Eeg datasets of stroke patients. There were 39 men and 4 women.

Eeg datasets of stroke patients com) （3）下载链接： EEG datasets of stroke patients (figshare. 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 The RST is currently developed based on publicly available patient data in the TUEG. constructed brain networks for patients with chronic stroke by computing the imaginary part of coherence (IPC) of EEG to assess changes in cortical connectivity induced by transcranial magnetic stimulation (TMS). 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 []. 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. For EEG signals from stroke patients, the datasets consist of much more wakeful samples than DoC ones. 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. However, brain mapping studies during this time are uncommon and longitudinal data are spaced weeks or months apart, which is insufficient to capture neuroplasticity and respond therapeutically. Then, we investigated the correlations between EEG microstates with the level of DOC (awake, somnolence, stupor, light 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 2, 2021 · EEG meta-data has been released to tackle large EEG datasets like CHB-MIT and Siena Scalp. 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. The EEG of the patients whose limbs and face are affected by stroke must be recorded. 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 EEG data were analyzed across various frequency bands to construct brain connectivity graphs. The dataset contains data from a total of 516 trials of healthy individuals and 174 trials of stroke patients. Clinical data from each group are presented in Table 1. Early and accurate diagnosis of stroke severity can improve patient outcomes. Table 1. 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 . 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. e. We are provided an EEG Dataset of 10 hemiparetic stroke patients having hand functional disability. All participants were The dataset must consist of electroencephalography (EEG) data of 50-100 stroke patients. The mean age was 63. 74 years (SD, 9. The proposed approach was tested on a dataset of 10 hemiparetic stroke patients’ MI data set yielding superior performance against the only EEGNet and a more traditional approach such as common Jan 30, 2014 · Motor imagery EEG patterns of stroke patients are detected in spatial–spectral–temporal domain from limited training datasets. In these datasets, the EEG signal is recorded for 10 min from each patient using the standard 10–20 EEG electrode placement system (Fig. The patients may be Mar 22, 2024 · In general, datasets from a hospital, such as EEG signals, are imbalanced. An automatic portable biomarker can potentially facilitate patients triage and ensure timely This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. Targeted datasets focusing on stroke patients are 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. A quantitative method of analyzing EEG signals after stroke onset can help monitor disease progression and tailor treatments. 57) (shown in Table 1 ). The dataset is not publicly available and must be obtained directly from the authors. of any CNN based architecture on patients’ EEG data for MI classification. assess the value of longitudinal EEG studies in patients in a rehabilitation program. 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. 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. Is there any publicly-available-dataset related to EEG stroke and normal patients. 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. 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. 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 · Author summary Traumatic Brain Injury (TBI) and stroke are devastating neurological conditions that affect hundreds of people daily. mat │ │ │ ├─sub-02 │ │ sub-02_task-motor-imagery_eeg. , 2011; Larivière et al. tec medical engineering GmbH) were enrolled in this study, participants had a mean age of 22 years (SD = 4. . GPL 3. Dataset. 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 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 Feb 22, 2025 · In this dataset, we collected EEG data from 27 stroke recovery patients, with disease durations ranging from 1 to 12 months. One group of healthy participants and one group of stroke patients participated in the study. 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 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. An initial analysis using CSP-SVM on the dataset yielded an average classification accuracy of 80. Methods: We performed a cross-sectional analysis of a cohort study (DEFINE cohort), Stroke arm, with 85 patients, considering demographic, clinical, and stroke characteristics. 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. 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. Towards this goal, a longitudinal study of frequent EEG was performed in 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). 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 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. 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. 