AIMC Topic: Electroencephalography

Clear Filters Showing 1081 to 1090 of 2295 articles

A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
OBJECTIVE: Scarcity of good quality electroencephalography (EEG) data is one of the roadblocks for accurate seizure prediction. This work proposes a deep convolutional generative adversarial network (DCGAN) to generate synthetic EEG data. Another obj...

Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning.

Scientific reports
The ability to forecast seizures minutes to hours in advance of an event has been verified using invasive EEG devices, but has not been previously demonstrated using noninvasive wearable devices over long durations in an ambulatory setting. In this s...

Coherent false seizure prediction in epilepsy, coincidence or providence?

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Seizure forecasting using machine learning is possible, but the performance is far from ideal, as indicated by many false predictions and low specificity. Here, we examine false and missing alarms of two algorithms on long-term datasets to...

Minimum spanning tree based graph neural network for emotion classification using EEG.

Neural networks : the official journal of the International Neural Network Society
Emotion classification based on neurophysiology signals has been a challenging issue in the literature. Recent neuroscience findings suggest that brain network structure underlying the different emotions provides a window in understanding human affec...

EEG-based detection of emotional valence towards a reproducible measurement of emotions.

Scientific reports
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
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
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)
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
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
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...