{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4ccc","dataset_id":"nm000268","associated_paper_doi":"10.1109/tnsre.2016.2628057","authors":["Jaeyoung Shin","Alexander von Lühmann","Benjamin Blankertz","Do-Won Kim","Jichai Jeong","Han-Jeong Hwang","Klaus-Robert Müller"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.1109/TNSRE.2016.2628057","datatypes":["eeg"],"demographics":{"subjects_count":29,"ages":[28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28],"age_min":28,"age_max":28,"age_mean":28.0,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000268","osf_url":null,"github_url":null,"paper_url":null},"funding":["Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A6A3A03057524)","Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A02037032)","Brain Korea 21 PLUS Program through the NRF funded by the Ministry of Education","Korea University Grant","BMBF (#01GQ0850, Bernstein Focus: Neurotechnology)"],"ingestion_fingerprint":"9872a4d14a4b18c13c8b5c3463bdec77287d341eea30ce84dd5a348fc81f975b","license":"GPL-3.0","n_contributing_labs":null,"name":"Shin et al. 2017 (Experiment B) — Open Access Dataset for EEG+NIRS Single-Trial Classification","readme":"Shin2017B\n=========\nMental Arithmetic Dataset from Shin et al 2017.\nDataset Overview\n----------------\n  Code: Shin2017B\n  Paradigm: imagery\n  DOI: 10.1109/TNSRE.2016.2628057\n  Subjects: 29\n  Sessions per subject: 6\n  Events: left_hand=1, right_hand=2, subtraction=3, rest=4\n  Trial interval: [0, 10] s\n  Session IDs: 1arithmetic, 3arithmetic, 5arithmetic\n  File format: MATLAB\n  Data preprocessed: True\nAcquisition\n-----------\n  Sampling rate: 200.0 Hz\n  Number of channels: 30\n  Channel types: eeg=30, eog=2\n  Channel names: AFF1h, AFF2h, AFF5h, AFF6h, AFp1, AFp2, CCP3h, CCP4h, CCP5h, CCP6h, Cz, F3, F4, F7, F8, FCC3h, FCC4h, FCC5h, FCC6h, HEOG, P3, P4, P7, P8, POO1, POO2, PPO1h, PPO2h, Pz, T7, T8, VEOG\n  Montage: 10-5\n  Hardware: BrainAmp\n  Software: MATLAB R2013b\n  Reference: linked mastoids\n  Ground: Fz\n  Sensor type: active electrodes\n  Line frequency: 50.0 Hz\n  Cap manufacturer: EASYCAP GmbH\n  Cap model: custom-made stretchy fabric cap\n  Auxiliary channels: EOG (4 ch, horizontal, vertical), ecg, respiration\nParticipants\n------------\n  Number of subjects: 29\n  Health status: healthy\n  Age: mean=28.5, std=3.7\n  Gender distribution: male=14, female=15\n  Handedness: {'right': 29, 'left': 1}\n  BCI experience: naive to MI experiment\n  Species: human\nExperimental Protocol\n---------------------\n  Paradigm: imagery\n  Number of classes: 2\n  Class labels: subtraction, rest\n  Trial duration: 10.0 s\n  Trials per class: subtraction=30, rest=30\n  Study design: Dataset B: mental arithmetic (serial subtraction of one-digit number) versus baseline/rest task\n  Feedback type: none\n  Stimulus type: visual instruction (subtraction problem and fixation cross)\n  Stimulus modalities: visual, auditory\n  Primary modality: visual\n  Synchronicity: cued-synchronous\n  Mode: offline\n  Training/test split: False\n  Instructions: For the MA task, subjects memorized an initial subtraction (three-digit minus one-digit) displayed for 2s, then repeatedly subtracted the one-digit number from each result. For baseline, subjects rested with no specific thought.\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n  left_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Left, Hand\n  right_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\n  subtraction\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Think\n          └─ Label/subtraction\n  rest\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Rest\nParadigm-Specific Parameters\n----------------------------\n  Detected paradigm: motor_imagery\n  Number of repetitions: 20\nData Structure\n--------------\n  Trials: {'per_session': 20, 'per_condition_session': 10, 'per_condition_total': 30}\n  Trials context: Each session: 1 min pre-experiment rest + 20 trials + 1 min post-experiment rest. Trial: 2s visual instruction + 10s task + 15-17s random rest\nPreprocessing\n-------------\n  Data state: