{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3468","dataset_id":"ds006803","associated_paper_doi":null,"authors":["Tania Yareni Pech-Canul","Roberto Guajardo","Luis Fernando Acosta-Soto","Mónica Sofía Margoya-Constantino","Juan Pablo Rosado-Aíza","Luz María Alonso-Valerdi"],"bids_version":"1.8.0","contact_info":["Juan Pablo Rosado-Aíza"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds006803.v1.1.1","datatypes":["eeg"],"demographics":{"subjects_count":63,"ages":[19,19,19,20,19,20,20,19,19,19,19,19,19,19,19,21,19,20,19,19,20,20,20,20,20,20,20,21,19,18,20,19,19,18,20,19,19,20,19,20,19,24,20,19,21,20,18,20,20,20,20,19,19,19,19,19,19,20,19,20,19,22,19],"age_min":18,"age_max":24,"age_mean":19.53968253968254,"species":null,"sex_distribution":{"m":26,"f":37},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds006803","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"1b96263f6d93b750b0253ea742cbdccc21f5b65d6ee514d1bcb5a8332880034d","license":"CC0","n_contributing_labs":null,"name":"NeuroTechs Dataset for Stem Skills","readme":"# README\n## Details related to access to the data]\n- [ ] Contact person\nJuan Pablo Rosado Aíza jprosadoa@gmail.com\nORCID 0009-0004-5690-1753\n- [ ] Practical information to access the data\nThe data units are in microvolts, transformed from raw Unicorn API for Python values.\n## Overview\nEvaluating STEM skills in students\n- [ ] Year(s) that the project ran\n2025 May - July\n- [ ] Brief overview of the tasks in the experiment\nParticipants answered a computer test through psychopy. The paradigm includes a 2 minute basal state (minute 1 with eyes closed, minute 2 with eyes open) and sections for each skill evaluated. 4 math sections, 1 per basic operation (sum, subtraction, multiplication and division), 1 programming section and 1 spatial ability section. The sections ran until either time or questions ran out. There was a 30 second break between sections.\nThe event markers with each question, answer and time can be found within each subject folder. The point of the paradigm is to compare different class groups and their global performance. The point of the EEG data is to image the brain for potential analysis of band activity to help explain differences in the groups. the experimental group took classes using interactive tools like Google Colab during class.\n- [ ] Description of the contents of the dataset\n8 Channel EEG data for 63 subjects, 23 experimental \"intervention\" subjects and 40 control subjects. You can find both raw (Session 1) and preprocessed (Session 2) data. All EEG data starts at second 3, since seconds (0-3) were cut in preprocessing. The timestamps in all event markers are in this time signature (Timestamp in second 3 corresponds to sample 1, second 4 is sample 251).\n- [ ] Independent variables\nGroups for the subjects.\n- [ ] Dependent variables\nPerformance, EEG data.\n- [ ] Control variables\nTime of participation (End of semester), place for data acquisition, status as student.\n## Methods\n### Subjects\nAll subjects are either experimental or control, whose ID is in the format XXc for control and XXe for experimental.\n[ ] Subject inclusion/exclusion criteria (if relevant)\nOnly students enrolled in the course at hand.\nParticipants 1e, 3e, 4e, 6e, 9e, 10e, 12e, 14e, 15e, 24e, 25e, 33e, 34e, 36e, 37e, 39e, 40e, 41e, 14c and 16c were outliers on RMS voltage.\n### Apparatus\nthe room was performed in a closed room with a single researcher there to give instructions and answer any questions. There was a laptop and the EEG device was mounted using conductive gel.\n### Initial setup\nSigning consent on paper was the first thing that was done, afterwards impedance measurements using UHB recorder software were made until all signals were \"good\" on the sofware.\nThe subjects then answered the test.\n### Task organization\nThe test's sections are not randomized nor counterbalanced, the order is as described above. The questions within each section were randomized.\n### Task details\nEach question answered has a code, an answer and a timestamp, which can be found in the corresponding main section file for each subject. The questions themselves with codes and correct answers can be found in the stimuli folder.\n### Additional data acquired\nAverage cycle data for female subjects was calculated for each group, anonymously. Refer to extra_metadata.xlsx.\n### Experimental location\nAll data collection was collected in a controlled environment.\n### Missing data\nSubject 17c, 30e, 32e and 35e where lost in the process of acquisition. All records start at second 3, instead of second 0, to eliminate connectivity noise and drift at the beginning. The basal state lasted 123 seconds to account for this, so the first 120 seconds correspond to the basal states.