Academic Articles & Research Papers

Consensus document on electroencephalography education in anaesthesiology: defining learning outcomes: A modified four-round Delphi study.

On-the-Fly Sleep Scoring Algorithm with Heart Rate, RR Intervals and Accelerometer as Input

In many applications, recognizing the depth of sleep (e.g., light, deep, REM sleep) while the subject is sleeping enables innovative features. For instance, in SAE Level 4 autonomous driving, a driver may need to takeover the vehicle contro...

Evaluating the influence of anatomical accuracy and electrode positions on EEG forward solutions

Generating realistic volume conductor models for forward calculations in electroencephalography (EEG) is not trivial and several factors contribute to the accuracy of such models, two of which are its anatomical accuracy and the accuracy wi...

Alpha-Band EEG Modulation as a Potential Aged-Related Biomarker for Biofeedback-Driven Motor–Cognitive Adaptability

Background: Age-related changes in motor–cognitive function are associated with altered neural adaptability, particularly during tasks requiring integration of cognitive and motor processes. Dual-task paradigms are commonly used to assess s...

Medical Informatics: An Introduction

1. Information Processing in Medicine.- 1.1 Introduction.- 1.2 Overlap With Other Disciplines.- 2. Informatics.- 2.1 Structure.- 2.2 Fundamental Concepts.- 2.2.1 Message and Information.- 2.2.1.1 Events, Probability.- 2.2.1.2 SHANNON'S Info...

Adaptive frequency band attention-guided CNN–BiLSTM for Spatial–Spectral–Temporal EEG emotion recognition

Abstract Electroencephalography (EEG)-based emotion recognition has gained increasing attention in affective computing because EEG provides high temporal resolution and reflects intrinsic neural activity. However, reliable emotion recogniti...

Neuromodulation Effect According to Lesion Location After Dual-Mode Brain Stimulation in Patients with Subacute Stroke: A Preliminary Study

Dual-mode non-invasive brain stimulation using repetitive transcranial magnetic stimulation and transcranial direct current stimulation is known to help neurorehabilitation in patients with stroke. However, this neuromodulation effect may v...

Bridging Deep Learning and Quantum Computing for EEG-Driven P300 Targeted ASD Response Detection

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose diagnosis relies heavily on subjective behavioral assessments. Electroencephalography (EEG) provides an inexpensive and non-invasive method of objective biomarker detect...

A Novel Depression Rehabilitation Game Framework Using VR Based on Neurofeedback

At present,depression is treated with antidepressant drugs and assisted with psychotherapy and physical therapy,and these expensive and time-consuming treatment methods often end prematurely and result in a prolonged course without effectiv...

Deep learning for electroencephalogram (EEG) classification tasks: a review

OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analyti...

Deep Learning and Its Applications in Biomedicine

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitat...

Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?

In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images i...

Machine learning applications in epilepsy

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recogn...

EEG Sensor-Based Computational Model for Personality and Neurocognitive Health Analysis Under Social Stress

This paper introduces an innovative EEG sensor-based computational framework that establishes a pioneering nexus between personality trait quantification and neural dynamics, leveraging biosignal processing of brainwave activity to elucidat...

Integrating task-state electroencephalography and motor function for intelligent assessment of recovery status from prolonged disorders of consciousness

Abstract Background Accurately assessing recovery in patients with prolonged disorders of consciousness (PDoC) is essential for evaluating the therapeutic efficacy and optimizing clinical care. However, substantial individual variability an...

A Novel Approach for Multichannel Epileptic Seizure Classification Based on Internet of Things Framework Using Critical Spectral Verge Feature Derived from Flower Pollination Algorithm

A novel approach for multichannel epilepsy seizure classification which will help to automatically locate seizure activity present in the focal brain region was proposed. This paper suggested an Internet of Things (IoT) framework based on a...