AIMC Topic: Electroencephalography
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Artificial intelligence in epilepsy - applications and pathways to the clinic.
Nature reviews. Neurology
May 8, 2024
Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of ...
EEG classification model for virtual reality motion sickness based on multi-scale CNN feature correlation.
Computer methods and programs in biomedicine
May 7, 2024
BACKGROUND: Virtual reality motion sickness (VRMS) is a key issue hindering the development of virtual reality technology, and accurate detection of its occurrence is the first prerequisite for solving the issue.
Machine learning-empowered sleep staging classification using multi-modality signals.
BMC medical informatics and decision making
May 6, 2024
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EO...
Detecting emotions through EEG signals based on modified convolutional fuzzy neural network.
Scientific reports
May 6, 2024
Emotion is a human sense that can influence an individual's life quality in both positive and negative ways. The ability to distinguish different types of emotion can lead researchers to estimate the current situation of patients or the probability o...
EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT.
IEEE journal of biomedical and health informatics
May 6, 2024
Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of ...
Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 6, 2024
Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real-life assistance to individuals facing movement challenges. However, ...
Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data.
NeuroImage
May 3, 2024
Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA o...
A causal perspective on brainwave modeling for brain-computer interfaces.
Journal of neural engineering
May 3, 2024
. Machine learning (ML) models have opened up enormous opportunities in the field of brain-computer Interfaces (BCIs). Despite their great success, they usually face severe limitations when they are employed in real-life applications outside a contro...
Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging.
IEEE transactions on neural networks and learning systems
May 2, 2024
Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system. However, due to the complex nonstationary charact...
Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.
Sensors (Basel, Switzerland)
Apr 30, 2024
The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable perform...
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