\nAll responses to \"OR4\" in the spatial ability sections are invalid, given that the correct answer is not among the options. It was excluded from all calculations shown in extra_metadata.xlsx.","recording_modality":["eeg"],"senior_author":"Luz María Alonso-Valerdi","sessions":["1","2"],"size_bytes":1497259364,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["STEMSKILLS"],"timestamps":{"digested_at":"2026-04-22T12:29:36.652280+00:00","dataset_created_at":"2025-10-16T23:52:02.995Z","dataset_modified_at":"2026-01-27T00:11:07.000Z"},"total_files":126,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006803","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"c0a72172934efdf2","model":"openai/gpt-5.2","tagged_at":"2026-01-20T19:09:08.014237+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Learning"],"confidence":{"pathology":0.8,"modality":0.7,"type":0.7},"reasoning":{"few_shot_analysis":"Closest few-shot conventions: (1) \"EEG: Probabilistic Learning with Affective Feedback\" is labeled Type=Learning for a task focused on performance differences/learning processes during a structured computer task. This guides mapping an educational/skills paradigm to Learning rather than e.g., Attention. (2) \"A Resting-state EEG Dataset for Sleep Deprivation\" shows that short eyes-open/closed baselines are labeled as Resting-state only when the dataset is primarily resting; here baseline is embedded within a broader task battery, so we should not label the whole dataset Resting-state.","metadata_analysis":"Population: participants are students in a course and are split into intervention vs control groups, not a clinical recruitment. Quotes: \"Evaluating STEM skills in students\" and \"Only students enrolled in the course at hand.\" Task/stimuli: computer-based PsychoPy test with math/programming/spatial sections and a baseline. Quotes: \"Participants answered a computer test through psychopy\" and \"The paradigm includes a 2 minute basal state (minute 1 with eyes closed, minute 2 with eyes open) and sections for each skill evaluated.\" Intervention framing: \"23 experimental \\\"intervention\\\" subjects and 40 control subjects\" and \"the experimental group took classes using interactive tools like Google Colab during class.\"","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says non-clinical student sample (\"Evaluating STEM skills in students\"; \"Only students enrolled in the course at hand.\"), with groups defined by educational intervention (\"23 experimental \\\"intervention\\\" subjects and 40 control subjects\"). Few-shot pattern suggests Healthy when no diagnosis is used for recruitment (aligns). Modality: Metadata says the task is a computer test via PsychoPy (\"Participants answered a computer test through psychopy\"), implying primarily visual presentation; few-shot conventions label screen-based tasks as Visual (aligns), despite having an eyes-open/closed baseline. Type: Metadata emphasizes skills/performance and an educational intervention comparison (\"compare different class groups and their global performance\"; \"experimental group took classes using interactive tools\"), which aligns better with Learning per few-shot conventions; few-shot pattern does not suggest Clinical/Intervention because there is no clinical population (aligns).","decision_summary":"Top-2 candidates per category.\n\nPathology: (A) Healthy vs (B) Unknown. Evidence for Healthy: \"Evaluating STEM skills in students\", \"Only students enrolled in the course at hand.\", and grouping is educational not diagnostic (\"23 experimental \\\"intervention\\\" subjects and 40 control subjects\"). Winner: Healthy. Alignment: aligned with few-shot convention that non-clinical cohorts are Healthy.\n\nModality: (A) Visual vs (B) Resting State. Evidence for Visual: \"computer test through psychopy\" and multiple cognitive sections (math/programming/spatial) delivered on a laptop. Evidence for Resting State: \"2 minute basal state (minute 1 with eyes closed, minute 2 with eyes open)\" but it is only a baseline. Winner: Visual. Alignment: aligned with few-shot style that embedded baselines do not dominate the dataset label when the main paradigm is task-based.\n\nType: (A) Learning vs (B) Attention/Other. Evidence for Learning: educational intervention framing (\"experimental \\\"intervention\\\" subjects\"; \"took classes using interactive tools\") and goal to relate EEG band activity to performance differences between class groups (\"compare different class groups and their global performance\"; \"help explain differences in the groups\"). Winner: Learning. Alignment: aligned with few-shot convention mapping educational/performance/skill paradigms to Learning rather than Resting-state or Clinical/Intervention."}},"computed_title":"NeuroTechs Dataset for Stem Skills","nchans_counts":[{"val":8,"count":126}],"sfreq_counts":[{"val":250.0,"count":126}],"stats_computed_at":"2026-04-22T23:16:00.311984+00:00","total_duration_s":165405.728,"author_year":"PechCanul2025","canonical_name":null}}