AIMC Topic: Electroencephalography

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Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach.

Sensors (Basel, Switzerland)
Visual stimuli from photographs and artworks raise corresponding emotional responses. It is a long process to prove whether the emotions that arise from photographs and artworks are different or not. We answer this question by employing electroenceph...

Revisiting the value of polysomnographic data in insomnia: more than meets the eye.

Sleep medicine
BACKGROUND: Polysomnography (PSG) is not recommended as a diagnostic tool in insomnia. However, this consensual approach might be tempered in the light of two ongoing transformations in sleep research: big data and artificial intelligence (AI).

Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry.

Journal of medical systems
Depression or Major Depressive Disorder (MDD) is a mental illness which negatively affects how a person thinks, acts or feels. MDD has become a major disease affecting millions of people presently. The diagnosis of depression is questionnaire based a...

EMS-Net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes.

IEEE transactions on medical imaging
Epilepsy is a neurological disorder characterized by sudden and unpredictable epileptic seizures, which incurs significant negative impacts on patients' physical, psychological and social health. A practical approach to assist with the clinical asses...

A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method.

Medical hypotheses
Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine learning methods have been proposed to develop automated disease diagnosis ...

Robot-assisted versus manual navigated stereoelectroencephalography in adult medically-refractory epilepsy patients.

Epilepsy research
OBJECTIVE: Stereoelectroencephalography (SEEG) has experienced a recent growth in adoption for epileptogenic zone (EZ) localization. Advances in robotics have the potential to improve the efficiency and safety of this intracranial seizure monitoring ...

A Bayesian Shared Control Approach for Wheelchair Robot With Brain Machine Interface.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
To enhance the performance of the brain-actuated robot system, a novel shared controller based on Bayesian approach is proposed for intelligently combining robot automatic control and brain-actuated control, which takes into account the uncertainty o...

Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network-Feasibility Study.

Sensors (Basel, Switzerland)
Electroencephalography (EEG) has relatively poor spatial resolution and may yield incorrect brain dynamics and distort topography; thus, high-density EEG systems are necessary for better analysis. Conventional methods have been proposed to solve thes...

Deep Learning Approach for Epileptic Focus Localization.

IEEE transactions on biomedical circuits and systems
The task of epileptic focus localization receives great attention due to its role in an effective epileptic surgery. The clinicians highly depend on the intracranial EEG data to make a surgical decision related to epileptic subjects suffering from un...

A hybrid self-attention deep learning framework for multivariate sleep stage classification.

BMC bioinformatics
BACKGROUND: Sleep is a complex and dynamic biological process characterized by different sleep patterns. Comprehensive sleep monitoring and analysis using multivariate polysomnography (PSG) records has achieved significant efforts to prevent sleep-re...