Seizures

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

Retinotopic tuning of neural populations is a key organizing principle of human visual cortex. However, state-of-the-art models that predict neural recordings based on task-optimized Convolutional Neural Networks (CNNs) do not take this retinotopic organization into account. Furthermore, while retinotopic tuning in visual cortex has been studied extensively using functional magnetic resonance imag...

Hybrid CNN transformer framework for EEG-based epileptic seizure detection.

Early diagnosis and early intervention are important in the treatment of epilepsy, so detecting epileptic seizures from EEG signal is very important. However, the non-stationary nature of EEG signals, inter-subject variability and artefacts arising from noise are major challenges to the development of a robust, generalisable and real-time automated seizure detection system. Many current deep learn...

Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures.

The structural and functional connectivity of the brain network is a combination of complex connections and interconnections among neurons of differen...

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Accurate and objective identification of Parkinson's Disease (PD) from Electroencephalogram (EEG) signals is important because EEG responses are compl...

Abrupt Scene Onsets and Gradually Emerging Scene Information Produce Distinct EEG Decoding Dynamics.

Multivariate analyses of M/EEG data are typically performed on neural responses time-locked to discrete stimulus onsets. Such designs usually reveal h...

CastNet: A three-channel EEG-based deep learning model for cross-subject depression detection.

Depression is a serious mental health condition affecting millions worldwide. In recent years, deep learning models achieved remarkable performance in...

An Upper-Limb Motor Imagery EEG Dataset of Chronic Stroke Patients.

Motor imagery (MI)-based brain-computer interface (BCI) systems offer a promising approach for post-stroke motor rehabilitation. However, their clinic...

[An electroencephalogram-based emotion recognition method using multi-branch convolutional neural networks and Transformer].

Electroencephalogram (EEG)-based emotion recognition is an important research area in affective computing and mental health assessment. To address the...

[A generalizable epilepsy detection network based on dual-attention mechanism].

Existing deep learning models for epileptic electroencephalogram (EEG) signal analysis frequently overlook intrinsic pathological characteristics duri...

Accuracy of the rapid-response electroencephalography's Automated Seizure Burden Estimator: A follow-up validation study of version 8 (AccuRASE II).

OBJECTIVE: Ceribell Inc.'s point-of-care electroencephalographic (EEG) system and artificial intelligence-based Automated Seizure Burden Estimator (AS...

EEG Connectivity Signatures in Active vs. Passive Mental Fatigue Settings.

In real-world occupational settings, mental fatigue commonly emerges from the combination of sleep deprivation with prolonged cognitive and physical w...

Efficient epileptic seizure prediction using single channel EEG signal and knowledge distillation on deep neural networks.

Epilepsy is a common neurological disease, and in some patients, abnormal changes in brain activity typically begin before the onset of a seizure. Ele...

Aggregating XAI-based explanations to identify spectral-spatial patterns in CNN-based resting-state EEG classification.

Convolutional neural networks (CNNs) achieve high performance in electroencephalographic (EEG) classification tasks; however, their decision-making me...

Decoding motor imagery related to major mimetic muscles from electroencephalography.

BACKGROUND: Functional and aesthetic deficits in individuals with facial nerve paralysis (FNP) significantly impair their quality of life. By decoding...

SinTransNet: an EEG-based deep learning framework for infantile epileptic spasms syndrome detection.

Infantile Epileptic Spasms Syndrome (IESS) represents a severe form of developmental epileptic encephalopathy in infancy, characterized by clusters of...

Vision-Based Artificial Intelligence Technologies for Epilepsy Monitoring: Scoping Review and Taxonomy Development Study.

BACKGROUND: Artificial intelligence (AI) technologies for vision-based epilepsy monitoring are advancing rapidly in health care. Despite growing resea...

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

Accurate and adaptive time-frequency representation is essential for analyzing nonstationary signals in critical applications, such as epileptic seizu...

Aggression in epilepsy and sleep: from historical accounts to brain networks and human behaviour.

For a long time, epilepsy has been associated with violent behaviour, acquiring a highly stigmatising reputation, shaped mainly by 19th-century medica...

Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks.

OBJECTIVE: Accurate and reliable neural decoding of locomotion holds promise for advancing clinical applications such as rehabilitation and prosthetic...

Bayesian Uncertainty-aware Deep Learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection.

Deep learning is advancing EEG processing for automated epileptic seizure detection and onset zone localization, yet its performance relies heavily on...