AIMC Topic: Electroencephalography

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Effects of Robotic Neurorehabilitation on Body Representation in Individuals with Stroke: A Preliminary Study Focusing on an EEG-Based Approach.

Brain topography
Patients with stroke can experience a drastic change in their body representation (BR), beyond the physical and psychological consequences of stroke itself. Noteworthy, the misperception of BR could affect patients' motor performance even more. Our s...

A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN.

Sensors (Basel, Switzerland)
Emotion recognition based on electroencephalograms has become an active research area. Yet, identifying emotions using only brainwaves is still very challenging, especially the subject-independent task. Numerous studies have tried to propose methods ...

Prediction of cerebral perfusion pressure during CPR using electroencephalogram in a swine model of ventricular fibrillation.

The American journal of emergency medicine
BACKGROUND: Measuring the quality of cardiopulmonary resuscitation (CPR) is important for improving outcomes in cardiac arrest. Cerebral perfusion pressure (CePP) could represent cerebral circulation during CPR, but it is difficult to measure non-inv...

The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning.

Sensors (Basel, Switzerland)
In addition to helping develop products that aid the disabled, brain-computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance...

A study on CNN image classification of EEG signals represented in 2D and 3D.

Journal of neural engineering
The novelty of this study consists of the exploration of multiple new approaches of data pre-processing of brainwave signals, wherein statistical features are extracted and then formatted as visual images based on the order in which dimensionality re...

Hybrid manifold-deep convolutional neural network for sleep staging.

Methods (San Diego, Calif.)
Analysis of electroencephalogram (EEG) is a crucial diagnostic criterion for many sleep disorders, of which sleep staging is an important component. Manual stage classification is a labor-intensive process and usually suffered from many subjective fa...

Machine learning for automated EEG-based biomarkers of cognitive impairment during Deep Brain Stimulation screening in patients with Parkinson's Disease.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker of cognition using a Machine Learning (ML) pipeline.

Neural state space alignment for magnitude generalization in humans and recurrent networks.

Neuron
A prerequisite for intelligent behavior is to understand how stimuli are related and to generalize this knowledge across contexts. Generalization can be challenging when relational patterns are shared across contexts but exist on different physical s...

Motor imagery recognition with automatic EEG channel selection and deep learning.

Journal of neural engineering
Modern motor imagery (MI)-based brain computer interface systems often entail a large number of electroencephalogram (EEG) recording channels. However, irrelevant or highly correlated channels would diminish the discriminatory ability, thus reducing ...

Multi-Scale Frequency Bands Ensemble Learning for EEG-Based Emotion Recognition.

Sensors (Basel, Switzerland)
Emotion recognition has a wide range of potential applications in the real world. Among the emotion recognition data sources, electroencephalography (EEG) signals can record the neural activities across the human brain, providing us a reliable way to...