preprocessed\n  Preprocessing applied: True\n  Steps: common average reference, bandpass filtering (0.5-50 Hz), ICA-based EOG rejection, downsampling to 200 Hz\n  Highpass filter: 0.5 Hz\n  Lowpass filter: 50.0 Hz\n  Bandpass filter: [0.5, 50.0]\n  Filter type: Chebyshev type II\n  Filter order: 4\n  Artifact methods: EOG correction, ICA\n  Re-reference: car\n  Downsampled to: 200.0 Hz\nSignal Processing\n-----------------\n  Classifiers: LDA, Shrinkage LDA\n  Feature extraction: CSP, log-variance\n  Frequency bands: analyzed=[4.0, 35.0] Hz\n  Spatial filters: CSP\nCross-Validation\n----------------\n  Method: 10x5-fold\n  Folds: 5\n  Evaluation type: within_subject\nPerformance (Original Study)\n----------------------------\n  Ma Eeg Max Accuracy: 75.9\n  Ma Hbr Max Accuracy: 80.7\n  Ma Hbo Max Accuracy: 83.6\nBCI Application\n---------------\n  Applications: hybrid_bci_research\n  Environment: laboratory\n  Online feedback: False\nTags\n----\n  Pathology: Healthy\n  Modality: Cognitive\n  Type: Cognitive\nDocumentation\n-------------\n  Description: Open access dataset for hybrid brain-computer interfaces using EEG and NIRS with motor imagery and mental arithmetic tasks\n  DOI: 10.1109/TNSRE.2016.2628057\n  License: GPL-3.0\n  Investigators: Jaeyoung Shin, Alexander von Lühmann, Benjamin Blankertz, Do-Won Kim, Jichai Jeong, Han-Jeong Hwang, Klaus-Robert Müller\n  Senior author: Klaus-Robert Müller\n  Contact: h2j@kumoh.ac.kr; klaus-robert.mueller@tuberlin.de\n  Institution: Berlin Institute of Technology\n  Department: Department of Computer Science, Machine Learning Group\n  Address: 10587 Berlin, Germany\n  Country: DE\n  Repository: GitHub\n  Data URL: http://doc.ml.tu-berlin.de/hBCI\n  Publication year: 2017\n  Funding: Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A6A3A03057524); Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A02037032); Brain Korea 21 PLUS Program through the NRF funded by the Ministry of Education; Korea University Grant; BMBF (#01GQ0850, Bernstein Focus: Neurotechnology)\n  Ethics approval: Ethics Committee of the Institute of Psychology and Ergonomics, Technical University of Berlin (approval number: SH_01_20150330)\n  Keywords: Brain-computer interface, BCI, electroencephalography, EEG, hybrid BCI, mental arithmetic, motor imagery, near-infrared spectroscopy, NIRS, open access dataset\nAbstract\n--------\nOpen access dataset for hybrid brain-computer interfaces using EEG and NIRS. Includes two experiments: (1) left vs right hand motor imagery, (2) mental arithmetic vs resting state. Dataset validated using baseline signal analysis showing hybrid approach enhances discrimination of mental states. Also includes motion artifacts and physiological data for wide range of validation approaches.\nMethodology\n-----------\nThirty subjects performed 6 sessions alternating between motor imagery (dataset A: left/right hand) and mental arithmetic (dataset B: MA vs rest). Each session: 20 trials with 2s cue, 10s task, 15-17s rest. EEG recorded at 1000 Hz with 30 channels, downsampled to 200 Hz. Preprocessing: CAR, 0.5-50 Hz bandpass (4th order Chebyshev II), ICA-based EOG rejection. Feature extraction: CSP with log-variance of first/last 3 components using 3s moving window (1s step). Classification: shrinkage LDA with 10x5-fold CV. Hybrid analysis combines EEG and NIRS outputs using meta-classifier.\nReferences\n----------\nShin, J., von Lühmann, A., Blankertz, B., Kim, D.W., Jeong, J., Hwang, H.J. and Müller, K.R., 2017. Open access dataset for EEG+NIRS single-trial classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10), pp.1735-1745.