Seizures

SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification.

In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classification accuracy. To do so, we propose a weighted neighborhood field graph (WNFG) to represent EEG signals. The WNFG reduces redundant edges between graph ...

Brain Emotion Perception Inspired EEG Emotion Recognition With Deep Reinforcement Learning.

Inspired by the well-known Papez circuit theory and neuroscience knowledge of reinforcement learning, a double dueling deep Q network (DQN) is built incorporating the electroencephalogram (EEG) signals of the frontal lobe as prior information, which is named frontal lobe double dueling DQN (FLD3QN). The framework of FLD3QN is constructed in accord with the brain emotion mechanism which takes the f...

Artificial intelligence and telemedicine in epilepsy and EEG: A narrative review.

The emergence of telemedicine and artificial intelligence (AI) has set the stage for a possible revolution in the future of medicine and neurology inc...

CTNet: a convolutional transformer network for EEG-based motor imagery classification.

Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human inter...

Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks.

In brain-computer interface (BCI), building accurate electroencephalogram (EEG) classifiers for specific mental tasks is critical for BCI performance....

Bio-inspired EEG signal computing using machine learning and fuzzy theory for decision making in future-oriented brain-controlled vehicles.

One kind of autonomous vehicle that can take instructions from the driver by reading their electroencephalogram (EEG) signals using a Brain-Computer I...

Schizophrenia diagnosis using the GRU-layer's alpha-EEG rhythm's dependability.

Verifying schizophrenia (SZ) can be assisted by deep learning techniques and patterns in brain activity observed in alpha-EEG recordings. The suggeste...

MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces.

Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex...

Online Privacy-Preserving EEG Classification by Source-Free Transfer Learning.

Electroencephalogram (EEG) signals play an important role in brain-computer interface (BCI) applications. Recent studies have utilized transfer learni...

Characterizing Autism Spectrum Disorder Through Fusion of Local Cortical Activation and Global Functional Connectivity Using Game-Based Stimuli and a Mobile EEG System.

The deficit in social interaction skills among individuals with autism spectrum disorder (ASD) is strongly influenced by personal experiences and soci...

Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection.

Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed t...

Research on low-power driving fatigue monitoring method based on spiking neural network.

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance f...

Evaluation of perceived urgency from single-trial EEG data elicited by upper-body vibration feedback using deep learning.

Notification systems that convey urgency without adding cognitive burden are crucial in human-computer interaction. Haptic feedback systems, particula...

Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.

The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer int...

SFT-SGAT: A semi-supervised fine-tuning self-supervised graph attention network for emotion recognition and consciousness detection.

Emotional recognition is highly important in the field of brain-computer interfaces (BCIs). However, due to the individual variability in electroencep...

Hybrid similarity based feature selection and cascade deep maxout fuzzy network for Autism Spectrum Disorder detection using EEG signal.

Autism Spectrum Disorder (ASD) is a neurological disorder that influences a person's comprehension and way of behaving. It is a lifetime disability th...

Inter-participant transfer learning with attention based domain adversarial training for P300 detection.

A Brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential...

XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG.

Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the obs...

Independent Vector Analysis for Feature Extraction in Motor Imagery Classification.

Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statisti...

Continual learning for seizure prediction via memory projection strategy.

Despite extensive algorithms for epilepsy prediction via machine learning, most models are tailored for offline scenarios and cannot handle actual sce...