Seizures

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

. 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 applications, especially in areas such as stroke rehabilitation. Electroencephalogram (EEG) signals and functional near-infrared spectroscopy (fNIRS) s...

Deep Multiview Module Adaption Transfer Network for Subject-Specific EEG Recognition.

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 feature representations from source domain to different target domains, resulting in poor transfer performance. To address this issue, we propose a n...

A multi-domain feature fusion epilepsy seizure detection method based on spike matching and PLV functional networks.

The identification of spikes, as a typical characteristic wave of epilepsy, is crucial for diagnosing and locating the epileptogenic region. The tradi...

Machine learning enables high-throughput, low-replicate screening for novel anti-seizure targets and compounds using combined movement and calcium fluorescence in larval zebrafish.

Identifying new anti-seizure medications (ASMs) is difficult due to limitations in animal-based assays. Zebrafish (Danio rerio) serve as a model for c...

FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training.

Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous concern in neurophysiological signal processing, parti...

EEG-based fatigue state evaluation by combining complex network and frequency-spatial features.

BACKGROUND: The proportion of traffic accidents caused by fatigue driving is increasing year by year, which has aroused wide concerns for researchers....

Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.

Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. However, it i...

Specific endophenotypes in EEG microstates for methamphetamine use disorder.

BACKGROUND: Electroencephalogram (EEG) microstates, which reflect large-scale resting-state networks of the brain, have been proposed as potential end...

Enhanced electroencephalogram signal classification: A hybrid convolutional neural network with attention-based feature selection.

Accurate recognition and classification of motor imagery electroencephalogram (MI-EEG) signals are crucial for the successful implementation of brain-...

Multi-branch convolutional neural network with cross-attention mechanism for emotion recognition.

Research on emotion recognition is an interesting area because of its wide-ranging applications in education, marketing, and medical fields. This stud...

Unveiling encephalopathy signatures: A deep learning approach with locality-preserving features and hybrid neural network for EEG analysis.

EEG signals exhibit spatio-temporal characteristics due to the neural activity dispersion in space over the brain and the dynamic temporal patterns of...

Graph convolution network-based eeg signal analysis: a review.

With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalogr...

A temporal-spatial feature fusion network for emotion recognition with individual differences reduction.

PURPOSE: In the context of EEG-based emotion recognition tasks, a conventional strategy involves the extraction of spatial and temporal features, subs...

Prediction of Pharmacoresistance in Drug-Naïve Temporal Lobe Epilepsy Using Ictal EEGs Based on Convolutional Neural Network.

Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epi...

Development and applications of a machine learning model for an in-depth analysis of pentylenetetrazol-induced seizure-like behaviors in adult zebrafish.

Epilepsy, a neurological disorder causing recurring seizures, is often studied in zebrafish by exposing animals to pentylenetetrazol (PTZ), which indu...

Beyond averaging: A transformer approach to decoding event related brain potentials.

The objective of this study is to assess the potential of a transformer-based deep learning approach applied to event-related brain potentials (ERPs) ...

Significance of gender, brain region and EEG band complexity analysis for Parkinson's disease classification using recurrence plots and machine learning algorithms.

Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogr...

Utilizing machine learning techniques for EEG assessment in the diagnosis of epileptic seizures in the brain: A systematic review and meta-analysis.

PURPOSE: Advancements in Machine Learning (ML) techniques have revolutionized diagnosing and monitoring epileptic seizures using Electroencephalogram ...

Machine learning-based algorithm of drug-resistant prediction in newly diagnosed patients with temporal lobe epilepsy.

OBJECTIVES: To develop a predicted algorithm for drug-resistant epilepsy (DRE) in newly diagnosed temporal lobe epilepsy (TLE) patients.

The 'Sandwich' meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding.

. Machine learning has enhanced the performance of decoding signals indicating human behaviour. Electroencephalography (EEG) brainwave decoding, as an...