This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. The participants included 39 male and 11 female. The time after stroke ranged from 1 days to 30 days. 22 participants had right hemisphere hemiplegia and 28 participants had left hemisphere hemiplegia. All participants were originally right-handed. Each of the participants sat in front of a computer screen with an arm resting on a pillow on their lap or on a table and they carried out the instructions given on the computer screen. At the trial start, a picture with text description which was circulated with left right hand, were presented for 2s. We asked the participants to focus their mind on the hand motor imagery which was instructed, at the same time, the video of ipsilateral hand movement is displayed on the computer screen and lasts for 4s. Next, take a 2s break.

In this dataset, we collected EEG data from 27 stroke recovery patients, with disease durations ranging from 1 to 12 months. The participants included 23 males and 4 females, aged between 33 and 68 years. Among the patients, 18 had right hemiplegia, and 9 had left hemiplegia. There are five distinct experiments: the initial assessment with a conventional paradigm prompted by text (Pre), initial assessment with an invariable electrical stimulation paradigm (IES), initial assessment with a gait-phase-encoded sequential sensory electrical stimulation paradigm (SES), post-treatment assessment with a conventional paradigm (Post), and follow-up assessment with a conventional paradigm (Follow). Each experiment comprised two tasks: gait MI and a idle-state task, where participants were instructed to focus on kinesthetic motor imagery to perform the gait MI task. Each trial lasted approximately 12 seconds. At the beginning of each trial (t = 0s), a fixation cross appeared on the screen, where MI tasks were labeled as "1" and idle tasks as "7." At t = 3s, the task text prompt was displayed, where MI tasks were labeled as "2" and idle tasks as "8." At t = 4s, participants started MI or idle task for 5 seconds. No stimulation was applied in the conventional tasks. the IES tasks received a constant sensory electrical stimulation, while the SES tasks received a gait-phase-encoded sequential sensory electrical stimulation. During the MI phase, the labels "3" to "6" were used sequentially according to the gait phase. For the idle tasks, the resting-state phase labeled sequentially as "9" to "12." In each paradigm, participants performed 10 trials each of gait MI and resting tasks in one run, presented in a randomized order.

This dataset includes EEG data from 8 subjects who were performing self-intended unilateral and bilateral movement executions. The dataset is structured as follows. The EEG_dataset folder contains an EEG and Metadata folder. The Metadata folder contains text files for each of the measured subjects identified by an individual pseudo-code with general meta information. Also, a short description of the experiment is provided in an additional text file. The EEG folder contains the EEG measurements separated into the unilateral and bilateral conditions of the study as well as a readme (text file) including useful information about the data recording. In each subfolder of the measurement conditions (unilateral and bilateral) the measurements are stored for each of the subjects. The data is stored in the BrainVision format (see https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/ for more information regarding the data format).

The EEG datasets of patients about motor imagery

Background: Early and accurate identification of factors that predict post-stroke cognitive outcome is important to set realistic targets for rehabilitation and to guide patients and their families accordingly. However, behavioral measures of cognition are difficult to obtain in the acute phase of recovery due to clinical factors (e.g. fatigue) and functional barriers (e.g. language deficits). The aim of the current study was to test whether single channel wireless EEG data obtained acutely following stroke could predict longer-term cognitive function. Methods: Resting state Relative Power (RP) of delta, theta, alpha, beta, delta/alpha ratio (DAR), and delta/theta ratio (DTR) were obtained from a single electrode over FP1 in 24 participants within 72 hours of a first-ever stroke. The Montreal Cognitive Assessment (MoCA) was administered at 90-days post-stroke. Correlation and regression analyses were completed to identify relationships between 90-day cognitive function and electrophysi...

Study data involving stroke patients, tDCS and dual-task training

https://docs.google.com/spreadsheets/d/1stjZYc5_58mnXEVUUtsbWVTMiNaHEyqh/edit#gid=1990422083

