AIMC Topic: Electroencephalography
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Multi-modal cross-domain self-supervised pre-training for fMRI and EEG fusion.
Neural networks : the official journal of the International Neural Network Society
Dec 24, 2024
Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have shown promise in detecting functional abnormalities in various brain disorders. However, existing studies often focus on a single domai...
Multimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person.
Scientific reports
Dec 24, 2024
Recently, social demands for a good quality of life have increased among the elderly and disabled people. So, biomedical engineers and robotic researchers aimed to fuse these techniques in a novel rehabilitation system. Moreover, these models utilize...
Detection and location of EEG events using deep learning visual inspection.
PloS one
Dec 23, 2024
The electroencephalogram (EEG) is a major diagnostic tool that provides detailed insight into the electrical activity of the brain. This signal contains a number of distinctive waveform patterns that reflect the subject's health state in relation to ...
Decoding thoughts, encoding ethics: A narrative review of the BCI-AI revolution.
Brain research
Dec 22, 2024
OBJECTIVES: This narrative review aims to analyze mechanisms underlying Brain-Computer Interface (BCI) and Artificial Intelligence (AI) integration, evaluate recent advances in signal acquisition and processing techniques, and assess AI-enhanced neur...
DCSENets: Interpretable deep learning for patient-independent seizure classification using enhanced EEG-based spectrogram visualization.
Computers in biology and medicine
Dec 20, 2024
Neurologists often face challenges in identifying epileptic activities within multichannel EEG recordings, requiring extensive hours of analysis. Computer-aided diagnosis systems have been proposed to reduce manual inspection of EEG signals by neurol...
Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies.
Sensors (Basel, Switzerland)
Dec 20, 2024
Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemodynamic regulation across various diseases. These sensors generate continuous high-frequency data st...
Enhancing Deep-Learning Classification for Remote Motor Imagery Rehabilitation Using Multi-Subject Transfer Learning in IoT Environment.
Sensors (Basel, Switzerland)
Dec 19, 2024
One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training...
Multimodal autism detection: Deep hybrid model with improved feature level fusion.
Computer methods and programs in biomedicine
Dec 18, 2024
OBJECTIVE: Social communication difficulties are a characteristic of autism spectrum disorder (ASD), a neurodevelopmental condition. The earlier method of diagnosing autism largely relied on error-prone behavioral observation of symptoms. More intell...
Annotated interictal discharges in intracranial EEG sleep data and related machine learning detection scheme.
Scientific data
Dec 18, 2024
Interictal epileptiform discharges (IEDs) such as spikes and sharp waves represent pathological electrophysiological activities occurring in epilepsy patients between seizures. IEDs occur preferentially during non-rapid eye movement (NREM) sleep and ...
Improving the Performance of Electrotactile Brain-Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials.
Sensors (Basel, Switzerland)
Dec 17, 2024
Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contr...
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