Eeg datasets of stroke patients. No patient was treated with endovascular therapy.

Eeg datasets of stroke patients 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. This database has limitations, including the lack of information about the phase and severity of TBI and stroke. The critical component in BMI-training consists of the associative connection (contingency) between the intention and the feedback provided. 6: Predict activities of daily living (Barthel index as the indicator) is crucial for post-stroke patients. In conclusion, an increasing trend in the release of open-source EEG datasets has been observed with 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. 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. An initial analysis using CSP-SVM on the dataset yielded an average classification accuracy of 80. 2. The mean interval between the stroke onset and the first EEG Jan 25, 2024 · Therefore, expanding the EEG datasets for BCI to restore upper limb function in stroke patients is crucial. Classification. Share theta, alpha, beta) and propofol requirement to anesthetize a Welcome to the resting state EEG dataset collected at the University of San Diego and curated by Alex Rockhill at the University of Oregon. The dataset was collected using a clinical EEG system with 19 Oct 7, 2004 · Background and Purpose— There is increased awareness that continuous brain monitoring might benefit neurological patients, because it may allow detection of derangement of brain function in a possible reversible state, allowing early intervention. Stroke 35(11):2489–2492. However, the relationship between the BMI design and its performance in stroke patients is still an open question The RST is currently developed based on publicly available patient data in the TUEG. The raw . 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. This paper analyzes the correlation of two EEG parameters, Brain Symmetry Index (BSI) and Laterality Coefficient (LC), with established functional scales for the stroke assessment. is study uses the stroke patients’ EEG dataset that includes two types of MI tasks (including le-hand and right Feb 8, 2024 · ports, we were able to construct a dataset of 1385 healthy subjects and 374 stroke patients. Categories. In conclusion, an increasing trend in the release of open-source EEG datasets has been observed with Mar 29, 2023 · A total of 44 healthy elderly and MCI and AD patients participated in this experiment. No patient was treated with endovascular therapy. openresty Mar 27, 2023 · The EMG sampling rate was 1,000 Hz. This is a list of openly available electrophysiological data, including EEG, MEG, ECoG/iEEG, and LFP data. 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. Licence. The dataset is not publicly available and must be obtained directly from the authors. 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. Conclusions: In general, datasets from a hospital, such as EEG signals, are imbalanced. Four patients received IV tPA, three prior (median 61 minutes) to EEG and one after (28 minutes) EEG. , 2011; Larivière et al. There is increasing evidence that the brain tries to reorganize itself and to replace the damaged circuits, by establishing compensatory pathways. These markers are useful for the determination of stroke severity and prediction of functional outcome. All subjects involved in this study were asked to fill out an informed consent form. The dataset collected EEG EMG data from 5 healthy volunteers and 2 stroke patients performing isometric push and pull movements of 3 s duration. Jul 6, 2020 · Here, we explore two different qEEG parameters and their relationship with the diagnosis and functional prognosis of stroke patients. One- and two-minute recordings of 109 volunteers performing a series of motor/imagery tasks. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. 57) (shown in Table 1 ). In our present study, the wrist extension experiment was designed, along with related EEG datasets being collected. Methods ˜e EEG dataset is stored in 3D format (M, C, T), where M is the number of trials. Dec 15, 2022 · We used the EOG and chin EMG to eliminate eye blink and muscle artifacts. 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. py │ ├─dataset │ │ subject. The distribution of patients among the hospitals is shown in Fig. Overall, our study reveals the underlying mechanism in oscillation frequencies and regions of stroke patient EC and EO states by a deep learning model. Design Type(s) parallel of any CNN based architecture on patients’ EEG data for MI classification. We would like to show you a description here but the site won’t allow us. , 2015). 