IntroductionMotor imagery functional near-infrared spectroscopy (MI-fNIRS) offers precise monitoring of neural activity in stroke rehabilitation, yet accurate cross-subject classification remains challenging due to limited training samples and significant inter-subject variability. This study proposes a Cross-Subject Heterogeneous Transfer Learning Model (CHTLM) to enhance the generalization of MI-fNIRS signal classification in stroke patients.MethodsCHTLM leverages labeled electroencephalogram (EEG) data from healthy individuals as the source domain. An adaptive feature matching network aligns task-relevant feature maps and convolutional layers between source (EEG) and target (fNIRS) domains. Multi-scale fNIRS features are extracted, and a sparse Bayesian extreme learning machine classifies the fused deep learning features.ResultsExperiments utilized two MI-fNIRS datasets from eight stroke patients pre- and post-rehabilitation. CHTLM achieved average accuracies of 0.831 (pre-rehabilitation) and 0.913 (post-rehabilitation), with mean AUCs of 0.887 and 0.930, respectively. Compared to five baselines, CHTLM improved accuracy by 8.6–10.5% pre-rehabilitation and 11.3–15.7% post-rehabilitation.DiscussionThe model demonstrates robust cross-subject generalization by transferring task-specific knowledge from heterogeneous EEG data while addressing domain discrepancies. Its performance gains post-rehabilitation suggest clinical potential for monitoring recovery progress. CHTLM advances MI-fNIRS-based brain-computer interfaces in stroke rehabilitation by mitigating data scarcity and variability challenges.

The brain, as a complex dynamically distributed information processing system, involves the coordination of large-scale brain networks such as neural synchronization and fast brain state transitions, even at rest. However, the neural mechanisms underlying brain states and the impact of dysfunction following brain injury on brain dynamics remain poorly understood. To this end, we proposed a microstate-based method to explore the functional connectivity pattern associated with each microstate class. We capitalized on microstate features from eyes-closed resting-state EEG data to investigate whether microstate dynamics differ between subacute stroke patients (N = 31) and healthy populations (N = 23) and further examined the correlations between microstate features and behaviors. An important finding in this study was that each microstate class was associated with a distinct functional connectivity pattern, and it was highly consistent across different groups (including an independent dataset). Although the connectivity patterns were diminished in stroke patients, the skeleton of the patterns was retained to some extent. Nevertheless, stroke patients showed significant differences in most parameters of microstates A, B, and C compared to healthy controls. Notably, microstate C exhibited an opposite pattern of differences to microstates A and B. On the other hand, there were no significant differences in all microstate parameters for patients with left-sided vs. right-sided stroke, as well as patients before vs. after lower limb training. Moreover, support vector machine (SVM) models were developed using only microstate features and achieved moderate discrimination between patients and controls. Furthermore, significant negative correlations were observed between the microstate-wise functional connectivity and lower limb motor scores. Overall, these results suggest that the changes in microstate dynamics for stroke patients appear to be state-selective, compensatory, and related to brain dysfunction after stroke and subsequent functional reconfiguration. These findings offer new insights into understanding the neural mechanisms of microstates, uncovering stroke-related alterations in brain dynamics, and exploring new treatments for stroke patients.

Anonymized MRI data of patients with post-stroke aphasia. Sociodemographic, diagnosis and lesion details in Cid-Fernández et al., 2022 Acquisition parameters: Philips 3T Achieva scanner sagittal T1-weighted 3D Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) sequence (repetition time/echo time = 7.45 ms / 3.40 ms. flip angle = 8º; 180 slices, voxel size = 1 x 1 x 1 mm, field of view = 240 x 240 mm2, matrix size = 240 x 240 mm)

Correlations between 90-day MoCA scores and acute qEEG metrics, neurological measures, and demographic characteristics for all participants (n = 19) and only participants with ischemic stroke (n = 15).

EEG dataset from the triple categorized MI-BCI of left and right hand and biped, with some data from stroke patients (number unknown) and some data from healthy individuals.

qEEG results for the stroke participants (n = 19) and the normative sample (n = 19; [27]).