\nGNU General Public License, Version 3 `<https://www.gnu.org/licenses/gpl-3.0.txt>`_\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8\n---\nGenerated by MOABB 1.5.0 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb","recording_modality":["eeg"],"senior_author":null,"sessions":["0imagery","1arithmetic","2imagery","3arithmetic","4imagery","5arithmetic"],"size_bytes":2014251525,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000268","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["imagery"],"timestamps":{"digested_at":"2026-04-30T14:09:48.174377+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-11T21:07:37Z"},"total_files":174,"computed_title":"Shin et al. 2017 (Experiment B) — Open Access Dataset for EEG+NIRS Single-Trial Classification","nchans_counts":[{"val":32,"count":174}],"sfreq_counts":[{"val":200.0,"count":174}],"stats_computed_at":"2026-05-01T13:49:34.659477+00:00","total_duration_s":104520.13,"tagger_meta":{"model":"gpt-6-sol","taxonomy":"v2","config_hash":"39931253008fe2df","tagged_at":"2026-10-06T13:13:11Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Other"],"confidence":{"pathology":0.9,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"The EEG Motor Movement/Imagery example shows that visually cued imagery is labeled Visual and Motor when the studied task is hand movement imagery. Here, however, the metadata identifies this as Experiment B, whose task is mental arithmetic rather than the hand-imagery task of Experiment A. The resting-state examples also distinguish a rest condition from a study primarily about resting-state activity.","metadata_analysis":"The readme states \"Health status: healthy\" and \"Number of subjects: 29.\" For Experiment B it specifies \"Class labels: subtraction, rest\" and \"Dataset B: mental arithmetic (serial subtraction of one-digit number) versus baseline/rest task.\" Stimuli are described as \"visual instruction (subtraction problem and fixation cross),\" with the subtraction displayed for 2 seconds. A generic field also says \"Stimulus modalities: visual, auditory,\" but does not describe an auditory stimulus.","paper_abstract_analysis":"The abstract distinguishes the two experiments as \"left vs. right hand motor imagery; mental arithmetic vs. resting state.\" This confirms that motor imagery belongs to Experiment A, not the Experiment B dataset being classified.","evidence_alignment_check":"Pathology: Metadata says \"Health status: healthy\"; the healthy-participant few-shot examples suggest Healthy. ALIGN. Modality: Metadata describes \"visual instruction (subtraction problem and fixation cross)\"; the visually cued imagery example suggests Visual. PARTIAL CONFLICT with the generic \"Stimulus modalities: visual, auditory\" field; the specific protocol and \"Primary modality: visual\" favor Visual because no auditory stimulus is identified. Type: Metadata says \"Dataset B: mental arithmetic ... versus baseline/rest task\"; the motor-imagery example would suggest Motor only if this were the hand-imagery experiment. CONFLICT with the generic \"Detected paradigm: motor_imagery\" field; the Experiment B protocol and abstract take precedence. Arithmetic is the studied task, while rest is its comparison condition.","decision_summary":"Pathology candidates: Healthy, supported by \"Health status: healthy\" and \"Subjects: 29,\" versus Unknown, with no supporting evidence; select Healthy (0.9, also supported by the absence of a recruited clinical condition and a clear healthy-cohort few-shot convention). Modality candidates: Visual, supported by \"visual instruction (subtraction problem and fixation cross),\" \"Primary modality: visual,\" and visual-presentation event annotations, versus Multisensory, supported only by the generic \"Stimulus modalities: visual, auditory\" field; select Visual (0.8). Type candidates: Other, supported by \"Class labels: subtraction, rest,\" \"Dataset B: mental arithmetic ... versus baseline/rest task,\" and the abstract's Experiment B description, versus Resting-state, supported by the baseline/rest comparison condition; select Other (0.8) because no allowed type specifically denotes mental arithmetic. No explicit clinical-population fact or specific task fact was overridden by a few-shot pattern."}},"canonical_name":null,"name_confidence":0.84,"name_meta":{"suggested_at":"2026-04-14T10:18:35.344Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Shin2017_Shin2017B","associated_paper_meta":{"channel":"text/readme","confidence":"high","author_overlap":7,"is_oa":false,"oa_status":"closed","source":"paper_resolver"}}}