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. Results: Using a rich set of features encompassing both the spectral and temporal domains, our model yielded an 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. Nov 30, 2024 · An EEG motor imagery dataset for brain computer interface in acute stroke patients | Scientific Data (nature. For EEG signals from stroke patients, the datasets consist of much more wakeful samples than DoC ones. assess the value of longitudinal EEG studies in patients in a rehabilitation program. Table 1. [48] Feb 26, 2024 · Welcome to awesome-emg-data, a curated list of Electromyography (EMG) datasets and scholarly publications designed for researchers, practitioners, and enthusiasts in the field of biomedical engineering, neurology, kinesiology, and related disciplines. May 20, 2022 · This study aims to assess the feasibility of using an ambulatory EEG system to classify the stroke patient group with neurological changes due to ischemic stroke and the control healthy adult group. mat │ │ │ ├─sub-02 │ │ sub-02_task-motor-imagery_eeg. Patients are likely to suffer various degrees of functional impairment after the onset of stroke, among which motor dysfunction is one of the most significant disabling manifestations after stroke (Krueger et al. Subjects completed specific MI tasks according to on-screen prompts while their EEG data Apr 11, 2023 · The second leading cause of death and one of the most common causes of disability in the world is stroke. The signals were recorded with 12 electrodes, sampled at 512 Hz and initially filtered with 0. This project contains EEG data from 11 healthy participants Tab. Clinically-meaningful benchmark dataset. OpenNeuro is a free and open platform for sharing neuroimaging data. The number of papers published examining prognostic utility of EEG for post-stroke outcome over the years (A) and mean EEG times (B). EEG variables selected using Lasso regression Jun 22, 2021 · The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. Stroke is a cerebrovascular disease with high morbidity, disability, and mortality (Sheorajpanday et al. MethodsThirty-two healthy subjects and thirty-six stroke patients with upper extremity In this paper, we first introduce the clinical application of BCI systems for post-stroke patients, then we summarize the research status of the relationship between image generation and EEG signals. 1 to 100 Hz pass-band filter and a notch filter at 50 Hz. 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. Feb 29, 2024 · The neurophysiological pattern of cortical rhythms can be changed by an acute stroke []. 1 illustrates the dataset, which contains 5110 rows, each row representing a patient, and 12 columns divided into 10 features, an identification column (ID) and a target feature column that is either stroke (1) or no stroke (0). 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. These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. Be sure to check the license and/or usage agreements for 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. GPL 3. 22 participants had right hemisphere hemiplegia and 28 participants had left hemisphere hemiplegia. The dataset consists of Oct 25, 2024 · This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. 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. This paper is organized as follows. on stroke, updating previous revisions [12] with a specic focus on dierent qEEG measures as biomarkers of clinical outcome. Datasets and resources listed here should all be openly-accessible for research purposes, requiring, at most, registration for access. 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. [Mayo Clinic] The goal of this project is to classify brain states from EEG data. Notably, the initial three sessions encompass training data, while the subsequent two sessions consist of test data. 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. There were 39 men and 4 women. In these datasets, the EEG signal is recorded for 10 min from each patient using the standard 10–20 EEG electrode placement system (Fig. mat │ │ │ │ │ │ │ └─sub-50 │ sub-50_task-motor-imagery_eeg. 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. There were many ways to access data 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. Stroke patients performed functional assessment sessions, and BCI rehabilitation therapy for the upper extremity. The EEG dataset is stored in 3D format (M, C, T), where M is the number of trials. (EEG) and Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) data in stroke patients, which can form the basis of future research into stroke classification. EEG is a cheap noninvasive technique that 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. However, since stroke patients in our dataset have unilateral affected limbs, care should be taken while using trials of a training subject whose affected limb is not the same as the target affected Compared to normal control, both TBI and stroke patients showed an overall reduction in coherence and relative PSD in delta frequency, and an increase in higher frequency (alpha, mu, beta and gamma) power. With subjects often producing more than one recording per session, the final dataset consisted of 2401 EEG recordings (63% healthy, 37% 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. This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. These social changes require new smart healthcare services for use in daily life, and COVID-19 has also led to a contactless trend necessitating more non-face-to-face health services. 