AIMC Topic: Electroencephalography

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Machine learning for detection of interictal epileptiform discharges.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Apr 21, 2021
The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased likelihood of seizures and are routinely assessed by visual analysis o...
Epilepsy Signal Processing, Computer-Assisted Brain Electroencephalography Neural Networks, Computer Machine Learning Humans
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An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees.

Computer methods and programs in biomedicine Apr 21, 2021
BACKGROUND AND OBJECTIVE: Recognition of motor intention based on electroencephalogram (EEG) signals has attracted considerable research interest in the field of pattern recognition due to its notable application of non-muscular communication and con...
Humans Amputees Intention Brain-Computer Interfaces Deep Learning Electroencephalography Upper Extremity
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Bring the model to the data: The Deep Learning Epilepsy Detection Challenge.

EBioMedicine Apr 12, 2021
Deep Learning Humans Electroencephalography Epilepsy
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Deep learning for robust detection of interictal epileptiform discharges.

Journal of neural engineering Apr 8, 2021
Automatic detection of interictal epileptiform discharges (IEDs, short as 'spikes') from an epileptic brain can help predict seizure recurrence and support the diagnosis of epilepsy. Developing fast, reliable and robust detection methods for IEDs bas...
Humans Electroencephalography Epilepsy Scalp Brain Deep Learning
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A win-win situation: Does familiarity with a social robot modulate feedback monitoring and learning?

Cognitive, affective & behavioral neuroscience Apr 6, 2021
Social species rely on the ability to modulate feedback-monitoring in social contexts to adjust one's actions and obtain desired outcomes. When being awarded positive outcomes during a gambling task, feedback-monitoring is attenuated when strangers a...
Evoked Potentials Social Interaction Humans Electroencephalography Adult Reward Child Robotics Feedback
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Neurophysiological brain mapping of human sleep-wake states.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Apr 1, 2021
OBJECTIVE: We recently proposed a spectrum-based model of the awake intracranial electroencephalogram (iEEG) (Kalamangalam et al., 2020), based on a publicly-available normative database (Frauscher et al., 2018). The latter has been expanded to inclu...
Normal Distribution Sleep Stages Brain Mapping Electroencephalography Wakefulness Databases, Factual Brain Humans Neural Networks, Computer
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Uncovering the structure of clinical EEG signals with self-supervised learning.

Journal of neural engineering Mar 31, 2021
Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG), where labeling can be costly in terms of s...
Electroencephalography Supervised Machine Learning Humans Neural Networks, Computer Research Design Sleep Stages
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Early identification of epilepsy surgery candidates: A multicenter, machine learning study.

Acta neurologica Scandinavica Mar 26, 2021
OBJECTIVES: Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. Our objective was to develop site-specific machine learning (ML) algorithms to identify candidates befo...
Epilepsy Adult Humans Male Early Diagnosis Algorithms Middle Aged Child, Preschool Magnetic Resonance Imaging Retrospective Studies Female Child Electroencephalography Machine Learning Adolescent Young Adult Longitudinal Studies Cohort Studies
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Efficient use of clinical EEG data for deep learning in epilepsy.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Mar 26, 2021
OBJECTIVE: Automating detection of Interictal Epileptiform Discharges (IEDs) in electroencephalogram (EEG) recordings can reduce the time spent on visual analysis for the diagnosis of epilepsy. Deep learning has shown potential for this purpose, but ...
Epilepsy Deep Learning Humans Electroencephalography Neural Networks, Computer
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Robot-assisted stereoelectroencephalography electrode placement in twenty-three pediatric patients: a high-resolution analysis of individual lead placement time and accuracy at a single institution.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery Mar 18, 2021
PURPOSE: We describe a detailed evaluation of predictors associated with individual lead placement efficiency and accuracy for 261 stereoelectroencephalography (sEEG) electrodes placed for epilepsy monitoring in twenty-three children at our instituti...
Female Stereotaxic Techniques Humans Electrodes, Implanted Electroencephalography Robotics Child Seizures
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