitoring brain activity through electroencephalography (EEG) has become an importa...
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