{"doi":"10.1038/s41597-024-04090-6","title":"A continuous pursuit dataset for online deep learning-based EEG brain-computer interface","abstract":"This dataset is from an EEG brain-computer interface (BCI) study investigating the use of deep learning (DL) for online continuous pursuit (CP) BCI. In this task, subjects use Motor Imagery (MI) to control a cursor to follow a randomly moving target, instead of a single stationary target used in other traditional BCI tasks. DL methods have recently achieved promising performance in traditional BCI tasks, but most studies investigate offline data analysis using DL algorithms. This dataset consists of ~168 hours of EEG recordings from complex CP BCI experiments, collected from 28 unique human subjects over multiple sessions each, with an online DL-based decoder. The large amount of subject specific data from multiple sessions may be useful for developing new BCI decoders, especially DL methods that require large amounts of training data. By providing this dataset to the public, we hope to help facilitate the development of new or improved BCI decoding algorithms for the complex CP paradigm for continuous object control, bringing EEG-based BCIs closer to real-world applications.","journal":"Scientific Data","year":2024,"id":486445,"datarank":0.24870274690743013,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0072870600423150566,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0072870600423150566,"corpus_percentile":40.07890461824089,"corpus_rank":7747,"citation_count":4,"citer_count":4,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.9044,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":70.8333,"fair_percentile":91.99021705900336,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":859138,"name":"Hao Zhu","orcid":"0000-0002-6756-9571","position":1,"is_corresponding":false},{"id":271429,"name":"Bin He","orcid":"0000-0003-2944-8602","position":2,"is_corresponding":false},{"id":859139,"name":"Dylan Forenzo","orcid":"0000-0002-2661-7434","position":0,"is_corresponding":true}],"reference_count":51,"raw_metadata":null,"created_at":"2026-07-19T02:08:01.404471Z","pmid":"39567538","pmcid":"PMC11579365","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":94.4444,"fair_a":62.5,"fair_i":40.0,"fair_r":58.3333,"fair_zscore":1.4401,"fair_rationale":{"fair_score":70.83,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":94.44,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":1.0,"verdict":"yes","evidence":"https://doi.org/10.1184/R1/25360300","grounded":true,"rationale":"The paper provides a DOI for its own dataset.","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit","RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier'","FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'"],"scored":true,"signal":null},{"key":"f_repository_named","label":"Named repository","kind":"llm","weight":2.0,"fraction":1.0,"verdict":"yes","evidence":"This dataset was uploaded to the Figshare (https://figshare.com) platform","grounded":true,"rationale":"Figshare is a named data repository. 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[majority verdict 'yes' (4/5 passes agreed)]","anchors":["RDA-I3-01M — '(meta)data include references to other (meta)data'","RDA-I3-03M — 'metadata includes qualified references to other metadata'","FsF-I3-01M — F-UJI: 'Metadata includes links between the data and its related entities'"],"scored":false,"signal":null}]},"R":{"name":"Reusable","score":58.33,"criteria":[{"key":"r_reuse_license","label":"Reuse licence","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No license for the data is stated in the paper text.","anchors":["RDA-R1.1-01M — 'Metadata includes information about the licence under which the data can be reu","RDA-R1.1-02M — 'Metadata refers to a standard reuse licence'","RDA-R1.1-03M — 'Metadata refers to a machine-understandable reuse licence'"],"scored":true,"signal":null},{"key":"r_provenance_methods","label":"Provenance of the data","kind":"llm","weight":1.0,"fraction":1.0,"verdict":"yes","evidence":"64-channel Neuroscan Quik-caps with the SynAmps/RT amplifiers","grounded":true,"rationale":"The paper names the specific instruments used to produce the data. 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'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No license for the data is stated in the paper text.","gain":16.67,"priority":"essential","scored":true},{"key":"f_dataset_cited","dimension":"F","label":"Dataset formally cited","action":"Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the neuroimaging repository accession (e.g. from OpenNeuro or NeuroVault) in the reference list.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"Forenzo, D. & He, B. 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A reporting checklist standardises your paper; it does nothing for your data. In neuroimaging, describe the data with BIDS, NIfTI or DICOM.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No data or metadata community standard (ontology, checklist, schema) is named.","gain":0.0,"priority":"important","scored":false},{"key":"a_controlled_access_for_sensitive","dimension":"A","label":"Gatekeeper for sensitive data","action":"Route sensitive data through an institutional gatekeeper — deposit in a controlled- access repository (dbGaP, EGA) with a Data Access Committee and a published DUA — rather than through the corresponding author's inbox. An author-gated dataset dies with the author's email address, and 'on reasonable request' has been shown repeatedly not to yield data.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"informed consent for the experimental procedure, including data sharing, was obtained from each subject via written consent form.","why":"The data are human-subject but shared openly with consent; no institutional gatekeeper is named.","gain":0.0,"priority":"useful","scored":false},{"key":"a_timeline_retention","dimension":"A","label":"Availability timing & retention","action":"State when the data become available AND how long they will be retained — cite the repository's preservation policy. 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Cite the neuroimaging repository accession (e.g. from OpenNeuro or NeuroVault) in the reference list.","Release the data in an open, community-standard format (CSV/TSV, JSON, HDF5, NetCDF, FASTQ, VCF, NIfTI…) instead of — or alongside — any proprietary or instrument-native format, and name the format in the paper. A dataset that needs a €2,000 licence to open is not reusable. Prefer open neuroimaging formats such as NIfTI or BIDS.","Version the deposit and cite the exact version analysed (a version-specific DOI, or an accession with its version suffix). A reader reproducing your work against 'the current release' is reproducing it against a different dataset.","State the access level in words, using the standard vocabulary: 'These data are open access' / 'These data are controlled access'. A reader — and a harvester — should not have to infer the access level from the presence of a download link."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"unpaywall_pdf"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"unpaywall_pdf","fair_has_llm":true,"fair_computed_at":"2026-07-20T13:07:31.954197Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}