Seizures

A novel deep learning model combining 3DCNN-CapsNet and hierarchical attention mechanism for EEG emotion recognition.

Emotion recognition plays a key role in the field of human-computer interaction. Classifying and predicting human emotions using electroencephalogram (EEG) signals has consistently been a challenging research area. Recently, with the increasing application of deep learning methods such as convolutional neural network (CNN) and channel attention mechanism (CA). The recognition accuracy of emotion r...

Explaining electroencephalogram channel and subband sensitivity for alcoholism detection.

Alcoholism, a progressive loss of control over alcohol consumption, deteriorates mental and physical health over time. Automatic alcoholism detection can aid in early interventions and timely corrective actions. For this purpose, electroencephalogram (EEG) signals are investigated using explainable artificial intelligence (XAI) techniques to obtain biomarkers. EEG signals are decomposed into five ...

A systematic literature review of machine learning techniques for the detection of attention-deficit/hyperactivity disorder using MRI and/or EEG data.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition common in teenagers across the globe. Neuroimaging and Machine Learn...

Latent alignment in deep learning models for EEG decoding.

. Brain-computer interfaces (BCIs) face a significant challenge due to variability in electroencephalography (EEG) signals across individuals. While r...

Efficient Neural Network Classification of Parkinson's Disease and Schizophrenia Using Resting-State EEG Data.

Timely identification of Parkinson's disease and schizophrenia is crucial for the effective management and enhancement of patients' quality of life. T...

A hybrid optimization-enhanced 1D-ResCNN framework for epileptic spike detection in scalp EEG signals.

In order to detect epileptic spikes, this paper suggests a deep learning architecture that blends 1D residual convolutional neural networks (1D-ResCNN...

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 accurately in the realm of biomedical signal proces...

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 device...

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 accura...

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...