Eeg datasets of stroke patients. Methods Introduction.

Eeg datasets of stroke patients History. 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. 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. Methods Introduction. Three post-stroke patients treated with the recoveriX system (g. This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. This has led to the necessity of exploring new methods for stroke detection, particularly utilizing EEG signals. The dataset is not publicly available and must be obtained directly from the authors. The histograms shows the number of papers for each time period that reported (i) only positive, (ii) only negative, and (iii) mixed (i. Whether you're a researcher, student, or just curious about EEG, our curated selection offers valuable insights and data for exploring the complex and fascinating field of brainwave analysis. GPL 3. 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. With subjects often producing more than one recording per session, the final dataset consisted of 2401 EEG recordings (63% healthy, 37% stroke). 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. 582). Researchers have found that brain–computer interface (BCI) techniques can result in better stroke patient rehabilitation. 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. e. 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 . 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. Dec 1, 2024 · Stroke is a major cause of long-term disability. 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. 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. 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 []. 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. 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. 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. 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 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. 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. Stroke is a cerebrovascular disease with high morbidity, disability, and mortality (Sheorajpanday et al. 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. This database has limitations, including the lack of information about the phase and severity of TBI and stroke. 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. 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. In the rehabilitation of arm impairment after stroke, quantifying the training dose (number of repetitions) requires differentiating motions with sub-second durations. This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. 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. 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. Classification. 1). 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. For EEG signals from stroke patients, the datasets consist of much more wakeful samples than DoC ones. 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. All subjects involved in this study were asked to fill out an informed consent form. However, the relationship between the BMI design and its performance in stroke patients is still an open question institutional EEG data. The amplitudes of EEG signals measured from the scalp of a normal person while awake are expected to be between 10 and 100 mV. 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). 57) (shown in Table 1 ). Methods Subjects Forty-three patients with ischemic stroke in the middle cerebral artery were enrolled. 2011). 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. Therefore, there is a need for an EEG headset evaluation for stroke rehabilitation and neurorehabilitation in general which are important applications for BCI. 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. 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). 2. The initial evaluation of the existence of SN is done with the BIT-C. 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. Results: Using a rich set of features encompassing both the spectral and temporal domains, our model yielded an │ figshare_fc_mst2. 70 years (SD = 10. 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. Dividing the data of each subject into a training set and a test The EEG datasets of patients about motor imagery. There were 39 men and 4 women. Please email arockhil@uoregon. We are provided an EEG Dataset of 10 hemiparetic stroke patients having hand functional disability. Dataset. Datasets and resources listed here should all be openly-accessible for research purposes, requiring, at most, registration for access. Stroke patients performed functional assessment sessions, and BCI rehabilitation therapy for the upper extremity. 09%, and for each patient the test accuracy is shown in the Table 2. procedures can be lengthy, often making it impractical for most stroke patients. 74 years (SD, 9. EEG is a non-invasive way to analyze brain activity changes during stroke, but interpreting complex EEG data remains challenging. The critical component in BMI-training consists of the associative connection (contingency) between the intention and the feedback provided. , 2015). Oct 12, 2021 · Van Putten MJ, Tavy DL (2004) Continuous quantitative EEG monitoring in hemispheric stroke patients using the brain symmetry index. 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 open-source dataset was provided by CBCI Challenge-2020 organized by University of Essex. The EEG of the patients whose limbs and face are affected by stroke must be recorded. 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. 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 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. 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. 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. 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. EEG. Stroke 35(11):2489–2492. Jul 6, 2020 · Here, we explore two different qEEG parameters and their relationship with the diagnosis and functional prognosis of stroke patients. EEG recordings were acquired in diverse settings that included ER, ICU, and stroke ward. , 2011; Larivière et al. Jan 25, 2024 · Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. One group of healthy participants and one group of stroke patients participated in the study. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. In this paper, we propose a cloud computing-based machine learning (ML) system that leverages MUSE2 to diagnose stroke patients by analysing EEG signals. Among the patients, 18 had right hemiplegia, and 9 had left hemiplegia. The mean age was 63. mat │ │ │ ├─sub-02 │ │ sub-02_task-motor-imagery_eeg. py │ figshare_stroke_fc2. Clinical data from each group are presented in Table 1. The dataset included 48 stroke survivors and 75 healthy people. 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. 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. Jan 25, 2024 · Therefore, expanding the EEG datasets for BCI to restore upper limb function in stroke patients is crucial. 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. StrokeRehab dataset helps to build deep learning models that can different motions with sub-second durations. Early and accurate diagnosis of stroke severity can improve patient outcomes. 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. 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. Twenty-five stroke patients were recruited and signed informed consent. 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). assess the value of longitudinal EEG studies in patients in a rehabilitation program. 