{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3466","dataset_id":"ds006801","associated_paper_doi":null,"authors":["Paloma Victoria de Sales Alves","Antonio Simeão Sobrinho Neto","Carla Alexandra da Silva Moita Minervino"],"bids_version":"1.9.0","contact_info":["PALOMA ALVES"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds006801.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":21,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds006801","osf_url":null,"github_url":null,"paper_url":null},"funding":["Coordination for the Improvement of Higher Education Personnel (CAPES)","Federal University of Paraíba (UFPB)"],"ingestion_fingerprint":"52fd49c560384f39f9ecd3d71d9a2a314b1af39e8c019431c6f71c77e877f4a9","license":"CC0","n_contributing_labs":null,"name":"Resting-state EEG before and after different study methods","readme":"The RECAP-EEG: Retrieval with Feedback and Cognitive Adaptation EEG Dataset provides an open-access collection of human electroencephalography (EEG) recordings aimed at investigating the neural correlates of learning processes in educational contexts. The study involved 21 neurotypical undergraduate students (mean age = 23.10 years, SD = 3.92) and was conducted at the Federal University of Paraíba (UFPB), Brazil. Participants were randomly assigned to one of three experimental groups through an automated Python v3.12.7 script that ensured continuous balance among groups. In the active learning group, participants completed a review session using the NeuroShow platform, which consisted of 10 retrieval-practice questions with immediate feedback after each response. In the passive learning group, participants performed a review session based on their own notes taken during the lecture. In the control group, participants watched the same lecture but did not perform any review activity.\nBefore data collection, all participants received detailed written instructions recommending that they avoid consuming caffeine or alcohol for at least 12 hours before the session, maintain a good night’s sleep, and have a proper breakfast on the morning of the experiment. Sessions were scheduled to start at 9:00 a.m., with a maximum delay tolerance of 15 minutes. Upon arrival at the laboratory, participants were briefed about the procedures specific to their group and were given the opportunity to ask questions before the experiment began. The first EEG recording (pre-intervention) was then performed, followed by the respective study condition for each group (active, passive, or control), and finally the second EEG recording (post-intervention).\nEEG signals were recorded using a 32-channel ActiChamp system (Brain Products GmbH, Germany) with active silver/silver chloride (Ag/AgCl) electrodes positioned according to the international 10–20 system. Electrode impedance was kept below 15 kΩ, with the ground at Fpz. Signals were sampled at 500 Hz, filtered between 0.5 and 50 Hz, and recorded at two time points: before and immediately after the study session. Each session lasted approximately nine minutes, comprising four blocks: two eyes-open blocks (2 minutes and 15 seconds each) and two eyes-closed blocks (2 minutes and 15 seconds each). The raw EEG data are organized in compliance with the BIDS (Brain Imaging Data Structure) standard and include .vhdr, .eeg, and .vmrk files, as well as the required metadata and descriptive files. Signal quality was ensured through impedance control and power spectral density (PSD) analysis, which confirmed the integrity and consistency of the recordings.\nThe RECAP-EEG dataset may contribute to research in cognitive neuroscience and learning, particularly studies on retrieval practice with feedback, attentional modulation, and functional reorganization associated with active learning. It also supports interdisciplinary investigations in educational neuroscience, cognitive training, and neural modeling of learning and memory processes.\nThe study was approved by the Research Ethics Committee of the Health Sciences Center at the Federal University of Paraíba (CCS/UFPB) under CAAE number 84958824.1.0000.5188 and approval number 7.400.264. All participants provided written informed consent prior to participation. The data are released under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction provided that proper credit is given to the original authors.","recording_modality":["eeg"],"senior_author":"Carla Alexandra da Silva Moita Minervino","sessions":["post","pre"],"size_bytes":1414488873,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["rest"],"timestamps":{"digested_at":"2026-04-22T12:29:35.893618+00:00","dataset_created_at":"2025-10-16T15:15:26.032Z","dataset_modified_at":"2025-10-16T15:41:38.000Z"},"total_files":42,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006801","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"d2a1aefb8a1a2177","model":"openai/gpt-5.2","tagged_at":"2026-01-20T19:08:20.434724+00:00"},"tags":{"pathology":["Healthy"],"modality":["Resting State"],"type":["Learning"],"confidence":{"pathology":0.85,"modality":0.85,"type":0.7},"reasoning":{"few_shot_analysis":"Closest convention matches are the resting-state examples.