Seizures

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 without warning. Therefore, the use of wearable devices for real-time detection and treatment of epilepsy is crucial. Additionally, personalizing disease detection algorithms for individual users is also a challenge in clinical applications. Some studies have proposed seizure detection algorithms with c...

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 epileptic seizures, as they provide users with accurate and comprehensible analytical results. However, current AI classifiers rely on a two-stage recognition process for continuous monitoring, which only reduces operation time but remains challenged by the high cost of additional hardware. To address ...

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 disorders and augment cognition. Medical implants that d...

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 sleep disorders. This work introduces PhysioEx (Phy...

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 reviewing recorded behaviour, which are laborious,...

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 high if an excessive stimulus is applied to induc...

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 Riemannian space has been frequently utilized to extract ...

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) of electroencephalogram (EEG) signals plays a cr...

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 localizing the seizure onset zone (SOZ) in patien...

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 consolidation; electroencephalography (EEG) is useful f...

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 accurate diagnosis through the analysis of brain sign...

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

Understanding cognitive workload improves learning performance and provides insights into human cognitive processes. Estimating cognitive workload fin...

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 invasive surgical requirements of brain-controlled lim...

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 mov...

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 t...

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...