AIMC Topic: Electroencephalography
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MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces.
IEEE transactions on cybernetics
Aug 26, 2024
Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. De...
Characterizing Autism Spectrum Disorder Through Fusion of Local Cortical Activation and Global Functional Connectivity Using Game-Based Stimuli and a Mobile EEG System.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 26, 2024
The deficit in social interaction skills among individuals with autism spectrum disorder (ASD) is strongly influenced by personal experiences and social environments. Neuroimaging studies have previously highlighted the link between social impairment...
Online Privacy-Preserving EEG Classification by Source-Free Transfer Learning.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 26, 2024
Electroencephalogram (EEG) signals play an important role in brain-computer interface (BCI) applications. Recent studies have utilized transfer learning to assist the learning task in the new subject, i.e., target domain, by leveraging beneficial inf...
Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection.
International journal of neural systems
Aug 23, 2024
Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed to automatically learn electroencephalography (EEG) features for seizure detection, the generalizatio...
Research on low-power driving fatigue monitoring method based on spiking neural network.
Experimental brain research
Aug 23, 2024
Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detect...
Evaluation of perceived urgency from single-trial EEG data elicited by upper-body vibration feedback using deep learning.
Scientific reports
Aug 23, 2024
Notification systems that convey urgency without adding cognitive burden are crucial in human-computer interaction. Haptic feedback systems, particularly those utilizing vibration feedback, have emerged as a compelling solution, capable of providing ...
Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.
IEEE journal of translational engineering in health and medicine
Aug 23, 2024
The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the eff...
Inter-participant transfer learning with attention based domain adversarial training for P300 detection.
Neural networks : the official journal of the International Neural Network Society
Aug 22, 2024
A Brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which requires training. This training process ...
Hybrid similarity based feature selection and cascade deep maxout fuzzy network for Autism Spectrum Disorder detection using EEG signal.
Computational biology and chemistry
Aug 22, 2024
Autism Spectrum Disorder (ASD) is a neurological disorder that influences a person's comprehension and way of behaving. It is a lifetime disability that cannot be completely treated using any therapy up to date. Nevertheless, in time identification a...
SFT-SGAT: A semi-supervised fine-tuning self-supervised graph attention network for emotion recognition and consciousness detection.
Neural networks : the official journal of the International Neural Network Society
Aug 22, 2024
Emotional recognition is highly important in the field of brain-computer interfaces (BCIs). However, due to the individual variability in electroencephalogram (EEG) signals and the challenges in obtaining accurate emotional labels, traditional method...
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