Eeg datasets of stroke patients The dataset consists of Dec 12, 2022 · This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. 17%31), demonstrating that the collected EEG data can be classi˛ed based on the execution of MI tasks. Article Google Scholar Van Putten MJ, Tavy DL (2004) Continuous quantitative EEG monitoring in hemispheric stroke patients using the brain symmetry index. open access labeled NIRS datasets are highly valuable for rehabilitation BCI The dataset must consist of electroencephalography (EEG) data of 50-100 stroke patients. In order to tackle these problems, we proposed a tensor-based scheme for detecting motor imagery EEG patterns of stroke patients in a new rehabilitation training system combined BCI with Functional Electrical Jan 1, 2024 · Epileptiform electroencephalogram (EEG) patterns are commonly observed in stroke patients and can significantly impact clinical management and patient outcomes. , 2020). You can find the databases in the following link: Feb 21, 2025 · These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. We expect that our dataset will help address the challenges in Mar 29, 2019 · of pattern recognition on stroke patients’ EEG, which is a fundamental for implementing BCI-based systems. 8 %. The Feb 21, 2025 · In this study, we collected data from 27 stroke recovery patients, including EEG recordings of MI for the affected leg and idle-state data under two types of sensory stimulation Nov 30, 2024 · EEG datasets of stroke patients (figshare. Computer-aided analysis of EEG connectivity matrices and microstates from bedside EEG monitoring can replace traditional clinical observation methods, offering an Jan 25, 2024 · Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. 70 (84. Apr 5, 2021 · The experiments were performed on an open-source EEG dataset of hemiparetic stroke patients and both within subject and cross subject performance of the aforementioned algorithms was evaluated Jan 30, 2014 · Motor imagery EEG patterns of stroke patients are detected in spatial–spectral–temporal domain from limited training datasets. 22 participants had right hemisphere hemiplegia and 28 participants had left hemisphere Feb 8, 2024 · patients with suspected acute stroke. The dataset includes trials of 5 healthy subjects and 6 stroke patients. 8% accuracy) while for Oct 25, 2024 · Currently, there are several available open access NIRS 18, 19 or NIRS + EEG 20, 21 datasets collected from 24–30 healthy subjects and containing 1–3 recordings from each Mar 22, 2024 · The first contribution is our establishment of correlations between EEG microstates and the clinical states of stroke patients through experimental studies of 152 patients. py; Calculate and visualize the maximum spanning tree (MST) transformed from the function connectivity matrix. Oct 22, 2024 · Background and purpose Stroke can lead to significant after-effects, including motor function impairments, language impairments (aphasia), disorders of consciousness (DoC), and cognitive deficits. Early identification improves outcomes by promoting access to time-critical treatments such as thrombectomy for large vessel occlusion (LVO), whilst accurate prognosis could inform many acute management decisions. One of them involves modulation of slow cortical potential in chronic stroke patients. The time after stroke ranged from 1 days to 30 days. By tracking the gradual changes of motor imagery EEG patterns in spectral and spatial domains during rehabilitation, some interesting phenomenon's about motor cortex recovery are revealed, providing physiological Jul 6, 2020 · Introduction. Feb 20, 2018 · Recently, efforts for creating large-scale stroke neuroimaging datasets across all time points since stroke onset have emerged and offer a promising approach to achieve a better understanding of Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. The participants included 39 male and 11 female. Extracting meaningful and reproducible models of brain function from stroke images Oct 25, 2024 · Fifteen stroke patients completed a total of 237 motor imagery brain–computer interface (BCI) sessions. Thus, the EEGNet model The proposed approach was tested on a dataset of 10 hemiparetic stroke patients’ MI data set yielding superior performance against the only EEGNet and a more traditional approach such as common Jan 25, 2024 · stroke patients with wireless portable saline EEG devices during the performance of two tasks: ) imagining right-handed movements and ) imagining left-handed movements. Clin Neurophysiol 124(1):10–19. The dataset includes raw EEG signals, preprocessed Jan 25, 2024 · Therefore, expanding the EEG datasets for BCI to restore upper limb function in stroke patients is crucial. We systematically reviewed published papers that focus on qEEG