Eeg datasets of stroke patients 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. 1). 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. mat │ │ │ ├─sub-02 │ │ sub-02_task-motor-imagery_eeg. │ figshare_fc_mst2. The data of 6 participants were removed from further processing due to issues with EEG data recording, history of stroke, or traumatic brain injuries. Keywords. Jul 6, 2023 · Author summary Traumatic Brain Injury (TBI) and stroke are devastating neurological conditions that affect hundreds of people daily. The dataset included 48 stroke survivors and 75 healthy people. Resting state EEG: resting-state EEG and EOG with both eyes-open and eyes-closed conditions recorded from 10 participants. 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. 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. 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. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. openresty Mar 27, 2023 · The EMG sampling rate was 1,000 Hz. 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. There are five distinct experiments: the initial assessment with a conventional paradigm prompted by text (Pre Feb 20, 2018 · 303 See Other. May 1, 2024 · The study focuses on developing EEG markers for patients with ischemic or hemorrhagic stroke. History. Dec 15, 2022 · We used the EOG and chin EMG to eliminate eye blink and muscle artifacts. 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. This study provides a comprehensive overview of recent developments in BCI and motor control for rehabilitation, emphasizing the integration of user-friendly 2. py │ ├─dataset │ │ subject. 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. stroke patients with wireless portable saline EEG devices during the performance of two tasks: ) imagining right-handed movements and ) imagining left-handed movements. 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. 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. There were 39 men and 4 women. 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. Feb 29, 2024 · The neurophysiological pattern of cortical rhythms can be changed by an acute 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 Jan 30, 2014 · Motor imagery EEG patterns of stroke patients are detected in spatial–spectral–temporal domain from limited training datasets. The dataset consists of Oct 25, 2024 · This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. OpenNeuro is a free platform for sharing neuroimaging data, supported by collaborations with renowned institutions. 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. Also, participants with any history of olfactory dysfunction were excluded from the study. For EEG signals from stroke patients, the datasets consist of much more wakeful samples than DoC ones. 97±8. 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. 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. 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. [Mayo Clinic] The goal of this project is to classify brain states from EEG data. 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. Nov 30, 2024 · An EEG motor imagery dataset for brain computer interface in acute stroke patients | Scientific Data (nature. The dataset includes raw EEG signals, preprocessed data, and patient information. The EEG data were analyzed across various frequency bands to construct brain connectivity graphs. 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. 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. 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 []. 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. Clinical data from each group are presented in Table 1. 2. In our present study, the wrist extension experiment was designed, along with related EEG datasets being collected. Overall, our study reveals the underlying mechanism in oscillation frequencies and regions of stroke patient EC and EO states by a deep learning model. 17%31), demonstrating that the collected EEG data can be classi˛ed based on the execution of MI tasks. 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. 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. Parameters setting and results of EEGNet under two conditions: 1) within-subject classification The motor imagery experiment contain 50 patients of stroke. This is a list of openly available electrophysiological data, including EEG, MEG, ECoG/iEEG, and LFP data. The dataset includes trials of 5 healthy subjects and 6 stroke patients. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. 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]. Methods Subjects Forty-three patients with ischemic stroke in the middle cerebral artery were enrolled. , 2011; Larivière et al. 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. 22 participants had right hemisphere hemiplegia and 28 participants had left hemisphere hemiplegia. We are provided an EEG Dataset of 10 hemiparetic stroke patients having hand functional disability. csv │ │ │ └─sourcedata │ ├─sub-01 │ │ sub-01_task-motor-imagery_eeg. Every patient has the right one and left one in according to paretic hand movement or unaffected hand movement. 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. The EEG datasets of patients about motor imagery. GPL 3. Methods: We performed a cross-sectional analysis of a cohort study (DEFINE cohort), Stroke arm, with 85 patients, considering demographic, clinical, and stroke characteristics. This database has limitations, including the lack of information about the phase and severity of TBI and stroke. 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. Licence. Nov 20, 2018 · Background Brain machine interface (BMI) technology has demonstrated its efficacy for rehabilitation of paralyzed chronic stroke patients. 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. 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). 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 future, we proposed to apply this model in different EEG-based stroke patient prediction scenarios. Therefore, whenever available, the tool needs to be further validated with data from more homogeneous populations of patients. In the rehabilitation of arm impairment after stroke, quantifying the training dose (number of repetitions) requires differentiating motions with sub-second durations. 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. , 2015). 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. bdf files are available should you wish to recreate or alter the processing of this dataset. In this paper, we propose a cloud computing-based machine learning (ML) system that leverages MUSE2 to diagnose stroke patients by analysing EEG signals. e. Mar 1, 2024 · Numerous studies have employed EEG to predict stroke outcomes in medical and healthcare settings. 