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. The participants included 23 males and 4 females, aged between 33 and 68 years. 97±8. Each record contains 64 channels of EEG recorded using the BCI2000 system, and a set of task annotations. csv │ │ │ └─sourcedata │ ├─sub-01 │ │ sub-01_task-motor-imagery_eeg. A standardized data collection This page is dedicated to providing you with extensive information on various EEG datasets, publications, software tools, hardware devices, and APIs. Usage metrics. Therefore, whenever available, the tool needs to be further validated with data from more homogeneous populations of patients. Domain adaptation and deep learning-based Apr 17, 2023 · The EMG sampling rate was 1,000 Hz. In total the dataset is ~150GB, and is thus split into parts based on the Zenodo 50 GB file limit. The patients included 39 males (78%) and 11 females (22%), aged between 31 and 77 years, with an average age of 56. Qureshi et al used 6 channel EEG data recorded for 15 min to 4 hrs. The remaining 35 participants (age = 70. There are five distinct experiments: the initial assessment with a conventional paradigm prompted by text (Pre Feb 20, 2018 · 303 See Other. Clinical data from each group are presented in Table 1. , when and to what extent they should expect to improve. This activity shows up as wavy lines on an EEG recording. 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. Sep 23, 2022 · IntroductionRecent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. EEG data motor imagery task stroke patient data. In this paper, we propose a cloud computing-based machine learning (ML) system that leverages MUSE2 to diagnose stroke patients by analysing EEG signals. The dataset included 48 stroke survivors and 75 healthy people. In recent years, machine learning based methods, especially deep neural networks, have improved the pattern recognition and classification Oct 12, 2021 · Van Putten MJ, Tavy DL (2004) Continuous quantitative EEG monitoring in hemispheric stroke patients using the brain symmetry index. Table 1 -. Mar 1, 2024 · Numerous studies have employed EEG to predict stroke outcomes in medical and healthcare settings. It consists of EEG brain imaging data for 10 hemiparetic stroke patients having hand functional disability. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. 582). e. An automatic portable biomarker can potentially facilitate patients triage and ensure timely The open-source dataset was provided by CBCI Challenge-2020 organized by University of Essex. Jul 6, 2023 · Author summary Traumatic Brain Injury (TBI) and stroke are devastating neurological conditions that affect hundreds of people daily. Jun 26, 2022 · Introduction. It is crucial to highlight that the dataset exclusively features EEG data from three specific channels: C3, Cz, and C4. Dividing the data of each subject into a training set and a test set. 57 Motor Imagery dataset from the Clinical BCI Challenge WCCI-2020. 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. 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. Stroke Prediction Module. , both positive and negative) findings for EEG-based prognosis of post-stroke outcome. In order to establish the dataset for DNNs, at last, we propose a clinical study conceptual to collect post-stroke patients’ training sample. 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. Keywords. Intra- and extra-cellular currents are involved in the communication between neurons and the macroscopic effects of such currents can be detected at the scalp through Sep 28, 2022 · We analyzed the EEG datasets recorded from 136 stroke patients during the BCI screening sessions of four clinical trials 29,41,42,43. The mean age was 63. But stroke patients showed a greater degree of change and had additional global decrease in theta power. One group of healthy participants and one group of stroke patients participated in the study. The histograms shows the number of papers for each time period that reported (i) only positive, (ii) only negative, and (iii) mixed (i. This study uses the stroke patients’ EEG dataset that includes two types of MI tasks (including left-hand and right-hand tasks). The EEG data was gathered with a 16-channel cap, using 10/20 montage setup. Nov 20, 2018 · Background Brain machine interface (BMI) technology has demonstrated its efficacy for rehabilitation of paralyzed chronic stroke patients. Stroke. A joint CU Anschutz/ULN project has collected EEG data on subjects during sessions in which the subjects were instructed to visualize performing a motor-based task. Every patients perform motor imagery instructed by a video. Previous research examined the classification accuracy for some subjects within this dataset 36 , demonstrating the Recently, there has been a growing research interest in utilizing the electroencephalogram (EEG) as a non-invasive diagnostic tool for neurodegenerative diseases. May 1, 2024 · The study focuses on developing EEG markers for patients with ischemic or hemorrhagic stroke. 