AIMC Topic: Electroencephalography
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EEG-based detection of emotional valence towards a reproducible measurement of emotions.
Scientific reports
Nov 3, 2021
A methodological contribution to a reproducible Measurement of Emotions for an EEG-based system is proposed. Emotional Valence detection is the suggested use case. Valence detection occurs along the interval scale theorized by the Circumplex Model of...
Robot-assisted stereoelectroencephalography in young children: technical challenges and considerations.
Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Oct 30, 2021
Robot-assisted stereoelectroencephalography (sEEG) is frequently employed to localize epileptogenic zones in patients with medically refractory epilepsy (MRE). Its methodology is well described in adults, but less so in children. Given the limited in...
AiED: Artificial intelligence for the detection of intracranial interictal epileptiform discharges.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Oct 27, 2021
OBJECTIVE: Deep learning provides an appealing solution for the ongoing challenge of automatically classifying intracranial interictal epileptiform discharges (IEDs). We report results from an automated method consisting of a template-matching algori...
Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis.
Sensors (Basel, Switzerland)
Oct 25, 2021
Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. Although dyslexia diagnosis has been addressed by these tests in clinical practice, it is difficult ...
Brain oscillatory correlates of visuomotor adaptive learning.
NeuroImage
Oct 21, 2021
Sensorimotor adaptation involves the recalibration of the mapping between motor command and sensory feedback in response to movement errors. Although adaptation operates within individual movements on a trial-to-trial basis, it can also undergo learn...
Causal decoding of individual cortical excitability states.
NeuroImage
Oct 21, 2021
Brain responsiveness to stimulation fluctuates with rapidly shifting cortical excitability state, as reflected by oscillations in the electroencephalogram (EEG). For example, the amplitude of motor-evoked potentials (MEPs) elicited by transcranial ma...
A Deep Learning-Based Classification Method for Different Frequency EEG Data.
Computational and mathematical methods in medicine
Oct 21, 2021
In recent years, the research on electroencephalography (EEG) has focused on the feature extraction of EEG signals. The development of convenient and simple EEG acquisition devices has produced a variety of EEG signal sources and the diversity of the...
Brain functional and effective connectivity based on electroencephalography recordings: A review.
Human brain mapping
Oct 20, 2021
Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional...
Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification.
IEEE transactions on bio-medical engineering
Oct 19, 2021
OBJECTIVES: Big data analytics can potentially benefit the assessment and management of complex neurological conditions by extracting information that is difficult to identify manually. In this study, we evaluated the performance of commonly used sup...
EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising.
Journal of neural engineering
Oct 14, 2021
Deep learning (DL) networks are increasingly attracting attention across various fields, including electroencephalography (EEG) signal processing. These models provide comparable performance to that of traditional techniques. At present, however, the...
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