ArticleBrain sciences2021
Quantitative Evaluation of Task-Induced Neurological Outcome after Stroke.
Article in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it, 89 citations in OpenAlex.
- Cellular and Molecular Targets for Non-Invasive, Non-Pharmacological Therapeutic/Rehabilitative Interventions in Acute Ischemic Stroke.International journal of molecular sciences · 2022Pooled it
- Reduced task-induced frontal midline theta activity in chronic stroke patients compared to healthy older adults - An MEG study.NeuroImage. Clinical · 2026Article
- Interpretable framework for predicting preoperative cardiorespiratory fitness using wearable data.Computer methods and programs in biomedicine · 2025Article
- Eeg Microstates and Balance Parameters for Stroke Discrimination: A Machine Learning Approach.Brain topography · 2025Article
- EEG Spectral Power Changes in Patients With Dysexecutive Syndrome Following Cognitive Intervention.Brain and behavior · 2024Article
- Review
- AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.Sensors (Basel, Switzerland) · 2024Article
- Automatic rehabilitation exercise task assessment of stroke patients based on wearable sensors with a lightweight multichannel 1D-CNN model.Scientific reports · 2024Article
- QEEG indices in traumatic brain injury - insights from the CAPTAIN RTMS trial.Journal of medicine and life · 2024Article
- Quantitative electroencephalography (qEEG), apolipoprotein A-I (APOA-I), and apolipoprotein epsilon 4 (APOE ɛ4) alleles for the diagnosis of mild cognitive impairment and Alzheimer's disease.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
- Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis.Heliyon · 2024Article
- Integrative Approaches in Acute Ischemic Stroke: From Symptom Recognition to Future Innovations.Biomedicines · 2023Review
- A Film Electrode upon Nanoarchitectonics of Bacterial Cellulose and Conductive Fabric for Forehead Electroencephalogram Measurement.Sensors (Basel, Switzerland) · 2023Article
- An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME.Sensors (Basel, Switzerland) · 2023Article
- Effects of nature on restorative and cognitive benefits in indoor environment.Scientific reports · 2023Article
- Task Cortical Connectivity Reveals Different Network Reorganizations between Mild Stroke Patients with Cortical and Subcortical Lesions.Brain sciences · 2023Article
- Quantitative Electroencephalography Analysis for Improved Assessment of Consciousness Levels in Deep Coma Patients Using a Proposed Stimulus Stage.Diagnostics (Basel, Switzerland) · 2023Article
- EEG-Based Emotion Classification in Financial Trading Using Deep Learning: Effects of Risk Control Measures.Sensors (Basel, Switzerland) · 2023Article
- An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model.Sensors (Basel, Switzerland) · 2023Article
- The Increase of Theta Power and Decrease of Alpha/Theta Ratio as a Manifestation of Cognitive Impairment in Parkinson's Disease.Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Electroencephalography (EEG) can access ischemic stroke-derived cortical impairment and is believed to be a prospective predictive method for acute stroke prognostics, neurological outcome, and post-stroke rehabilitation management. This study aims to quantify EEG features to understand task-induced neurological declines due to stroke and evaluate the biomarkers to distinguish the ischemic stroke group and the healthy adult group. We investigated forty-eight stroke patients (average age 72.2 years, 62% male) admitted to the rehabilitation center and seventy-five healthy adults (average age 77 years, 31% male) with no history of known neurological diseases. EEG was recorded through frontal, central, temporal, and occipital cortical electrodes (Fz, C1, C2, T7, T8, Oz) using wireless EEG devices and a newly developed data acquisition platform within three months after the appearance of symptoms of ischemic stroke (clinically confirmed). Continuous EEG data were recorded during the consecutive resting, motor (walking and working activities), and cognitive reading tasks. The statistical results showed that alpha, theta, and delta activities are biomarkers classifying the stroke patients and the healthy adults in the motor and cognitive states. DAR and DTR of the stroke group differed significantly from those of the healthy control group during the resting, motor, and cognitive tasks. Using the machine-learning approach, the C5.0 model showed 78% accuracy for the resting state, 89% accuracy in the functional motor walking condition, 84% accuracy in the working condition, and 85% accuracy in the cognitive reading state for classification the stroke group and the control group. This study is expected to be helpful for post-stroke treatment and post-stroke recovery.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.