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 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 Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Methods: We performed a cross-sectional analysis of a cohort study (DEFINE cohort), Stroke arm, with 85 patients, considering demographic, clinical, and stroke characteristics. , severity of speech production impairments) with the purpose of answering stroke patients’ expectations, i. bdf files are available should you wish to recreate or alter the processing of this dataset. The EEG datasets of patients about motor imagery. stroke patients with wireless portable saline EEG devices during the performance of two tasks: ) imagining right-handed movements and ) imagining left-handed movements. In future, we proposed to apply this model in different EEG-based stroke patient prediction scenarios. BCIs are typically used by subjects with no damage to the brain therefore relatively little is known about the technical requirements for the design of a rehabilitative BCI for stroke. The dataset contains data from a total of 516 trials of healthy individuals and 174 trials of stroke patients. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variati … Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. │ figshare_fc_mst2. Here, we explore if quantitative continuous electroencephalography (cEEG) monitoring is technically feasible and possibly clinically relevant in The median time from EEG to neuroimaging among patients with stroke (the first images that showed the index infarct, and so were used to measure infarct volume) was 3. Jun 20, 2024 · This dataset comprises data collected across a total of five sessions, involving nine subjects. [Class 2] EEG Signals from an RSVP Task. Continuous EEG: few seconds of 64-channel EEG recording from an alcoholic patient. Please email arockhil@uoregon. Seven stroke patients had a mild stroke (NIHSS: 1–4), ten had a moderate stroke (NIHSS: 5–15), 13 had a moderate-to-severe stroke (NIHSS: 16–20), and eighteen had a severe stroke (NIHSS: 21–42). This has led to the necessity of exploring new methods for stroke detection, particularly utilizing EEG signals. Feb 21, 2019 · [Class 2] EEG Motor Movement/Imagery Dataset. The dataset includes trials of 5 healthy subjects and 6 stroke patients. 8 hours. Is there any publicly-available-dataset related to EEG stroke and normal patients. 70 years (SD = 10. In addition, because of the significant between-participant variability in neuroplasticity in response to rehabilitation Jan 1, 2025 · This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. The stroke prediction module for the elderly using deep learning-based real-time EEG data proposed in this paper consists of two units, as illustrated in Figure 4. com) （3）下载链接： EEG datasets of stroke patients (figshare. Stroke is a critical event that causes the disruption of neural connections. Among the patients, 18 had right hemiplegia, and 9 had left hemiplegia. openresty 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 the rehabilitation of arm impairment after stroke, quantifying the training dose (number of repetitions) requires differentiating motions with sub-second durations. 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. Some datasets used in Brain Computer Interface competitions are also available at Raw EEG signal samples: (a) Raw EEG signals from elderly stroke patients; (b) Raw EEG signal samples from control group. tec medical engineering GmbH) were enrolled in this study, participants had a mean age of 22 years (SD = 4. 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 Jan 30, 2014 · Motor imagery EEG patterns of stroke patients are detected in spatial–spectral–temporal domain from limited training datasets. Machine learning algorithms, such as support vector machines (SVMs), random forests, and neural networks, have been used to classify EEG data from stroke patients and predict stroke occurrence or outcome [63]. 1). While there has not been much research Jun 14, 2017 · 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 . The dataset includes raw EEG signals, preprocessed data, and patient information. 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. 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. Dataset. 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. 58, female = 57. 