This collection contains the processed EEG responses to faces and novel objects during the pre-training and post-training test sessions. We used the fast periodic visual stimulation (FPVS) paradigm. Subjects performed a fixation task during EEG data collection. The data for this study are organised into four collections. The first contains the visual stimuli used throughout the study. These includes faces and novel three-dimensional (3D) objects rendered from different viewpoints. This collection contains the 3D model and Matlab scripts to help create more stimuli. The second contains the response time and accuracy data during the training sessions and during the pre-training and post-training test sessions. We used an inversion task for the test sessions. During all sessions, subjects were responding to faces or novel objects. The last contains the raw functional data for brain responses to faces and novel objects. In this phase, we adapted the FPVS paradigm used in the EEG study to the FMRI study. Subjects performed a fixation task during FMRI data collection. We also acquired structural and diffusion imaging data. (Find the other collections under 'Related Resources')Recognising faces is at the heart of human social interactions. By adulthood, people are very good at extracting identity, sex, race, emotions, and social signals from faces. Therefore, impairments to this ability can drastically reduce their quality of life. The aim of this project is to investigate the neural mechanisms underlying people’s ability to process faces and how these mechanisms adapt with experience. The approach is to test whether individuals with prosopagnosia can acquire expertise of novel non-face objects through training. These individuals had head trauma during adulthood that lead to damage in specific brain regions. These regions are thought to process only faces and no other object categories. However, these regions may be more generally involved in processing object categories for which people have expertise (eg, bird experts). In addition to neurological case studies, volunteers will also go through the training. Their brain will be scanned using magnetic resonance imaging to determine how the putative face-specific regions change over the course of training. Overall, the results will have an impact on clinical populations which can result in face recognition deficits, such as Alzheimer’s disease, stroke patients, and developmental disorders that affect social interactions (eg, Autism). EEG was acquired at 512 Hz using a 128-channel Biosemi Active II system (Biosemi, Amsterdam, Netherlands), with electrodes including standard 10-20 system locations as well as additional intermediate positions (http://www.biosemi.com). Two additional electrodes (Common 252 Mode Sense [CMS] active electrode and Driven Right Leg [DRL] passive electrode) were used as reference and ground electrodes, respectively. Eye movements were monitored using four electrodes placed at the outer canthi of the eyes and above and below the right eye.

Conduction aphasia is a language disorder occurred after a left-brain injury. It is characterized
by fluent speech production, reading, writing and normal comprehension, while speech repetition is
impaired. The aimof this study is to investigate the cortical responses, induced by language activities,
in a sub-acute stroke patient affected by conduction aphasia before and after an intensive speech therapy
training. The patientwas examined by usingHigh-Density Electroencephalogram(HD-EEG) examination,
while was performing language tasks. The patient was evaluated at baseline and after two months
after rehabilitative treatment. Our results showed that an intensive rehabilitative process, in sub-acute
stroke, could be useful for a good outcome of language deficits. HD-EEG results showed that left
parieto-temporol-frontal areas were more activated after 2 months of rehabilitation training compared
with baseline. Our results provided evidence that an intensive rehabilitation process could contribute to
an inter- and intra-hemispheric reorganization.

BackgroundEndovascular thrombectomy (EVT) is the standard treatment for large vessel occlusion stroke of the anterior circulation (LVO-a stroke). Approximately half of EVT-eligible patients are initially presented to hospitals that do not offer EVT. Subsequent inter-hospital transfer delays treatment, which negatively affects patients' prognosis. Prehospital identification of patients with LVO-a stroke would allow direct transportation of these patients to an EVT-capable center. Electroencephalography (EEG) may be suitable for this purpose because of its sensitivity to cerebral ischemia. The hypothesis of ELECTRA-STROKE is that dry electrode EEG is feasible for prehospital detection of LVO-a stroke.MethodsELECTRA-STROKE is an investigator-initiated, diagnostic study. EEG recordings will be performed in patients with a suspected stroke in the ambulance. The primary endpoint is the diagnostic accuracy of the theta/alpha ratio for the diagnosis of LVO-a stroke, expressed by the area under the receiver operating characteristic (ROC) curve. EEG recordings will be performed in 386 patients.DiscussionIf EEG can be used to identify LVO-a stroke patients with sufficiently high diagnostic accuracy, it may enable direct routing of these patients to an EVT-capable center, thereby reducing time-to-treatment and improving patient outcomes.Clinical trial registrationClinicalTrials.gov, identifier: NCT03699397.

Cerebral stroke is a common disease across the world, and it is a promising method to recognize the intention of stroke patients with the help of brain–computer interface (BCI). In the field of motor imagery (MI) classification, appropriate filtering is vital for feature extracting of electroencephalogram (EEG) signals and consequently influences the accuracy of MI classification. In this case, a novel two-stage refine filtering method was proposed, inspired by Gradient-weighted Class Activation Mapping (Grad-CAM), which uses the gradients of any target concept flowing into the final convolutional layer to highlight the important part of training data for predicting the concept. In the first stage, MI classification was carried out and then the frequency band to be filtered was calculated according to the Grad-CAM of the MI classification results. In the second stage, EEG was filtered and classified for a higher classification accuracy. To evaluate the filtering effect, this method was applied to the multi-branch neural network proposed in our previous work. Experiment results revealed that the proposed method reached state-of-the-art classification kappa value levels and acquired at least 3% higher kappa values than other methods This study also proposed some promising application scenarios with this filtering method.

