AIMC Topic: Electroencephalography

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Three simple steps to improve the interpretability of EEG-SVM studies.

Journal of neurophysiology
Machine-learning systems that classify electroencephalography (EEG) data offer important perspectives for the diagnosis and prognosis of a wide variety of neurological and psychiatric conditions, but their clinical adoption remains low. We propose he...

Localizing seizure onset zones in surgical epilepsy with neurostimulation deep learning.

Journal of neurosurgery
OBJECTIVE: In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization that use brief interictal recordings could supplement presurgical evaluations and improve care. Thus, the authors sought to localize SOZs b...

Investigating Methods for Cognitive Workload Estimation for Assistive Robots.

Sensors (Basel, Switzerland)
Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing mod...

A novel approach for detection of dyslexia using convolutional neural network with EOG signals.

Medical & biological engineering & computing
Dyslexia is a learning disability in acquiring reading skills, even though the individual has the appropriate learning opportunity, adequate education, and appropriate sociocultural environment. Dyslexia negatively affects children's educational deve...

Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN Autoencoder.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals which often get contaminated with noise during the recording process, mostly due to muscle artifacts ...

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works.

Computers in biology and medicine
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood...

ResNet-50 based technique for EEG image characterization due to varying environmental stimuli.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Emotion is an important factor affecting a person's physical and mental health, but there are few ways to detect a patient's emotions in daily life. Negative emotions not only affect recovery after treatment, but also cause ...

Diagnostic and prognostic EEG analysis of critically ill patients: A deep learning study.

NeuroImage. Clinical
Visual interpretation of electroencephalography (EEG) is time consuming, may lack objectivity, and is restricted to features detectable by a human. Computer-based approaches, especially deep learning, could potentially overcome these limitations. How...

The Portiloop: A deep learning-based open science tool for closed-loop brain stimulation.

PloS one
Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events of interest, and producing auditory, magnetic or electrical stimulation so as to interact with brain...

CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis.

Scientific reports
Recognizing emotional state of human using brain signal is an active research domain with several open challenges. In this research, we propose a signal spectrogram image based CNN-XGBoost fusion method for recognising three dimensions of emotion, na...