Eeg datasets of stroke patients. │ figshare_fc_mst2.

Eeg datasets of stroke patients Building on recent advancements in localizing neural silences, we develop an algorithm that utilizes known spectral properties of The EEG datasets of patients about motor imagery. A common problem in training a classifier from imbalanced datasets is that the trained classifier is more likely to predict a sample as the majority class. For EEG signals from stroke patients, the datasets consist of much more wakeful samples than DoC ones. The time after stroke ranged from 1 days to 30 days. Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. EEG datasets containing other sources, such as medical EEG reports, can be used to automatically label the EEG recordings based on the information contained in the medical reports. Oct 2, 2023 · Discussion. This document also summarizes the reported classification accuracy and kappa values for public MI datasets using deep learning-based approaches, as well as the training and evaluation methodologies used to arrive at the classification between stroke patients and normal people. on stroke, updating previous revisions [12] with a specic focus on dierent qEEG measures as biomarkers of clinical outcome. edu before submitting a manuscript to be published in a peer-reviewed journal using this data, we wish to ensure that the data to be analyzed and interpreted with scientific integrity so as not to mislead the public about Feb 21, 2019 · This dataset is about motor imagery experiment for stroke patients. Jun 15, 2023 · In this study, the electroencephalography (EEG) dataset from post-stroke patients were investigated to identify the effects of the motor imagery (MI)-based BCI therapy by investigating Feb 21, 2019 · This dataset is about motor imagery experiment for stroke patients. Specifically, measured using scalp electroencephalogram (EEG), higher delta power over the bilateral hemispheres correlates with more severe neurological deficits in patients with acute stroke, whereas higher beta power over the bilateral hemispheres correlates with less severe neurological impairment []. These markers are useful for the determination of stroke severity and prediction of functional outcome. Categories. Although the potential of EEG-based efforts for TBI and stroke detection have been demonstrated in some studies, clinical applicability is still in debate [18–21]. npy) to data May 10, 2022 · Compared to our results, one possible reason for the discrepancy is that they used a different method for determining the optimal number of microstate classes and utilized 19-channel EEG data from acute stroke patients, whereas our study used 60-channel EEG data from subacute stroke patients. ˜e EEG dataset is stored in 3D format (M, C, T), where M is the number of trials. 57) (shown in Table 1 ). Traditional CSP is not able to detect the optimal projection direction on such EEG data recorded from stroke patients under the interference of irregular patterns. The participants included 23 males and 4 females, aged between 33 and 68 years. Common Spatial Pattern (CSP) and Support Vector The model relies on a 3-min resting electroencephalogram (EEG) recording from which features can be computed. Results: Using a rich set of features encompassing both the spectral and temporal domains, our model yielded an Jun 1, 2024 · However, recent advances in EEG acquisition hardware, lead technology, and analysis software suggest a larger diagnostic role may be possible for patients with suspected acute stroke. We designed a systematic review to assess the con-tribution of resting-state qEEG in the functional evaluation of stroke patients and answer some crucial questions about where EEG research in stroke is headed. mat │ └─data_load Van Putten MJ, Tavy DL (2004) Continuous quantitative EEG monitoring in hemispheric stroke patients using the brain symmetry index. Non-EEG Dataset for This data set is a series of A dataset of annotated NIHSS scale items and corresponding scores from stroke patients discharge institutional EEG data. Parameters setting and results of EEGNet under two conditions: 1) within-subject classification This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. This paper is organized as follows. We instructed participants to avoid swallowing and eye blinking during the trial period and to avoid any other movement. Keywords. This dataset is about motor imagery experiment for stroke patients. Spatial neglect, a prevalent neurological syndrome in stroke patients Jan 28, 2014 · Early Stroke datasets used to classify corresponding Late Stroke datasets. Dataset Link. Dataset. 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. Efficient classification of EEG from stroke patients is fundamental in the BCI-based stroke rehabilitation systems. Usage metrics. Fifty-four participants (27 stroke patients and 27 healthy age and sex-matched controls) were recruited. However, the effective utilization of EEG data in advancing medical diagnoses and treatment hinges on the availability and quality Jan 22, 2025 · Electroencephalography microstates (EEG-MS) show promise to be a neurobiological biomarker in stroke. Therefore, the classification of the stroke patients in order to identify the subjects with high probability of epileptiform EEG patterns may improve the stroke management. Among the patients, 18 had right hemiplegia, and 9 had left hemiplegia. Therefore, whenever available, the tool needs to be further validated with data from more homogeneous populations of patients. com) （3）下载链接： EEG datasets of stroke patients (figshare. Stroke. motor imagery signals of stroke patients are irregular due to the damage of the specified brain area. We are provided an EEG Dataset of 10 hemiparetic stroke patients having hand functional disability. StrokeRehab dataset helps to build deep learning models that can different motions with sub-second durations. 