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. The EEG data was gathered with a 16-channel cap, using 10/20 montage setup. 0 Jan 30, 2014 · Motor imagery EEG patterns of stroke patients are detected in spatial–spectral–temporal domain from limited training datasets. The EEG of the patients whose limbs and face are affected by stroke must be recorded. Classification. 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. Stroke is a critical event that causes the disruption of neural connections. 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. 32 ± Mar 9, 2024 · Objective: Investigate the relationship between resting-state EEG-measured brain oscillations and clinical and demographic measures in Stroke patients. Jun 26, 2022 · Introduction. g. 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. Dividing the data of each subject into a training set and a test set. 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. 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. Among the patients, 18 had right hemiplegia, and 9 had left hemiplegia. 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. This article provides a detailed description of a resting-state EEG dataset of individuals with Alzheimer’s disease and frontotemporal dementia, and healthy controls. 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. 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. Jan 1, 2024 · Epileptiform electroencephalogram (EEG) patterns are commonly observed in stroke patients and can significantly impact clinical management and patient outcomes. The signals were recorded with 12 electrodes, sampled at 512 Hz and initially filtered with 0. This study uses the stroke patients’ EEG dataset that includes two types of MI tasks (including left-hand and right-hand tasks). com) （3）下载链接： EEG datasets of stroke patients (figshare. 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. 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. This has led to the necessity of exploring new methods for stroke detection, particularly utilizing EEG signals. 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. Abnormal EEG in general and generalized slowing in particular are associated with clinical deterioration after acute ischemic stroke. Each record contains 64 channels of EEG recorded using the BCI2000 system, and a set of task annotations. These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. Stroke. 582). 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. Table 1 -. EEG data motor imagery task stroke patient data. With subjects often producing more than one recording per session, the final dataset consisted of 2401 EEG recordings (63% healthy, 37% stroke). , 2018). Dataset. Every patients perform motor imagery instructed by a video. 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). Usage metrics. , when and to what extent they should expect to improve. Conclusions: In general, datasets from a hospital, such as EEG signals, are imbalanced. 1 to 100 Hz pass-band filter and a notch filter at 50 Hz. Stroke 35(11):2489–2492. 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. This activity shows up as wavy lines on an EEG recording. Is there any publicly-available-dataset related to EEG stroke and normal patients. 8 hours. Please email arockhil@uoregon. 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 paper analyzes the correlation of two EEG parameters, Brain Symmetry Index (BSI) and Laterality Coefficient (LC), with established functional scales for the stroke assessment. The mean age was 63. Methods Following the Preferred Reporting Items for Systematic procedures can be lengthy, often making it impractical for most stroke patients. on stroke, updating previous revisions [12] with a specic focus on dierent qEEG measures as biomarkers of clinical outcome. 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. This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. (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. 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. The histograms shows the number of papers for each time period that reported (i) only positive, (ii) only negative, and (iii) mixed (i. 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. 58, female = 57. This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. 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. Three post-stroke patients treated with the recoveriX system (g. 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. 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. Clinically-meaningful benchmark dataset. Design Type(s) parallel of any CNN based architecture on patients’ EEG data for MI classification. Categories. mat │ └─data_load Aug 5, 2023 · Object Quantitative electroencephalography (qEEG) has shown promising results as a predictor of clinical impairment in stroke. 57) (shown in Table 1 ). 3. We instructed participants to avoid swallowing and eye blinking during the trial period and to avoid any other movement. 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. 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. 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. 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. assess the value of longitudinal EEG studies in patients in a rehabilitation program. 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. The remaining 35 participants (age = 70. 8 years). 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. Feb 22, 2025 · In this dataset, we collected EEG data from 27 stroke recovery patients, with disease durations ranging from 1 to 12 months. The patients included 39 males (78%) and 11 females (22%), aged between 31 and 77 years, with an average age of 56. 74 years (SD, 9. [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. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. In order to establish the dataset for DNNs, at last, we propose a clinical study conceptual to collect post-stroke patients’ training sample. Jan 25, 2024 · Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. In these datasets, the EEG signal is recorded for 10 min from each patient using the standard 10–20 EEG electrode placement system (Fig. In Section II, we describe the dataset and modified EEGNet architecture implemented on this patient dataset. Table 1. 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. Sep 23, 2022 · IntroductionRecent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. One- and two-minute recordings of 109 volunteers performing a series of motor/imagery tasks. 