14% 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. This article provides a detailed description of a resting-state EEG dataset of individuals with Alzheimer’s disease and frontotemporal dementia, and healthy controls. 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 This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. 0 Jan 30, 2014 · Motor imagery EEG patterns of stroke patients are detected in spatial–spectral–temporal domain from limited training datasets. Low-voltage background activity, absence of reactivity, and epileptiform discharges are correlated with worse functional outcomes [ 10 , 12 , 14 Jun 7, 2024 · However, this deep learning model only test on stroke patient’s EEG states classification. 74 years (SD, 9. 8 years). py │ figshare_stroke_fc2. 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 This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. 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]. We instructed participants to avoid swallowing and eye blinking during the trial period and to avoid any other movement. Methods Subjects Forty-three patients with ischemic stroke in the middle cerebral artery were enrolled. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. 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. Dividing the data of each subject into a training set and a test 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 of pattern recognition on stroke patients’ EEG, which is a fundamental for implementing BCI-based systems. StrokeRehab dataset helps to build deep learning models that can different motions with sub-second durations. The data of 6 participants were removed from further processing due to issues with EEG data recording, history of stroke, or traumatic brain injuries. 32-channel electroencephalogram (EEG) was recorded during a finger-tapping task Dec 7, 2024 · This study utilizes a comprehensive dataset comprising EEG recordings from 72 patients collected during hospitalization across four medical centers. 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 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 Mar 22, 2024 · In general, datasets from a hospital, such as EEG signals, are imbalanced. Given that the dataset is unbalanced, with 4861 normal patients and 249 stroke patients, we will process it 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 Jan 1, 2017 · EEG is commonly used to diagnose vascular epilepsy secondary to stroke in adults; it lets physicians study the characteristics and clinical outcomes of patients, as well as analyse the effectiveness of different antiepileptic treatments. The dataset includes raw EEG signals, preprocessed Jan 17, 2024 · To train the 'S-to-S' model for each test/target patient, the training data includes all trials from the remaining patients in the stroke dataset. 33 Furthermore, EEG is typically used as a monitoring method during carotid endarterectomy to detect 43 Ischaemic Stroke patients, 7 Haemorrhagic Stroke patients, 13 TIA patients, 37 Stroke mimics: Not Reported < 23: 17 electrodes, portable, dry electrode system, eyes open, resting: Offline analysis: filtering, noise removal and re-referencing. 17%31), demonstrating that the collected EEG data can be classi˛ed based on the execution of MI tasks. mat │ └─data_load Aug 5, 2023 · Object Quantitative electroencephalography (qEEG) has shown promising results as a predictor of clinical impairment in stroke. We are provided an EEG Dataset of 10 hemiparetic stroke patients having hand functional disability. 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. 3. Mar 7, 2024 · The most visible functional hallmark among AD patients is the so-called “slowing of EEG,” which corresponds to a shift in the brain waves’ power spectrum to slower frequencies 8. OpenNeuro is a free platform for sharing neuroimaging data, supported by collaborations with renowned institutions. g. Parameters setting and results of EEGNet under two conditions: 1) within-subject classification The motor imagery experiment contain 50 patients of stroke. EEG. 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 Aug 22, 2023 · 303 See Other. 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 []. Some previous literatures talked about detecting stroke using EEG signals. Abnormal EEG in general and generalized slowing in particular are associated with clinical deterioration after acute ischemic stroke. 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 28, 2014 · Brain-Computer Interfaces (BCI) can potentially be used to aid in the recovery of lost motor control in a limb following stroke. Jan 1, 2018 · The results presented in this study demonstrate, on the one hand, that rejecting trials with artifacts from the EEG datasets helps to better quantify the brain activity of stroke patients during motor tasks; and on the other hand, that after rejecting the artifacts from the training datasets, the obtained BMI performances are lower. Due to the improvements that have been achieved in healthcare technologies, an The document summarizes publicly available MI-EEG datasets released between 2002 and 2020, sorted from newest to oldest. The time after stroke ranged from 1 days to 30 days. Jan 1, 2024 · Epileptiform electroencephalogram (EEG) patterns are commonly observed in stroke patients and can significantly impact clinical management and patient outcomes. Dataset Link Aug 2, 2021 · EEG meta-data has been released to tackle large EEG datasets like CHB-MIT and Siena Scalp. Sleep data: Sleep EEG from 8 subjects (EDF format). 