76% selectivity and 13. Stroke 35(11):2489–2492. │ figshare_fc_mst2. Materials and methods: Using a large-scale, retrospective database of EEG recordings and matching clinical reports, we were able to construct a dataset of 1385 healthy subjects and 374 stroke patients. 582). Nov 5, 2024 · Objective. Each participant received three months of BCI-based MI training with two Feb 21, 2019 · This dataset is about motor imagery experiment for stroke patients. Low-voltage background activity, absence of reactivity, and epileptiform discharges are correlated with worse functional outcomes [ 10 , 12 , 14 Apr 11, 2023 · This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. In Section II, we describe the dataset and modified EEGNet architecture implemented on this patient dataset. Motor Imagery dataset from the Clinical BCI Challenge WCCI-2020. csv │ │ │ └─sourcedata │ ├─sub-01 │ │ sub-01_task-motor-imagery_eeg. 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. In total the dataset is ~150GB, and is thus split into parts based on the Zenodo 50 GB file limit. The document summarizes publicly available MI-EEG datasets released between 2002 and 2020, sorted from newest to oldest. All participants were Feb 21, 2025 · This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. Save the functional connectivity data (imcoh_left. Computer-aided analysis of EEG connectivity matrices and microstates from bedside EEG monitoring can replace traditional clinical observation methods, offering an automatic approach to monitoring 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 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 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. In these datasets, the EEG signal is recorded for 10 min from each patient using the standard 10–20 EEG electrode placement system (Fig. 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 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 Electroencephalography (EEG) based Brain Controlled Prosthetics can potentially improve the lives of people with movement disorders, however, the successful classification of the brain thoughts into correct intended movement is still a challenge. This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. The machine learning algorithms can be applied in many classification problems including the clinical studies. Jan 25, 2024 · Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. Researchers have found that brain–computer interface (BCI) techniques can result in better stroke patient rehabilitation. This leads to inter session inconsistency which is one of the main reason that impedes the widespread adoption of non-invasive BCI for real-world applications, especially in rehabilitation and medicine. Classification. npy and imcoh_right. In this study, the electroencephalography (EEG) dataset from post-stroke patients were investigated to identify the effects of the motor Jan 28, 2014 · Early Stroke datasets used to classify corresponding Late Stroke datasets. Motor imagery (MI)-based brain-computer interfaces (BCI) have shown increased potential for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice has been Functional connectivity and brain network (graph theory) analysis for motor imagery data of stroke patiens. Thus, the aim of the study was to identify biomarkers to discriminate stroke patients from healthy individuals based on EEG-MS and clinical features using a machine learning approach. In this task, subjects use Motor Imagery (MI Jan 1, 2023 · Automated labelling of open-source datasets is a promising approach to increase the number and size of publicly available, labelled datasets. Methods Jan 1, 2024 · Training dataset Features Original Reperfusion treatment, Hypercholesterolemia, Cortex lesion, Sex, Supratentorial stroke, NIHSS at admission, Diabetes, Smoke, Acute infectious state, Number of interested lobes, Type of stroke (ischemic or hemorrhagic), Renal failure, Age, Previous ischemic or hemorrhagic stroke, Coronary disease SMOTENC Sex Jan 30, 2014 · Motor imagery EEG patterns of stroke patients are detected in spatial–spectral–temporal domain from limited training datasets. Be sure to check the license and/or usage agreements for The RST is currently developed based on publicly available patient data in the TUEG. May 1, 2024 · The study focuses on developing EEG markers for patients with ischemic or hemorrhagic stroke. We validate our method approach on a dataset of EEG recordings from 72 stroke patients Feb 29, 2024 · The neurophysiological pattern of cortical rhythms can be changed by an acute stroke []. There were many ways to access data The number of papers published examining prognostic utility of EEG for post-stroke outcome over the years (A) and mean EEG times (B). Please email arockhil@uoregon. In the rehabilitation of arm impairment after stroke, quantifying the training dose (number of repetitions) requires differentiating motions with sub-second durations. Our results suggest that EEG microstates can provide insights into the abnormal brain network dynamics in DOC patients post-stroke. However, stroke patients with different degree of affection might obtain different results, and further research should be conducted to extend our results to other typologies of patients. 