AIMC Topic: Electroencephalography

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A General DNA-Like Hybrid Symbiosis Framework: An EEG Cognitive Recognition Method.

IEEE journal of biomedical and health informatics
In electroencephalogram (EEG) cognitive recognition research, the combined use of artificial neural networks (ANNs) and spiking neural networks (SNNs) plays an important role to realize different categories of recognition tasks. However, most of the ...

Classification of EEG evoked in 2D and 3D virtual reality: traditional machine learning versus deep learning.

Biomedical physics & engineering express
. Virtual reality (VR) simulates real-life events and scenarios and is widely utilized in education, entertainment, and medicine. VR can be presented in two dimensions (2D) or three dimensions (3D), with 3D VR offering a more realistic and immersive ...

Task-oriented EEG denoising generative adversarial network for enhancing SSVEP-BCI performance.

Journal of neural engineering
The quality of electroencephalogram (EEG) signals directly impacts the performance of brain-computer interface (BCI) tasks. Many methods have been proposed to eliminate noise from EEG signals, but most of these methods focus solely on signal denoisin...

Machine learning for forecasting initial seizure onset in neonatal hypoxic-ischemic encephalopathy.

Epilepsia
OBJECTIVE: This study was undertaken to develop a machine learning (ML) model to forecast initial seizure onset in neonatal hypoxic-ischemic encephalopathy (HIE) utilizing clinical and quantitative electroencephalogram (QEEG) features.

Design of EEG based thought identification system using EMD & deep neural network.

Scientific reports
Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based mes...

A protocol for trustworthy EEG decoding with neural networks.

Neural networks : the official journal of the International Neural Network Society
Deep learning solutions have rapidly emerged for EEG decoding, achieving state-of-the-art performance on a variety of decoding tasks. Despite their high performance, existing solutions do not fully address the challenge posed by the introduction of m...

Review of deep representation learning techniques for brain-computer interfaces.

Journal of neural engineering
In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial interest.: This review synthesizes empirical findings from a collection...

Application of Electroencephalography Sensors and Artificial Intelligence in Automated Language Teaching.

Sensors (Basel, Switzerland)
This study developed an automated language learning teaching assessment system based on electroencephalography (EEG) and differential language large models (LLMs), aimed at enhancing language instruction effectiveness by monitoring learners' cognitiv...

Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning.

Sensors (Basel, Switzerland)
The perception and recognition of objects around us empower environmental interaction. Harnessing the brain's signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is ...

Seizure Sources Can Be Imaged from Scalp EEG by Means of Biophysically Constrained Deep Neural Networks.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Seizure localization is important for managing drug-resistant focal epilepsy. Here, the capability of a novel deep learning-based source imaging framework (DeepSIF) for imaging seizure activities from electroencephalogram (EEG) recordings in drug-res...