\n- The few-shot dataset “A Resting-state EEG Dataset for Sleep Deprivation” is labeled Modality=Resting State and Type=Resting-state, and its README explicitly describes “resting-state EEG data (eyes open,partially eyes closed)”. This guides labeling eyes-open/eyes-closed blocks as Resting State modality.\n- The few-shot “EEG, pupillometry… digit span task and rest” shows that when the goal is a cognitive construct (there: working memory), Type can reflect that construct even if there is also rest recorded. This supports considering Learning as Type here because the dataset’s stated aim is learning/retrieval practice effects, despite recordings being resting blocks.","metadata_analysis":"Key factual extracts from the dataset README:\n- Population: “The study involved 21 neurotypical undergraduate students (mean age = 23.10 years, SD = 3.92)”.\n- Resting-state recording structure: “Each session lasted approximately nine minutes, comprising four blocks: two eyes-open blocks… and two eyes-closed blocks…”.\n- Learning/intervention purpose: “aimed at investigating the neural correlates of learning processes in educational contexts” and “In the active learning group… 10 retrieval-practice questions with immediate feedback”.\n- Pre/post design: “The first EEG recording (pre-intervention)… and finally the second EEG recording (post-intervention).”","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n1) Metadata says: “21 neurotypical undergraduate students” (i.e., no disorder-based recruitment).\n2) Few-shot pattern suggests: neurotypical/general population cohorts map to Healthy.\n3) ALIGN.\n\nModality:\n1) Metadata says: “four blocks: two eyes-open… and two eyes-closed…” (no external sensory stimulus described during EEG recording blocks).\n2) Few-shot pattern suggests: eyes-open/eyes-closed protocols map to Resting State modality.\n3) ALIGN.\n\nType:\n1) Metadata says primary research aim is learning: “investigating the neural correlates of learning processes” and specifically “retrieval-practice questions with immediate feedback”. It also says recordings are resting blocks.\n2) Few-shot pattern suggests two plausible conventions: (a) label as Resting-state when data are purely resting-state (sleep deprivation example), or (b) label by target construct when rest is used alongside/around a cognitive manipulation (digit span labeled Memory).\n3) PARTIAL ALIGN / AMBIGUOUS (no direct conflict, but two valid conventions). Metadata emphasizes learning as the scientific purpose, so Learning is selected as Type even though the acquisition is resting-state.","decision_summary":"Top-2 candidates and selection:\n\nPathology:\n- Healthy (winner): Supported by “21 neurotypical undergraduate students”.\n- Unknown (runner-up): would apply only if recruitment health status were unclear, but it is explicit.\nFinal: Healthy. Evidence alignment: aligned.\n\nModality:\n- Resting State (winner): Supported by “two eyes-open blocks… two eyes-closed blocks…”.\n- Other (runner-up): only if a specific stimulus modality during recording were described; none is.\nFinal: Resting State. Evidence alignment: aligned.\n\nType:\n- Learning (winner): Supported by “neural correlates of learning processes” and “retrieval-practice questions with immediate feedback” plus pre/post design (“pre-intervention… post-intervention”), indicating learning-related change is the main purpose.\n- Resting-state (runner-up): Supported by the fact that EEG sessions are eyes-open/closed resting blocks.\nFinal: Learning, because the dataset is framed as an educational learning/retrieval-practice intervention study using resting-state EEG pre/post. Confidence is moderate due to the plausible Resting-state alternative."}},"computed_title":"Resting-state EEG before and after different study methods","nchans_counts":[{"val":31,"count":42}],"sfreq_counts":[{"val":500.0,"count":42}],"stats_computed_at":"2026-04-22T23:16:00.311960+00:00","total_duration_s":null,"author_year":"Alves2025","canonical_name":null}}