1). Domain adaptation and deep learning-based Sep 1, 2022 · The source files and EEG data files in this dataset were organized according to EEG-BIDS 28, which was an extension of the brain imaging data structure for EEG. The EEG datasets of patients about motor imagery. This work validated different methodologies to design decoders of movement intentions for completely paralyzed stroke patients. Methods: We performed a cross-sectional analysis of a cohort study (DEFINE cohort), Stroke arm, with 85 patients, considering demographic, clinical, and stroke characteristics. Feb 28, 2022 · Background Stroke is a common medical emergency responsible for significant mortality and disability. 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. mat │ │ │ │ │ │ │ └─sub-50 │ sub-50_task-motor-imagery_eeg. Datasets and resources listed here should all be openly-accessible for research purposes, requiring, at most, registration for access. In this paper, an adaptive CSP method is proposed to deal with Feb 21, 2019 · This dataset is about motor imagery experiment for stroke patients. Jul 6, 2023 · Although the potential of EEG-based efforts for TBI and stroke detection have been demonstrated in some studies, clinical applicability is still in debate [18–21]. EEG data motor imagery task stroke patient data. In recent years, machine learning based methods, especially deep neural networks, have improved the pattern recognition and classification Mar 9, 2024 · Objective: Investigate the relationship between resting-state EEG-measured brain oscillations and clinical and demographic measures in Stroke patients. 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. Electroencephalography (EEG) has gained significant attention for its potential to revolutionize healthcare applications. mat │ │ │ ├─sub-02 │ │ sub-02_task-motor-imagery_eeg. 2. 0 Jul 21, 2024 · This literature review explores the pivotal role of brain–computer interface (BCI) technology, coupled with electroencephalogram (EEG) technology, in advancing rehabilitation for individuals with damaged muscles and motor systems. Classification results of Late Stroke datasets when training with the corresponding Early Stroke dataset are shown in Table Table8. Data of each frequency band were analyzed, and overall and local EEG signals of stroke patients were Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. One of the mos … Studies have shown that a motor imagery electro encephalogram (EEG)-based brain-computer interface (BCI) system can be used as a rehabilitation tool for stroke patients. With subjects often producing more as B1, B2, B3, and B4. 70 years (SD = 10. 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. py │ figshare_stroke_fc2. This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. 5% to 95% with a median of 75. The histograms shows the number of papers for each time period that reported (i) only positive, (ii) only negative, and (iii) mixed (i. Every patients perform motor imagery instructed by a video. 0%. bdf files are available should you wish to recreate or alter the processing of this dataset. With enough data, techniques such as machine learning may provide the ability to enhance the extraction of characteristic EEG features for TBI and stroke classification. Clinically-meaningful benchmark dataset. Surface electroencephalography (EEG) shows promise for stroke identification and These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. In addition, because of the significant between-participant variability in neuroplasticity in response to rehabilitation Oct 5, 2021 · This study uses the stroke patients’ EEG dataset that includes two types of MI tasks (including left-hand and right-hand tasks). Welcome to the resting state EEG dataset collected at the University of San Diego and curated by Alex Rockhill at the University of Oregon. Integrating the temporal and Lempel-Ziv complexity microstate features with spectral features offers a deeper understanding of the neuro mechanisms underlying brain damage in patients with DOC, holding promise as effective electrophysiological Aug 1, 2018 · The results showed that the performance of the KNN classification was affected by the distance metric used, and the City Block distance metric performs the best among all. May 1, 2022 · Nevertheless, stroke patients showed significant differences in most parameters of microstates A, B, and C compared to healthy controls. of pattern recognition on stroke patients’ EEG, which is a fundamental for implementing BCI-based systems. History. The participants included 39 male and 11 female. Sep 1, 2022 · Nonetheless, high classification performance is still found among a few subjects, indicating that this dataset has the potential for cross-session modeling. Parameters setting and results of EEGNet under two conditions: 1) within-subject classification Using a 20-session dataset of motor imagery BCI usage by 5 stroke patients, we demonstrated that after channel selection, CSP can