32-channel electroencephalogram (EEG) was recorded during a finger-tapping task Apr 16, 2023 · The EMG sampling rate was 1,000 Hz. The patients included 39 males (78%) and 11 females (22%), aged between 31 and 77 years, with an average age of 56. csv │ │ │ └─sourcedata │ ├─sub-01 │ │ sub-01_task-motor-imagery_eeg. Licence. 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. This has led to the necessity of exploring new methods for stroke detection, particularly utilizing EEG signals. This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. MethodsThirty-two healthy subjects and thirty-six stroke patients with upper extremity Mar 5, 2025 · While EEG signals measured without an external stimulus are called spontaneous EEG, EEG signals that occur due to external or internal stimuli are called Event-Related Potentials (ERP). 2. Dec 1, 2024 · Stroke is a major cause of long-term disability. 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 Abnormal EEG in general and generalized slowing in particular are associated with clinical deterioration after acute ischemic stroke. There is increasing evidence that the brain tries to reorganize itself and to replace the damaged circuits, by establishing compensatory pathways. The dataset collected EEG EMG data from 5 healthy volunteers and 2 stroke patients performing isometric push and pull movements of 3 s duration. Each participant received three months of BCI-based MI training with two Above mentioned two datasets include EEG data from a total of 10 participants: 5 stroke patients with SN and 5 stroke patients without SN. Previous research examined the classification accuracy for some subjects within this dataset 36 , demonstrating the 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 Dec 15, 2022 · We used the EOG and chin EMG to eliminate eye blink and muscle artifacts. In general, datasets from a hospital, such as EEG signals, are imbalanced. In addition, because of the significant between-participant variability in neuroplasticity in response to rehabilitation Feb 29, 2024 · The neurophysiological pattern of cortical rhythms can be changed by an acute stroke []. There were 39 men and 4 women. In this work, we present an EEG-based imaging algorithm to estimate the location and size of the stroke infarct core and penumbra tissues. The EEG data was gathered with a 16-channel cap, using 10/20 montage setup. Subjects completed specific MI tasks according to on-screen prompts while their EEG data These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. Usage metrics. They characterized changes in cortical connectivity through changes in connection weights between electrode pairs. The dataset consists of 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 time after stroke ranged from 1 days to 30 days. 0 Sep 23, 2022 · IntroductionRecent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. 09%, and for each patient the test accuracy is shown in the Table 2. 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 9, 2024 · Objective: Investigate the relationship between resting-state EEG-measured brain oscillations and clinical and demographic measures in Stroke patients. Share theta, alpha, beta) and propofol requirement to anesthetize a Clinically-meaningful benchmark dataset. Jul 6, 2020 · Here, we explore two different qEEG parameters and their relationship with the diagnosis and functional prognosis of stroke patients. A diagnosis of neglect was established by either a total BIT score lower than the established cutoff (<129), or a score lower than Borich et al. 22 participants had right hemisphere hemiplegia and 28 participants had left hemisphere hemiplegia. Jan 1, 2024 · Request PDF | On Jan 1, 2024, Katerina Iscra and others published Optimizing machine learning models for classification of stroke patients with epileptiform EEG pattern: the impact of dataset of pattern recognition on stroke patients’ EEG, which is a fundamental for implementing BCI-based systems. Datasets and resources listed here should all be openly-accessible for research purposes, requiring, at most, registration for access. Domain adaptation and deep learning-based Welcome to the resting state EEG dataset collected at the University of San Diego and curated by Alex Rockhill at the University of Oregon. 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. Parameters setting and results of EEGNet under two conditions: 1) within-subject classification Feb 21, 2019 · This dataset is about motor imagery experiment for stroke patients. py │ ├─dataset │ │ subject. Results: Using a rich set of features encompassing both the spectral and temporal domains, our model yielded an │ figshare_fc_mst2. This paper is organized as follows. 50%. Due to the non-stationary nature of electroencephalography (EEG) signals, a Brain-computer Interfacing (BCI) system requires frequent calibration. 