Seizures

Exploring the potential of explainable deep learning for EEG-based cognitive decline prediction.

OBJECTIVE: Detecting Alzheimer's disease (AD) at an early stage is essential for administering effective treatments and preventing neuronal damage. Unfortunately, current diagnostic techniques are often invasive and expensive. Our research focuses on creating a cost-effective and non-invasive method for the early detection of cognitive decline. METHODS: Using a publicly available dataset of restin...

Memristive Reservoir Computing Circuit for Real-Time Prediction of Epilepsy.

The prediction of epileptic seizures can significantly improve patients' quality of life by enabling timely preventive interventions. However, realizing automated real-time prediction on edge hardware remains challenging due to high computational complexity, inefficient temporal signal processing, and the von Neumann bottleneck. In this work, we propose a memristor-based multi-stage reservoir comp...

TPCNet: A Temporal Periodicity Convolutional Network for motor imagery EEG decoding in stroke patients.

BACKGROUND: Stroke caused by vascular rupture or blockage has high incidence and leads to significant disability. Motor imagery (MI) electroencephalog...

VSSI2p-Net: Physics-guided deep unfolding with L2p-norm and variation sparsity for EEG source imaging.

Electroencephalogram (EEG) source imaging (ESI) is highly underdetermined, which poses a long-standing challenge in neuroimaging. Traditional methods ...

Optimizing deep CNN architecture via hybrid Harris Hawks arithmetic algorithm for EEG meditation classification.

Meditation is a widely recognized practice that enhances mental well-being and cognitive function. Despite advances in EEG meditation neuroscience, ch...

Theoretical and applied research on spatio-temporal graph attention networks for single-trial P300 detection.

Objective.Accurate detection of single-trial P300 ERPs (event-related potentials) is crucial for developing high-performance non-invasive BCIs (brain-...

Enhancing Anesthetic Depth Assessment via Unsupervised Machine Learning in Processed Electroencephalography Analysis: Novel Methodological Study.

BACKGROUND: General anesthesia comprises 3 essential components-hypnosis, analgesia, and immobility. Among these, maintaining an appropriate hypnotic ...

LSTM-GPT-4 Integration for Interpretable Biomedical Signal Classification.

BACKGROUND: Approximately 3.8 billion people lack access to essential health services, and diagnostic interpretation remains a major bottleneck in rem...

Diagnosis of disorders of consciousness using nonlinear feature derived EEG topographic maps via deep learning.

This study explored the value of nonlinear features extracted from EEG signals to facilitate the assessment of patients with disorders of consciousnes...

Dissociable impacts of perceived race and ascribed status in event-related brain potentials and multivariate network activity.

Humans rapidly and efficiently categorize others with limited information, forming split-second impressions. Prior EEG person perception research has ...

Epileptic Seizure Detection from EEG Signals with Long Short-Term Memory-Transformer and Self-Supervised Learning.

Electroencephalogram (EEG) plays a vital role in seizure detection, yet existing methods often fail to adequately capture the spatiotemporal character...

Multiscale spatiotemporal neural network with multi-attention mechanism using brain partitioning for motor imagery recognition.

BACKGROUND: Motor imagery (MI)-based electroencephalogram (EEG) brain-computer interfaces (BCIs) facilitate communication for motor-impaired patients ...

Comprehensive segmentation of focal cortical dysplasia by combining surface-based and whole-brain MRI deep learning algorithms: a proof-of-concept study.

Introduction.Focal cortical dysplasia type II (FCD II) is a significant cause of drug-resistant epilepsy, and the full surgical resection of the lesio...

Video-based diagnostics supported by artificial intelligence as an opportunity to address the epilepsy diagnostic gap: A narrative review.

Despite advancements in epilepsy care, a substantial diagnostic gap persists, particularly in resource-limited settings. This narrative review explore...

DNA-Driven EEG monitoring for rapid seizure prediction in healthcare.

BACKGROUND AND OBJECTIVE: Worldwide, over 50 million people suffer from epilepsy, a neurological disorder characterised by recurrent seizures due to a...

Characteristics of electroencephalographic changes induced by different hypnotics in elderly patients: a narrative review.

Aging is associated with widespread structural and functional changes in the brain including reduced neural plasticity, slower information processing,...

Identification of cognitive phenotypes in temporal lobe epilepsy and genetic generalized epilepsy using robotic assessment.

BACKGROUND: Cognitive dysfunction is common in people with epilepsy (PWE). Although expectations exist for deficits based on diagnosis, phenotypic var...

A multi-view neural framework with attention for epileptic seizure classification.

Objective. Epilepsy is a chronic brain disorder characterized by recurrent seizures due to abnormal neuronal firing. Electroencephalogram (EEG)-based ...

A two-stage algorithm to detect electrographically focal seizures using a wearable single-channel EEG sensor.

OBJECTIVE: This paper presents a two-stage machine learning model for electrographic seizure detection using wearable single-channel scalp electroence...

A Lightweight Depthwise Separable Convolution and Channel Attention Based GRU Network for Multichannel EEG Seizure Detection.

Epilepsy is the fourth most common neurological disorder, and seizures significantly impact quality of life of affected individuals. Electroencephalog...