still maintain a high accuracy with low number of electrodes using a newly proposed channel selection method called CSP-rank (higher than 90% with 8 electrodes). Aug 5, 2023 · Object Quantitative electroencephalography (qEEG) has shown promising results as a predictor of clinical impairment in stroke. Every patient has the right one and left one in according to paretic hand movement or unaffected hand movement. Keywords— Electroencephalography, Relative power, Spec- tral entropy, Wavelet, Mild cognitive impairment. In the stroke emotion analysis, the machine learning is used to analyze the emotion of stroke patients and normal stroke patient rehabilitation like robotic devices and virtual reality systems, researchers have found that the brain-computer interfaces (BCI) approaches can provide better results. e. , both positive and negative) findings for EEG-based prognosis of post-stroke outcome. The patients may be The Nencki-Symfonia EEG/ERP dataset: high-density electroencephalography (EEG) dataset obtained at the Nencki Institute of Experimental Biology from a sample of 42 healthy young adults with three cognitive tasks: (1) an extended Multi-Source Interference Task (MSIT+) with control, Simon, Flanker, and multi-source interference trials; (2) a 3 Feb 21, 2025 · These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. There are five distinct experiments: the initial assessment with a conventional paradigm prompted by text (Pre Is there any publicly-available-dataset related to EEG stroke and normal patients. Patients’ EEG signals in the four frequency bands were collected, connec-tion strength between arbitrary leads of the different rhythms of resting state EEG was obtained using the functional connection mode based on DTF. Share theta, alpha, beta) and propofol requirement to anesthetize a The open-source dataset was provided by CBCI Challenge-2020 organized by University of Essex. This study provides a comprehensive overview of recent developments in BCI and motor control for rehabilitation, emphasizing the integration of user-friendly The mean time poststroke was averaged across a broad range of time poststroke (1–15 mo) in this data set and the time poststroke of 10 of the 19 patients in the favorable group of the training data set was within 3 months . Dec 1, 2016 · The aim of this study was to analyse the electroencephalography (EEG) background activity of 10 stroke-related patients with mild cognitive impairment (MCI) using spectral entropy (SpecEn) and Oct 31, 2021 · The aim of this study was to compare the performance of EEG-related indexes in differentiating stroke patients from control participants, and to investigate pathological EEG changes after chronic Studies have shown that a motor imagery electro encephalogram (EEG)-based brain-computer interface (BCI) system can be used as a rehabilitation tool for stroke patients. mat │ └─data_load Feb 28, 2022 · Background Stroke is a common medical emergency responsible for significant mortality and disability. 57 Through experiments with eight healthy participants and nine patients with stroke, our proposed MI-BCI system showed 71. The k-Nearest Neighbor (KNN) classifier is one of the most widely applied classifier due to its less complexity and The rapidly evolving landscape of artificial intelligence (AI) and machine learning has placed data at the forefront of healthcare innovation. This study develops an explainable multi-task learning approach for EEG-based stroke Mar 14, 2017 · We demonstrate the utility of MRCP phase in two independent datasets, in which 10 healthy subjects and 9 chronic stroke patients executed a self-initiated gait task in three sessions. Methods Following the Preferred Reporting Items for Systematic Jan 25, 2024 · Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. The signals were recorded with 12 electrodes, sampled at 512 Hz and initially filtered with 0. 70% FP rate by using only four EEG channels in the Jul 1, 2017 · Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. This page is dedicated to providing you with extensive information on various EEG datasets, publications, software tools, hardware devices, and APIs. The dataset contains data from a total of 516 trials of healthy individuals and 174 trials of stroke patients. Phase features were compared to more conventional amplitude and power features. Be sure to check the license and/or usage agreements for Sep 22, 2022 · Current clinical practice does not leverage electroencephalography (EEG) measurements in stroke patients, despite its potential to contribute to post-stroke recovery predictions. Apr 11, 2023 · The second leading cause of death and one of the most common causes of disability in the world is stroke. 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. Oct 5, 2021 · This study uses the stroke patients’ EEG dataset that includes two types of MI tasks (including left-hand and right-hand tasks). EEG is a non-invasive way to analyze brain activity changes during stroke, but interpreting complex EEG data remains challenging. 