AIMC Topic: Electroencephalography

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ECA-FusionNet: a hybrid EEG-fNIRS signals network for MI classification.

Journal of neural engineering Feb 7, 2025
. Among all BCI paradigms, motion imagery (MI) has gained favor among researchers because it allows users to control external devices by imagining movements rather than actually performing actions. This property holds important promise for clinical a...
Neural Networks, Computer Brain Spectroscopy, Near-Infrared Humans Imagination Electroencephalography Brain-Computer Interfaces Movement
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A low-cost transhumeral prosthesis operated via an ML-assisted EEG-head gesture control system.

Journal of neural engineering Feb 7, 2025
Key challenges in upper limb prosthetics include a lack of effective control systems, the often invasive surgical requirements of brain-controlled limbs, and prohibitive costs. As a result, disuse rates remain high despite potential for increased qua...
Artificial Limbs Neural Networks, Computer Machine Learning Prosthesis Design Adult Head Male Humans Gestures Electroencephalography
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Deep Multiview Module Adaption Transfer Network for Subject-Specific EEG Recognition.

IEEE transactions on neural networks and learning systems Feb 6, 2025
Transfer learning is one of the popular methods to solve the problem of insufficient data in subject-specific electroencephalogram (EEG) recognition tasks. However, most existing approaches ignore the difference between subjects and transfer the same...
Humans Neural Networks, Computer Algorithms Seizures Pattern Recognition, Automated Imagination Databases, Factual Deep Learning Electroencephalography
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A multi-domain feature fusion epilepsy seizure detection method based on spike matching and PLV functional networks.

Journal of neural engineering Feb 5, 2025
The identification of spikes, as a typical characteristic wave of epilepsy, is crucial for diagnosing and locating the epileptogenic region. The traditional seizure detection methods lack spike features and have low sample richness. This paper propos...
Humans Neural Networks, Computer Seizures Nerve Net Action Potentials Electroencephalography Epilepsy
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FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training.

Journal of neural engineering Feb 4, 2025
Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous concern in neurophysiological signal processing, particularly for enhancing the signal-to-noise ratio in electroencephalograph (EEG) analysis. Novel metho...
Humans Signal-To-Noise Ratio Signal Processing, Computer-Assisted Attention Neural Networks, Computer Artifacts Deep Learning Electroencephalography
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EEG-based fatigue state evaluation by combining complex network and frequency-spatial features.

Journal of neuroscience methods Feb 3, 2025
BACKGROUND: The proportion of traffic accidents caused by fatigue driving is increasing year by year, which has aroused wide concerns for researchers. In order to rapidly and accurately detect drivers' fatigue, this paper proposed an electroencephalo...
Humans Neural Networks, Computer Brain Signal Processing, Computer-Assisted Automobile Driving Fatigue Adult Male Electroencephalography
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Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.

Scientific reports Feb 3, 2025
Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. However, it ignores the three-dimensional spatial characteristics of EEG signals and cannot dynamically learn the...
Graph Neural Networks Electroencephalography Schizophrenia Nerve Net Convolutional Neural Networks Models, Neurological Humans Algorithms Female Adult Male
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Enhanced electroencephalogram signal classification: A hybrid convolutional neural network with attention-based feature selection.

Brain research Feb 2, 2025
Accurate recognition and classification of motor imagery electroencephalogram (MI-EEG) signals are crucial for the successful implementation of brain-computer interfaces (BCI). However, inherent characteristics in original MI-EEG signals, such as non...
Electroencephalography Signal Processing, Computer-Assisted Attention Brain-Computer Interfaces Imagination Male Deep Learning Convolutional Neural Networks Humans Neural Networks, Computer Brain
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Multi-branch convolutional neural network with cross-attention mechanism for emotion recognition.

Scientific reports Feb 1, 2025
Research on emotion recognition is an interesting area because of its wide-ranging applications in education, marketing, and medical fields. This study proposes a multi-branch convolutional neural network model based on cross-attention mechanism (MCN...
Electroencephalography Attention Humans Neural Networks, Computer Algorithms Convolutional Neural Networks Emotions
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Integrating neuroscience and artificial intelligence: EEG analysis using ensemble learning for diagnosis Alzheimer's disease and frontotemporal dementia.

Journal of neuroscience methods Jan 31, 2025
BACKGROUND: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are both progressive neurological disorders that affect the elderly. Distinguishing between individuals suffering from these two diseases in the early stages can be quite challeng...
Frontotemporal Dementia Aged Ensemble Learning Machine Learning Humans Electroencephalography Algorithms Neurosciences Male Artificial Intelligence Female Middle Aged Signal Processing, Computer-Assisted Alzheimer Disease
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