AIMC Topic: Electroencephalography

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Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment.

Artificial intelligence in medicine
Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures...

Manifold Learning-Based Common Spatial Pattern for EEG Signal Classification.

IEEE journal of biomedical and health informatics
EEG signal classification using Riemannian manifolds has shown great potential. However, the huge computational cost associated with Riemannian metrics poses challenges for applying Riemannian methods, particularly in high-dimensional feature data. T...

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.

PloS one
Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promis...

Applying Common Spatial Pattern and Convolutional Neural Network to Classify Movements via EEG Signals.

Clinical EEG and neuroscience
Developing an electroencephalography (EEG)-based brain-computer interface (BCI) system is crucial to enhancing the control of external prostheses by accurately distinguishing various movements through brain signals. This innovation can provide comfor...

Deep learning approaches for seizure video analysis: A review.

Epilepsy & behavior : E&B
Seizure events can manifest as transient disruptions in the control of movements which may be organized in distinct behavioral sequences, accompanied or not by other observable features such as altered facial expressions. The analysis of these clinic...

Using multivariate pattern analysis to increase effect sizes for event-related potential analyses.

Psychophysiology
Multivariate pattern analysis (MVPA) approaches can be applied to the topographic distribution of event-related potential (ERP) signals to "decode" subtly different stimulus classes, such as different faces or different orientations. These approaches...

A hybrid EEG classification model using layered cascade deep learning architecture.

Medical & biological engineering & computing
The problem of multi-class classification is always a challenge in the field of EEG (electroencephalogram)-based seizure detection. The traditional studies focus on computing or learning a set of features from EEG to distinguish between different pat...

eDeeplepsy: An artificial neural framework to reveal different brain states in children with epileptic spasms.

Epilepsy & behavior : E&B
OBJECTIVE: Despite advances, analysis and interpretation of EEG still essentially rely on visual inspection by a super-specialized physician. Considering the vast amount of data that composes the EEG, much of the detail inevitably escapes ordinary hu...

Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human-robot collaboration.

Journal of neural engineering
. Brain-computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human rob...

PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding.

Biomedical physics & engineering express
Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-based brain-computer interfaces (BCI), which has been widely applied to neurological function assessment and postoperative rehabilitation. However, accura...