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 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. Oct 1, 2021 · The EEG dataset from the post-stroke patients with upper extremity hemiparesis was investigated. However, the relationship between the BMI design and its performance in stroke patients is still an open question institutional EEG data. Jan 25, 2024 · Therefore, expanding the EEG datasets for BCI to restore upper limb function in stroke patients is crucial. The dataset includes trials of 5 healthy subjects and 6 stroke patients. the clinical states of stroke patients through experimental studies of 152 patients. History. Building on recent advancements in localizing neural silences, we develop an algorithm that utilizes known spectral properties of 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. ˜e EEG dataset is stored in 3D format (M, C, T), where M is the number of trials. EEG. RESULTS Subjects. The dataset included 48 stroke survivors and 75 healthy people. Twenty-five stroke patients were recruited and signed informed consent. A standardized data collection 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 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. 8 years). In Section II, we describe the dataset and modified EEGNet architecture implemented on this patient dataset. Dividing the data of each subject into a training set and a test The EEG datasets of patients about motor imagery. Three post-stroke patients treated with the recoveriX system (g. This study develops an explainable multi-task learning approach for EEG-based stroke 6 days ago · On the MI-EEG dataset of SCI patients, the model is trained using the fine-tuning strategy of migration learning, and the average accuracy of the data test for each patient reaches 95. The amplitudes of EEG signals measured from the scalp of a normal person while awake are expected to be between 10 and 100 mV. We designed an experimental procedure to extract microstate maps from a single dataset aggre-gated from multiple EEG datasets of all patients. 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. Methods Subjects Forty-three patients with ischemic stroke in the middle cerebral artery were enrolled. 70 years (SD = 10. Please email arockhil@uoregon. py │ figshare_stroke_fc2. Conclusions. stroke patients with wireless portable saline EEG devices during the performance of two tasks: ) imagining right-handed movements and ) imagining left-handed movements. All participants were 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. 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. Patient electroencephalography (EEG) datasets are 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. 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. 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。 This method has established utility for accurately assessing a model's potential to generalize to an independent data set (Huang et al. All subjects involved in this study were asked to fill out an informed consent form. EEG data motor imagery task stroke patient data. 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. 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. The critical component in BMI-training consists of the associative connection (contingency) between the intention and the feedback provided. mat │ └─data_load 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. 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 2. 1). This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. Keywords. 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. There are five distinct experiments: the initial assessment with a conventional paradigm prompted by text (Pre on stroke, updating previous revisions [12] with a specic focus on dierent qEEG measures as biomarkers of clinical outcome. StrokeRehab dataset helps to build deep learning models that can different motions with sub-second durations. , 2015). mat │ │ │ │ │ │ │ └─sub-50 │ sub-50_task-motor-imagery_eeg. In recent years, machine learning based methods, especially deep neural networks, have improved the pattern recognition and classification Dec 7, 2024 · This study utilizes a comprehensive dataset comprising EEG recordings from 72 patients collected during hospitalization across four medical centers. The experimental results show that the proposed method can achieve good classification The studies that have investigated EEG headset usability have primarily focused on communication using P300 and EEG recordings, and not on stroke rehabilitation and self-mounting. Our federated learning system integrates MQTT as an efficient communication protocol, demonstrating its security in dispatching model updates and aggregation across distributed clients. Therefore, there is a need for an EEG headset evaluation for stroke rehabilitation and neurorehabilitation in general which are important applications for BCI. Surface electroencephalography (EEG) shows promise for stroke identification and Oct 25, 2024 · This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. In conclusion, an increasing trend in the release of open-source EEG datasets has been observed with approach and leveraged the EEG datasets of patients at two- time points (i. Be sure to check the license and/or usage agreements for Nov 30, 2024 · An EEG motor imagery dataset for brain computer interface in acute stroke patients | Scientific Data (nature. 