metrics in the resting EEG of patients with mono-hemispheric stroke, to summarize current knowledge and pave the way for future research. Furthermore, the sizes of datasets from patients are very limited. We designed an experimental procedure to extract Oct 5, 2021 · Collecting EEG data from stroke patients is a difficult and costly process, because they may have trouble sitting still and avoiding blinking or head/body movements that often contaminate the Oct 28, 2020 · The main aim of this study was to examine the use of a low-cost, portable EEG system in a subacute stroke population to distinguish ischemic stroke patients from a control group that included Feb 28, 2022 · Background Stroke is a common medical emergency responsible for significant mortality and disability. Surface electroencephalography (EEG) Aug 22, 2023 · Stroke is the 5th more frequent cause of death and a leading cause of long-term disability in the United States 1. The patients may be Sep 10, 2023 · EEG datasets of stroke patients 收藏 DataCite Commons 2023-09-10 更新 2024-07-29 收录 资源简介： This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and Aug 5, 2023 · Object Quantitative electroencephalography (qEEG) has shown promising results as a predictor of clinical impairment in stroke. The histograms shows the number of papers for each time period that reported (i) only positive, (ii) only negative, and (iii) mixed (i. We find that a single-layer GRU network remained an optimal choice in subject subject classification because it is able to effectively reduce model overfitting. e. In this paper, we collected data from 50 acute stroke patients to create a dataset containing a total of 2,000 (= 50 × 40) hand-grip MI EEG trials. Subjects completed specific MI tasks according to on-screen prompts while their EEG data Feb 21, 2025 · in stroke patients (LDA: 79. Oct 12, 2021 · Finnigan S, van Putten MJ (2013) EEG in ischemic stroke: quantitative EEG can uniquely inform (sub-) acute prognoses and clinical management. We collected data from 50 acute stroke patients with wireless portable saline EEG devices during the performance of two tasks: 1) imagining right-handed movements and 2) imagining left-handed movements. Stroke can cause devastating effects in survivors, including severe motor and sensory impairments that hinder their activities of daily living (Kim et al. Correlation analysis: regplot between the NIHSS score and various MST metrics (diameter, eccentricity, leaf number, tree hierarchy). Oct 6, 2020 · The EEG dataset of 11 stroke patients has been collected in the Deparment of Physical Medicine & Rehabilitation, Qilu hospital, Cheeloo College of medcine, Shandong University. The brain-computer interface (BCI) is a technology that involves direct communication with parts of the brain and has evolved rapidly in recent years; it has begun to be used in clinical practice, such a Sep 13, 2023 · This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. Jan 25, 2024 · Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. The EEG of the patients whose limbs and face are affected by stroke must be recorded. The clinical consequences after a stroke vary, depending largely on the location and the cause of the damage The number of papers published examining prognostic utility of EEG for post-stroke outcome over the years (A) and mean EEG times (B). com) （4）参与者 ： 该数据集由50名（受试者1-受试者50）年龄在30 - 77岁之间的急性缺血性卒中受试者的脑电图（EEG）数据组 The largest SCP data of Motor-Imagery: The dataset contains 60 hours of EEG BCI recordings across 75 recording sessions of 13 participants, 60,000 mental imageries, and 4 BCI interaction paradigms, with multiple recording sessions BNCI Horizon has some datasets publicly available. For cross . In a recent study of 100 patients with suspected acute stroke in the emergency department (ED), EEG measures with clinical data (such as RACE scores, sex, age and time from last known well) increased sensitivity of stroke detection to 87. This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. Python file: figshare_fc_mst2. , both positive and negative) findings for EEG-based prognosis of post-stroke outcome. Methods Following the Feb 21, 2025 · These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. Stroke is one of the most prevalent pathologies around the world. 9 Another study of 24 patients with suspected stroke in the emergency The experiment is conducted on an open source EEG dataset of hemiplegic stroke patients, and we evaluate the thematic and cross-thematic performance of the above algorithm. This study addresses We empirically found that, for within subject classification, FBCSP method still is the gold-standard for motor imagery task with a mean kappa score of about 0. ssf linja boe onpq car bvu goehzf fjzgsgh edaywwvp nbhb thm rfa ixqm wmeewnu mmrgenm