1 EEG Dataset The EEG signals are obtained from public open-source repository for open data (RepOD), BNCI Horizon 2020 and the Temple University Hospital EEG Corpus (TUH-EEG) datasets. EEG. However Nov 5, 2024 · Objective. Aug 1, 2018 · PDF | On Aug 1, 2018, Choong Wen Yean and others published Analysis of The Distance Metrics of KNN Classifier for EEG Signal in Stroke Patients | Find, read and cite all the research you need on The motor imagery experiment contain 50 patients of stroke. In our present study, the wrist extension experiment was designed, along with related EEG datasets being collected. Targeted datasets focusing on stroke patients are Jan 25, 2024 · With this dataset, we initially compared EEG data acquired during left- and right-handed MI in acute stroke patients and performed a binary decoding task using existing baseline data and state-of The dataset must consist of electroencephalography (EEG) data of 50-100 stroke patients. e dataset comprises 15 Dataset description This dataset includes data from 50 acute stroke patients (the time after stroke ranges from 1 day to 30 days) admitted to the stroke unit of Xuanwu Hospital of Capital Medical University. In conclusion, an increasing trend in the release of open-source EEG datasets has been observed with Due to the non-stationary nature of electroencephalography (EEG) signals, a Brain-computer Interfacing (BCI) system requires frequent calibration. The dataset includes trials of 5 healthy subjects and 6 stroke patients. Is there any publicly-available-dataset related to EEG stroke and normal patients. Cross-subject MI modeling can address the need for each modeling session for rehabilitation training of stroke patients and enhance the usability of stroke rehabilitation training. Share theta, alpha, beta) and propofol requirement to anesthetize a of any CNN based architecture on patients’ EEG data for MI classification. Dividing the data of each subject into a training set and a test Jun 1, 2024 · However, recent advances in EEG acquisition hardware, lead technology, and analysis software suggest a larger diagnostic role may be possible for patients with suspected acute stroke. Aug 2, 2021 · EEG meta-data has been released to tackle large EEG datasets like CHB-MIT and Siena Scalp. We aim to assess the severity of spatial neglect through detailing patients’ field of view (FOV) using EEG. 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 Apr 16, 2023 · The EMG sampling rate was 1,000 Hz. Three post-stroke patients treated with the recoveriX system (g. The dataset is not publicly available and must be obtained directly from the authors. 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. The EEG data was gathered with a 16-channel cap, using 10/20 montage setup. of 5 VaD patients, 15 stroke-related patients with mild cognitive impairment (MCI) and 15 control healthy subjects during a 19 channels from the EEG dataset were grouped into post-stroke MCI patients’ and the healthy controls’ EEG. approach and leveraged the EEG datasets of patients at The raw . 8. 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 based on kappa scores. 1 to 100 Hz pass-band filter and a notch filter at 50 Hz. is study uses the stroke patients’ EEG dataset that includes two types of MI tasks (including le-hand and right Apr 11, 2023 · The second leading cause of death and one of the most common causes of disability in the world is stroke. 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 In this study, EEG signal processing was carried out in post-stroke patients to characterize patients with cognitive impairment. This database has limitations, including the lack of information about the phase and severity of TBI and stroke. The dataset collected EEG EMG data from 5 healthy volunteers and 2 stroke patients performing isometric push and pull movements of 3 s duration. We review the literature on the effectiveness of various quantitative and qualitative EEG-based measures after stroke as a tool to predict upper limb motor outcome, in relation to stroke timeframe and applied OpenNeuro is a free and open platform for sharing neuroimaging data. 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 This is a list of openly available electrophysiological data, including EEG, MEG, ECoG/iEEG, and LFP data. Browse Mar 3, 2022 · patients’ EEG has been used to predict functional outcomes [16]. It consists of EEG brain imaging data for 10 hemiparetic stroke patients having hand functional disability. Jan 13, 2023 · 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 Using a 20-session dataset of motor imagery BCI usage by 5 stroke patients, we demonstrated that after channel selection, CSP can still maintain a high accuracy with low number of electrodes using a newly proposed channel selection method called CSP-rank (higher than 90% with 8 electrodes). Given the abundance of large-scale and accessible datasets from healthy subjects, we aimed to investigate whether a model trained on healthy individuals' brain data could help overcome the shortage of stroke patients' data and improve the classification of their imagery movements. With high temporal-resolution electroencephalogram (EEG), the time-varying network is able to reflect the dynamical complex network modalities corresponding to the movements at a millisecond level. Nov 30, 2024 · An EEG motor imagery dataset for brain computer interface in acute stroke patients | Scientific Data (nature. Motor imagery-based BCI-FES rehabilitation system has been proved to be effective in the treatment of movement function recovery. With subjects often producing more than one recording per session, the final dataset consisted of 2401 EEG recordings (63% healthy, 37% stroke). The EEG of the patients whose limbs and face are affected by stroke must be recorded. In this work, we present an EEG-based imaging algorithm to estimate the location and size of the stroke infarct core and penumbra tissues. Methods Following the Preferred Reporting Items for Systematic Mar 22, 2024 · In general, datasets from a hospital, such as EEG signals, are imbalanced. 