Seizures

EEG Acquisition and Motor Imagery Classification for Robotic Control.

The adoption of brain-computer interfaces (BCIs) has significantly increased in various application domains, particularly in the field of controlling robotic systems through motor imagery. The article contributes in two primary ways: 1) validating the effectiveness of using a minimally invasive electroencephalography (EEG) device combined with machine learning techniques to control fundamental mov...

Neurocognitive assessment under various human-robot teaming environments.

Human-robot teaming has become increasingly important with the advent of intelligent machines. Prior efforts suggest that performance, mental workload, and trust are critical elements of human-robot dynamics that can be altered by the robot's behavior. Most prior human-robot teaming studies used behavioral analyses, but a limited number used neural markers, without the use of physical robots and c...

Challenging Deep Learning Methods for EEG Signal Denoising under Data Corruption.

Capturing informative electroencephalogram (EEG) signals is a challenging task due to the presence of noise (e.g., due to human movement). In extreme ...

TAU-DI Net: A Multi-Scale Convolutional Network Combining Prob-Sparse Attention for EEG-based Depression Identification.

EEG-based detection of major depression disorder (MDD) plays a pivotal role in the subsequent treatment and recovery. With the rapid development of de...

An Efficient Deep Transfer Learning Network for Characterization of Stroke Patients' Motor Execution from Multi-Channel EEG-Recordings.

Recent advances in stroke rehabilitation technology have been focused on developing Intelligent Rehabilitation Robots (IRR) that can effectively engag...

Complexity Analysis based on Parietal Fuzzy Entropy to Facilitate ADHD Diagnosis in Young Children.

Attention deficit hyperactivity disorder (ADHD) is the most common condition affecting the development of neurons in children. Therefore, early and ac...

Decoding Visual Perception from EEG Using Explainable Graph Neural Network.

Brain decoding is an emerging area in the fields of neuroscience and machine learning. The goal of decoding is to utilize measured brain activity to u...

Mapping Cognitive Engagement: EEG and Graph Theory Analysis of Brain Region Involvement in Supernumerary Robotic Finger Utilization.

As the worldwide incidence of stroke increases, supernumerary robotic limbs (SRLs), more specifically supernumerary robotic fingers (SRFs), present a ...

Interpretable SincNet-Based Spatiotemporal Neural Network for Seizure Prediction.

Spatiotemporal convolutional neural networks (CNNs) have emerged as potent tools for seizure prediction (SP) using electroencephalogram (EEG) signals,...

Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data.

While deep learning methods are increasingly applied in research contexts for neuropsychiatric disorder diagnosis, small dataset size limits their pot...

Transformer-based Model Captures Neural Representation Differences between Nouns and Verbs in Spoken Narratives.

Nouns and verbs constitute the fundamental elements of human language systems. Abundant studies have demonstrated that nouns and verbs exhibit differe...

Epileptic State Prediction using Phase Space Domain and Machine Learning Algorithms.

Epilepsy is a disease of the brain that causes unprovoked or reflex seizures that affects millions of individuals worldwide. Traditionally, identifyin...

Knowledge-guided EEG Representation Learning.

Self-supervised learning has produced impressive results in multimedia domains of audio, vision and speech. This paradigm is equally, if not more, rel...

An Explainable Transfer Learning Method for EEG-based Seizure Type Classification.

Epilepsy, traditionally conceptualized as a neurological disorder characterized by a persistent inclination toward epileptic seizures, is commonly dia...

Bi-hemisphere Interaction Convolutional Neural Network for Motor Imagery Classification.

Decoding EEG-based, Motor Imagery Brain-Computer Interfaces (MI-BCI) in a subject-independent manner is very challenging due to high dimensionality of...

Transformer-Based Wavelet-Scalogram Deep Learning for Improved Seizure Pattern Recognition in Post-Hypoxic-Ischemic Fetal Sheep EEG.

Hypoxic-ischemic (HI) events in newborns can trigger seizures, which are highly associated with later neurodevelopmental impairment. The precise detec...

Enhancing Epileptic Seizure Detection with Random Input Selection in Graph-Wave Networks.

Graph neural networks show strong capability of learning spatial relationships between channels. In recent studies, they greatly advanced automatic ep...

LightIED: Explainable AI with Light CNN for Interictal Epileptiform Discharge Detection.

Interictal epileptic discharge (IED) detection from electroencephalography (EEG) is an important but difficult step in the epilepsy diagnosis. To redu...

Reconstruction of Continuous Hand Grasp Movement from EEG Using Deep Learning.

Brain-Computer Interface (BCI) is a promising neu-rotechnology offering non-muscular control of external devices, such as neuroprostheses and robotic ...

Machine Learning Approach for Music Familiarity Classification with Single-Channel EEG.

Recognition of familiar music on brainwaves through machine learning (ML) can be instrumental in innovative therapeutic devices that improve memory an...