50%. 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. Domain adaptation and deep learning-based Apr 17, 2023 · The EMG sampling rate was 1,000 Hz. An initial analysis using CSP-SVM on the dataset yielded an average classification accuracy of 80. , severity of speech production impairments) with the purpose of answering stroke patients’ expectations, i. Feb 21, 2019 · [Class 2] EEG Motor Movement/Imagery Dataset. tec medical engineering GmbH) were enrolled in this study, participants had a mean age of 22 years (SD = 4. 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. 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 participants included 39 male and 11 female. 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. 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. 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. 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). Fifteen stroke patients completed a total of 237 motor imagery brain–computer interface (BCI in stroke patients (LDA: 79. , both positive and negative) findings for EEG-based prognosis of post-stroke outcome. The dataset is not publicly available and must be obtained directly from the authors. 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 number of papers published examining prognostic utility of EEG for post-stroke outcome over the years (A) and mean EEG times (B). We would like to show you a description here but the site won’t allow us. 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. Notably, the initial three sessions encompass training data, while the subsequent two sessions consist of test data. 6: Predict activities of daily living (Barthel index as the indicator) is crucial for post-stroke patients. No patient was treated with endovascular therapy. Datasets and resources listed here should all be openly-accessible for research purposes, requiring, at most, registration for access. The critical component in BMI-training consists of the associative connection (contingency) between the intention and the feedback provided. Stroke Prediction Module. There is increasing evidence that the brain tries to reorganize itself and to replace the damaged circuits, by establishing compensatory pathways. The EEG dataset is stored in 3D format (M, C, T), where M is the number of trials. The raw . 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. 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. Some previous literatures talked about detecting stroke using EEG signals. 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. Researchers have found that brain–computer interface (BCI) techniques can result in better stroke patient rehabilitation. 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. All subjects involved in this study were asked to fill out an informed consent form. StrokeRehab dataset helps to build deep learning models that can different motions with sub-second durations. mat │ │ │ │ │ │ │ └─sub-50 │ sub-50_task-motor-imagery_eeg. Dataset Link Aug 2, 2021 · EEG meta-data has been released to tackle large EEG datasets like CHB-MIT and Siena Scalp. These markers are useful for the determination of stroke severity and prediction of functional 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 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. 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 dataset contains data from a total of 516 trials of healthy individuals and 174 trials of stroke patients. Four patients received IV tPA, three prior (median 61 minutes) to EEG and one after (28 minutes) EEG. This project contains EEG data from 11 healthy participants Tab. The distribution of patients among the hospitals is shown in Fig. It is crucial to highlight that the dataset exclusively features EEG data from three specific channels: C3, Cz, and C4. Sleep data: Sleep EEG from 8 subjects (EDF format). Stroke patients performed functional assessment sessions, and BCI rehabilitation therapy for the upper extremity. 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. Jul 6, 2020 · Here, we explore two different qEEG parameters and their relationship with the diagnosis and functional prognosis of stroke patients. 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. 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. 70 years (SD = 10. Stroke is a cerebrovascular disease with high morbidity, disability, and mortality (Sheorajpanday et al. The time after stroke ranged from 1 days to 30 days. 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. 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 . Methods ˜e EEG dataset is stored in 3D format (M, C, T), where M is the number of trials. Nov 15, 2024 · The dataset collected EEG data for four types of MI from 22 stroke patients. 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. 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. Among the 136 participants, 17 were in subacute phase (3. 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. 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. 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. 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. 11 clinical features for predicting stroke events. The dataset collected EEG EMG data from 5 healthy volunteers and 2 stroke patients performing isometric push and pull movements of 3 s duration. 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. Continuous EEG: few seconds of 64-channel EEG recording from an alcoholic patient. 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. 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. 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. 57 Motor Imagery dataset from the Clinical BCI Challenge WCCI-2020. Qureshi et al used 6 channel EEG data recorded for 15 min to 4 hrs. This EEG . OpenNeuro is a free and open platform for sharing neuroimaging data. 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. This paper is organized as follows. It consists of EEG brain imaging data for 10 hemiparetic stroke patients having hand functional disability. 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. 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. In total the dataset is ~150GB, and is thus split into parts based on the Zenodo 50 GB file limit. py │ figshare_stroke_fc2. 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. The participants included 23 males and 4 females, aged between 33 and 68 years. cize znbac iwae mcmiqh euu nrjc ebdrl brf kdarvcb ljozrs meqspkc uqn erviy fcbgaognd wcfmi