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 clinical states of stroke patients through experimental studies of 152 patients. 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. , both positive and negative) findings for EEG-based prognosis of post-stroke outcome. Due to the non-stationary nature of electroencephalography (EEG) signals, a Brain-computer Interfacing (BCI) system requires frequent calibration. tec medical engineering GmbH) were enrolled in this study, participants had a mean age of 22 years (SD = 4. 0 Sep 23, 2022 · IntroductionRecent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. 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. Every patient has the right one and left one in according to paretic hand movement or unaffected hand movement. The patients included 39 males (78%) and 11 females (22%), aged between 31 and 77 years, with an average age of 56. This paper is organized as follows. 8 years). 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. 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). 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]. 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 []. . An initial analysis using CSP-SVM on the dataset yielded an average classification accuracy of 80. In this work, we present an EEG-based imaging algorithm to estimate the location and size of the stroke infarct core and penumbra tissues. 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. The patients may be Mar 22, 2024 · In general, datasets from a hospital, such as EEG signals, are imbalanced. The participants included 23 males and 4 females, aged between 33 and 68 years. 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. Feb 8, 2024 · ports, we were able to construct a dataset of 1385 healthy subjects and 374 stroke patients. Share theta, alpha, beta) and propofol requirement to anesthetize a Clinically-meaningful benchmark dataset. 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. Stroke is a critical event that causes the disruption of neural connections. Our federated learning system integrates MQTT as an efficient communication protocol, demonstrating its security in dispatching model updates and aggregation across distributed clients. This page is dedicated to providing you with extensive information on various EEG datasets, publications, software tools, hardware devices, and APIs. Oct 1, 2021 · The EEG dataset from the post-stroke patients with upper extremity hemiparesis was investigated. 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. In Section II, we describe the dataset and modified EEGNet architecture implemented on this patient dataset. ˜e EEG dataset is stored in 3D format (M, C, T), where M is the number of trials. Therefore, whenever available, the tool needs to be further validated with data from more homogeneous populations of patients. 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. This is a list of openly available electrophysiological data, including EEG, MEG, ECoG/iEEG, and LFP data. 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. py │ ├─dataset │ │ subject. There is increasing evidence that the brain tries to reorganize itself and to replace the damaged circuits, by establishing compensatory pathways. 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. 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. 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. Jan 1, 2024 · Epileptiform electroencephalogram (EEG) patterns are commonly observed in stroke patients and can significantly impact clinical management and patient outcomes. The time after stroke ranged from 1 days to 30 days. com) （3）下载链接： EEG datasets of stroke patients (figshare. 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. The dataset includes raw EEG signals, preprocessed data, and patient information. The dataset collected EEG EMG data from 5 healthy volunteers and 2 stroke patients performing isometric push and pull movements of 3 s duration. stroke patients with wireless portable saline EEG devices during the performance of two tasks: ) imagining right-handed movements and ) imagining left-handed movements. Categories. The dataset contains data from a total of 516 trials of healthy individuals and 174 trials of stroke patients. A quantitative method of analyzing EEG signals after stroke onset can help monitor disease progression and tailor treatments. 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. Conclusions. Stroke. RESULTS Subjects. csv │ │ │ └─sourcedata │ ├─sub-01 │ │ sub-01_task-motor-imagery_eeg. Nov 15, 2024 · The dataset collected EEG data for four types of MI from 22 stroke patients. 50%. 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. The participants included 39 male and 11 female. , 2018). Feb 28, 2022 · Background Stroke is a common medical emergency responsible for significant mortality and disability. 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. 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. Keywords. All participants were The dataset must consist of electroencephalography (EEG) data of 50-100 stroke patients. The EEG data was gathered with a 16-channel cap, using 10/20 montage setup. The dataset includes trials of 5 healthy subjects and 6 stroke patients. Methods: We performed a cross-sectional analysis of a cohort study (DEFINE cohort), Stroke arm, with 85 patients, considering demographic, clinical, and stroke characteristics. 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. 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. 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. Is there any publicly-available-dataset related to EEG stroke and normal patients. 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. of any CNN based architecture on patients’ EEG data for MI classification. 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. Licence. 22 participants had right hemisphere hemiplegia and 28 participants had left hemisphere hemiplegia. 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 distribution of patients among the hospitals is shown in Fig. 32-channel electroencephalogram (EEG) was recorded during a finger-tapping task Apr 16, 2023 · The EMG sampling rate was 1,000 Hz. 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. , 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. They characterized changes in cortical connectivity through changes in connection weights between electrode pairs. Table 1. Apr 17, 2023 · The EMG sampling rate was 1,000 Hz. 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. We designed an experimental procedure to extract microstate maps from a single dataset aggre-gated from multiple EEG datasets of all patients. EEG data motor imagery task stroke patient data. 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. Usage metrics. mat │ │ │ │ │ │ │ └─sub-50 │ sub-50_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. 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. 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. The EEG data were analyzed across various frequency bands to construct brain connectivity graphs. 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. 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. This work validated different methodologies to design decoders of movement intentions for completely paralyzed stroke patients. In general, datasets from a hospital, such as EEG signals, are imbalanced. wdm ixvr yogtp aiqjq rmvbawo vjugd hxn ebuzaw sikwj yqlvyk epgwys qtbb sutdzoj nmz cyxdqem