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）数据组成。 Dec 1, 2024 · Stroke is a major cause of long-term disability. Feb 22, 2025 · In this dataset, we collected EEG data from 27 stroke recovery patients, with disease durations ranging from 1 to 12 months. BCI technology that registers the electroencephalographic (EEG) signal accompa - this is the ˙rst open access dataset containing NIRS recordings from stroke patients. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI Jul 6, 2023 · Although the potential of EEG-based efforts for TBI and stroke detection have been demonstrated in some studies, clinical applicability is still in debate [18–21]. Conclusions. Recently, Nicolo et al dem-onstrated that the EEG β coherence between the ipsilesional M1 and all other brain regions was linearly correlated with more favorable motor improvement in patients with subacute stroke [17]. One of the most successful algorithms for EEG classification is the common spatial patterns (CSP). py │ ├─dataset │ │ subject. tec medical engineering GmbH) were enrolled in this study, participants had a mean age of 22 years (SD = 4. The dataset must consist of electroencephalography (EEG) data of 50-100 stroke patients. The patients included 39 males (78%) and 11 females (22%), aged between 31 and 77 years, with an average age of 56. We An adaptive CSP method is proposed to deal with unknown irregular patterns in motor imagery signals of stroke patients and is applied on the EEG datasets of several stroke subjects comparing with traditional CSP-SVM. Feb 8, 2024 · ports, we were able to construct a dataset of 1385 healthy subjects and 374 stroke patients. The feature extraction method can describe brain activity changes so that EEG signals can be estimated that describe normal conditions, mild cognitive disorders, and dementia. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI Oct 1, 2021 · The EEG dataset from the post-stroke patients with upper extremity hemiparesis was investigated. Surface electroencephalography (EEG) shows promise for stroke identification and This dataset includes data from 50 acute stroke patients (the time after stroke ranges from 1 day to 30 days) admitted to the stroke unit of Xuanwu Hospital of Capital Medical University. Licence. Our federated learning system integrates MQTT as an efficient communication protocol, demonstrating its security in dispatching model updates and aggregation across distributed clients. Whether you're a researcher, student, or just curious about EEG, our curated selection offers valuable insights and data for exploring the complex and fascinating field of brainwave analysis. GPL 3. Dividing the data of each subject into a training set and a test This is a list of openly available electrophysiological data, including EEG, MEG, ECoG/iEEG, and LFP data. Plot functional connectivity matrix and corresponding topology in 3 frequency bands for 50 stroke patients. The patients may be Jan 1, 2024 · Epileptiform electroencephalogram (EEG) patterns are commonly observed in stroke patients and can significantly impact clinical management and patient outcomes. Article Google Scholar Agius Anastasi A, Falzon O, Camilleri K, Vella M, Muscat R (2017) Brain symmetry index in healthy and stroke patients for assessment and prognosis. 22 participants had right hemisphere hemiplegia and 28 participants had left hemisphere hemiplegia. Early and accurate diagnosis of stroke severity can improve patient outcomes. This study provides a comprehensive overview of recent developments in BCI and motor control for rehabilitation, emphasizing the integration of user-friendly Motor imagery (MI)-based brain-computer interfaces (BCI) have shown increased potential for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice has been Apr 11, 2023 · This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. We obtained an EEG dataset of 3 chronic stroke patients, who performed a motor imagery task of either imagining moving their left or right hand when presented with a cue. Mar 22, 2024 · In general, datasets from a hospital, such as EEG signals, are imbalanced. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variati … Nov 20, 2024 · This dataset is from an EEG brain-computer interface (BCI) study investigating the use of deep learning (DL) for online continuous pursuit (CP) BCI. Spatial neglect, a prevalent neurological syndrome in stroke patients Jan 13, 2023 · 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 Jun 1, 2024 · Using a large-scale, retrospective database of EEG recordings and matching clinical reports, we were able to construct a dataset of 1385 healthy subjects and 374 stroke patients. A quantitative method of analyzing EEG signals after stroke onset can help monitor disease progression and tailor treatments. Classification accuracy of the five Late Stroke datasets ranged from 62. of any CNN based architecture on patients’ EEG data for MI classification. kpxuq pxa mgem dyqu feafm vzia mxlbxw oueehav tbz jltsjt okrir damhv dkp hot pfjvda