Neurology

Seizures

Latest AI and machine learning research in seizures for healthcare professionals.

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Latent alignment in deep learning models for EEG decoding.

. Brain-computer interfaces (BCIs) face a significant challenge due to variability in electroencepha...

A combination of deep learning models and type-2 fuzzy for EEG motor imagery classification through spatiotemporal-frequency features.

Developing a robust and effective technique is crucial for interpreting a user's brainwave signals a...

BrainForest: Neuromorphic Multiplier-Less Bit-Serial Weight-Memory-Optimized 1024-Tree Brain-State Classification Processor.

Personalized brain implants have the potential to revolutionize the treatment of neurological disord...

Low-Power and Low-Cost AI Processor With Distributed-Aggregated Classification Architecture for Wearable Epilepsy Seizure Detection.

Wearable devices with continuous monitoring capabilities are critical for the daily detection of epi...

RVDLAHA: An RISC-V DLA Hardware Architecture for On-Device Real-Time Seizure Detection and Personalization in Wearable Applications.

Epilepsy is a globally distributed chronic neurological disorder that may pose a threat to life with...

A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus.

Quantifying emesis in Suncus murinus (S. murinus) has traditionally relied on direct observation or ...

PhysioEx: a new Python library for explainable sleep staging through deep learning.

Sleep staging is a crucial task in clinical and research contexts for diagnosing and understanding s...

Pseudo-HFOs Elimination in iEEG Recordings Using a Robust Residual-Based Dictionary Learning Framework.

High-frequency oscillations (HFOs) in intracranial EEG (iEEG) recordings are critical biomarkers for...

Multiclass Classification Framework of Motor Imagery EEG by Riemannian Geometry Networks.

In motor imagery (MI) tasks for brain computer interfaces (BCIs), the spatial covariance matrix (SCM...

Incremental Classification for High-Dimensional EEG Manifold Representation Using Bidirectional Dimensionality Reduction and Prototype Learning.

In brain-computer interface (BCI) systems, symmetric positive definite (SPD) manifold within Riemann...

EEG Temporal-Spatial Feature Learning for Automated Selection of Stimulus Parameters in Electroconvulsive Therapy.

The risk of adverse effects in Electroconvulsive Therapy (ECT), such as cognitive impairment, can be...

Unlocking Dreams and Dreamless Sleep: Machine Learning Classification With Optimal EEG Channels.

Research suggests that dreams play a role in the regulation of emotional processing and memory conso...

EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals.

BACKGROUND AND OBJECTIVE: Deep learning models have gained widespread adoption in healthcare for acc...

Enhanced EEG-based cognitive workload detection using RADWT and machine learning.

Understanding cognitive workload improves learning performance and provides insights into human cogn...

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 ...

A low-cost transhumeral prosthesis operated via an ML-assisted EEG-head gesture control system.

Key challenges in upper limb prosthetics include a lack of effective control systems, the often inva...

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...

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 diagnosin...

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 conc...

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...

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