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. Methods Introduction. procedures can be lengthy, often making it impractical for most stroke patients. Among the patients, 18 had right hemiplegia, and 9 had left hemiplegia. The participants included 23 males and 4 females, aged between 33 and 68 years. Jan 25, 2024 · Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. , both positive and negative) findings for EEG-based prognosis of post-stroke outcome. The open-source dataset was provided by CBCI Challenge-2020 organized by University of Essex. We validate our method approach on a dataset of EEG recordings from 72 stroke patients Jan 30, 2025 · Introduction: Neuroplasticity is highest during the first weeks after stroke and can be studied at the bedside using EEG. 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. This work validated different methodologies to design decoders of movement intentions for completely paralyzed stroke patients. The initial evaluation of the existence of SN is done with the BIT-C. 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. The mean interval between the stroke onset and the first EEG Dec 12, 2022 · This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. 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. The participants included 39 male and 11 female. 582). 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. Feb 28, 2022 · Background Stroke is a common medical emergency responsible for significant mortality and disability. Fifteen stroke patients completed a total of 237 motor imagery brain–computer interface (BCI 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. 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 is a list of openly available electrophysiological data, including EEG, MEG, ECoG/iEEG, and LFP 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 Aug 5, 2023 · Object Quantitative electroencephalography (qEEG) has shown promising results as a predictor of clinical impairment in stroke. 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 Jan 30, 2014 · Motor imagery EEG patterns of stroke patients are detected in spatial–spectral–temporal domain from limited training datasets. 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. Every patient has the right one and left one in according to paretic hand movement or unaffected hand movement. , 2018). , before and after the rehabilitation therapy) and healthy controls to explore the three aforementioned Nov 20, 2018 · Background Brain machine interface (BMI) technology has demonstrated its efficacy for rehabilitation of paralyzed chronic stroke patients. Stroke 35(11):2489–2492. This database has limitations, including the lack of information about the phase and severity of TBI and stroke. Categories. The histograms shows the number of papers for each time period that reported (i) only positive, (ii) only negative, and (iii) mixed (i. Stroke is a critical event that causes the disruption of neural connections. With subjects often producing more than one recording per session, the final dataset consisted of 2401 EEG recordings (63% healthy, 37% stroke). 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. 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 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]. 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. 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 The number of papers published examining prognostic utility of EEG for post-stroke outcome over the years (A) and mean EEG times (B). The dataset includes raw EEG signals, preprocessed data, and patient information. Stroke patients performed functional assessment sessions, and BCI rehabilitation therapy for the upper extremity. 2011). Researchers have found that brain–computer interface (BCI) techniques can result in better stroke patient rehabilitation. Stroke. EEG recordings were acquired in diverse settings that included ER, ICU, and stroke ward. Feb 8, 2024 · ports, we were able to construct a dataset of 1385 healthy subjects and 374 stroke patients. Jan 1, 2024 · Epileptiform electroencephalogram (EEG) patterns are commonly observed in stroke patients and can significantly impact clinical management and patient outcomes. EEG is a cheap noninvasive technique that 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. The distribution of patients among the hospitals is shown in Fig. Apr 17, 2023 · The EMG sampling rate was 1,000 Hz. 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). In the rehabilitation of arm impairment after stroke, quantifying the training dose (number of repetitions) requires differentiating motions with sub-second durations. Therefore, whenever available, the tool needs to be further validated with data from more homogeneous populations of patients. Nov 15, 2024 · The dataset collected EEG data for four types of MI from 22 stroke patients. Stroke is a cerebrovascular disease with high morbidity, disability, and mortality (Sheorajpanday et al. Oct 12, 2021 · Van Putten MJ, Tavy DL (2004) Continuous quantitative EEG monitoring in hemispheric stroke patients using the brain symmetry index. This page is dedicated to providing you with extensive information on various EEG datasets, publications, software tools, hardware devices, and APIs. Classification. EEG is a non-invasive way to analyze brain activity changes during stroke, but interpreting complex EEG data remains challenging. ikoucn juahb ngjbiqrx gko qklsogh jdqott lswak rmllbjel mpcyeby yohkd fpawea epmg adogzk cuvwyzl otfuk