Hand rehabilitation in chronic stroke remains challenging, and finding markers that could reflect motor function would help to understand and evaluate the therapy and recovery. The present study explored whether brain oscillations in different electroencephalogram (EEG) bands could indicate the motor status and recovery induced by action observation-driven brain–computer interface (AO-BCI) robotic therapy in chronic stroke. The neurophysiological data of 16 chronic stroke patients who received 20-session BCI hand training is the basis of the study presented here. Resting-state EEG was recorded during the observation of non-biological movements, while task-stage EEG was recorded during the observation of biological movements in training. The motor performance was evaluated using the Action Research Arm Test (ARAT) and upper extremity Fugl–Meyer Assessment (FMA), and significant improvements (p  0.01) were found both in the pre-training and post-training stages. After comparing the variation of oscillations over training, we found patients with good and poor recovery presented different trends in delta, low-beta, and high-beta variations, and only patients with good recovery presented significant changes in EEG band power after training (delta band, p 

The global EEG brain imaging market is experiencing robust growth, driven by technological advancements, increasing prevalence of neurological disorders, and rising demand for accurate and non-invasive diagnostic tools. The market, currently valued at approximately $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by the development of advanced EEG systems with improved resolution and portability, enabling faster and more efficient diagnoses in various healthcare settings. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) algorithms in EEG analysis is enhancing diagnostic accuracy and streamlining workflows, further boosting market expansion. The aging global population, coupled with an increasing incidence of neurological conditions such as epilepsy, Alzheimer's disease, and stroke, contributes significantly to the market's upward trajectory. Several key segments contribute to the market's growth. The increasing adoption of portable and wireless EEG systems for home healthcare and ambulatory monitoring is a significant driver. Furthermore, the development of sophisticated software for EEG data analysis and interpretation is enhancing diagnostic accuracy and reducing the time required for results. Leading players like Nihon Kohden, Natus Medical, Medtronic, and Compumedics are actively involved in research and development, introducing innovative technologies and expanding their product portfolios to cater to the growing market demand. The market is expected to see continued fragmentation, driven by technological innovation and the entry of new players offering specialized solutions. However, high costs associated with advanced EEG systems and a lack of skilled professionals in certain regions might pose challenges to market growth in the coming years.

OBJECTIVE: To study the diagnostic value of volume perfusion CT (VPCT) in patients with transient focal neurological deficits following and during epileptic seizures, that mimic symptoms of stroke. METHODS: a retrospective case-control study was performed on 159 patients that presented with a seizure and received an emergency VPCT within the first 3.5 hours of admission, after being misjudged to have an acute stroke. The reference test was a clinical-based, EEG-supported diagnostic algorithm for seizure. RESULTS: We included 133 patients: 94 stroke-mimicking cases with postictal focal neurological deficits (“Todd’s phenomenon”, n=67) or ongoing seizure on hospital admission (“ictal patients”, n=27), and 39 postictal controls without focal neurological deficits. Patients with Todd’s phenomenon showed normal (64%), hypo (21%)- and hyperperfusion (14%) on early VPCT. Ictal patients displayed more hyperperfusion compared to postictal patients (p=0.015). Test sensitivity of hyperperfusion for ictal patients is 38% CI [20.7-57.7], specificity 86% CI [77.3-91.7], positive predictive value (ppv) is 42% CI [27.5-58.7], the negative predictive value (npv) 83% CI [78.6-86.9]. A cortical distribution was seen in all hyperperfusion scans, compared to a cortico-subcortical pattern in hypoperfusion (p<0.001). A history of complex focal seizure and age were associated with hyperperfusion (p= 0.046 and 0.038, respectively). CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that VPCT accurately differentiates ictal stroke mimics from acute ischemic stroke. CONCLUSION: VPCT can differentiate ictal stroke mimics with hyperperfusion from acute ischemic stroke, but not postictal patients who display perfusion patterns overlapping with ischemic stroke.