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。 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. 32 ± Mar 9, 2024 · Objective: Investigate the relationship between resting-state EEG-measured brain oscillations and clinical and demographic measures in Stroke patients. History. 50%. There are five distinct experiments: the initial assessment with a conventional paradigm prompted by text (Pre Mar 27, 2022 · This dataset is the most comprehensive of its kind and enables combined analysis of MFEIT, Electroencephalography (EEG) and Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) data in Jun 1, 2024 · Apart from BCI application and studying stroke rehabilitation, EEG can also be used to classify different types of stroke (ischemic/hemorrhagic). This EEG . Every patient has the right one and left one in according to paretic hand movement or unaffected hand movement. Resting state EEG: resting-state EEG and EOG with both eyes-open and eyes-closed conditions recorded from 10 participants. 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 Oct 1, 2021 · The EEG dataset from the post-stroke patients with upper extremity hemiparesis was investigated. 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. 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 5, 2021 · This study uses the stroke patients’ EEG dataset that includes two types of MI tasks (including left-hand and right-hand tasks). Unfortunately, detecting TBI and stroke without specific imaging techniques or access to a hospital often proves difficult and may lead to long-term health problems. The EEG data were analyzed across various frequency bands to construct brain connectivity graphs. 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 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 Feb 28, 2022 · Background Stroke is a common medical emergency responsible for significant mortality and disability. Oct 1, 2018 · ischemic stroke patients datasets are used to detect ischemic signals by deep learning is proposed to help predict the coma etiology of ICU patients. The study demonstrates the value of routine EEG as a simple diagnostic tool in the evaluation of stroke patients especially with regard to short-term prognosis. Among the 136 participants, 17 were in subacute phase (3. Surface electroencephalography (EEG) shows promise for stroke identification and Jan 1, 2023 · Automated labelling of open-source datasets is a promising approach to increase the number and size of publicly available, labelled datasets. 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. Feb 22, 2025 · In this dataset, we collected EEG data from 27 stroke recovery patients, with disease durations ranging from 1 to 12 months. 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 Oct 1, 2020 · Realistic long-term behavioral outcome predictions after stroke (e. 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. Three post-stroke patients treated with the recoveriX system (g. This study provides a comprehensive overview of recent developments in BCI and motor control for rehabilitation, emphasizing the integration of user-friendly 2. Fifteen stroke patients completed a total of 237 motor imagery brain–computer interface (BCI in stroke patients (LDA: 79. We expect that our dataset will help address the challenges in Due to the non-stationary nature of electroencephalography (EEG) signals, a Brain-computer Interfacing (BCI) system requires frequent calibration. Nov 15, 2024 · The dataset collected EEG data for four types of MI from 22 stroke patients. The EEG of the patients whose limbs and face are affected by stroke must be recorded. In Section II, we describe the dataset and modified EEGNet architecture implemented on this patient dataset. Methods Following the Preferred Reporting Items for Systematic procedures can be lengthy, often making it impractical for most stroke patients. Also, participants with any history of olfactory dysfunction were excluded from the study. The total number of participants was 50 subjects, consisting of 18 subjects with normal categories, 19 post-ischemic stroke patients with MCI, and 13 post-ischemic stroke patients with dementia. 11 clinical features for predicting stroke events. , 2018). The participants included 39 male and 11 female. euaxw mbta budr helgvds bidsu ijdzj phvcxv epyuk svyqr kcve zgqpk azkbna qmrooej fyvh nuft