{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3357","dataset_id":"ds004511","associated_paper_doi":null,"authors":["Makowski, Dominique","Pham, Tam","Lau, Zen Juen"],"bids_version":"1.8.0","contact_info":["Anshu Te","Dominique Makowski"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004511.v1.0.2","datatypes":["eeg"],"demographics":{"subjects_count":45,"ages":[22,23,22,23,26,22,22,21,24,25,25,23,22,20,23,23,21,21,21,24,22,24,22,24,35,25,23,25,30,33,23,40,22,31,23,23,24,28,30,29,22,28,43,26],"age_min":20,"age_max":43,"age_mean":25.181818181818183,"species":null,"sex_distribution":{"f":21,"m":23},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004511","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"34aae2c6da5ef0108b8fb2045c2385f39a8ac29e21a24037c99a9b52f4982a99","license":"CC0","n_contributing_labs":null,"name":"Deception_data","readme":"# Overview\nThis dataset was collected in 2020 and comprises electroencephalography, physiological and behavioural data. The dataset includes both resting-state (eyes closed) and task-related neurophysiological signals acquired from 44 healthy individuals (ages: 21-40). The tasks administered to subjects include a spontaneous deception task (Gambling Game; GG) as well as a task assessing cognitive control (CC).\n# Task Description\n## Spontaneous Deception Task (GG)\nParticipants were informed that the GG task aimed to study a player's behaviour during a gambling game. They were given SGD 50 at the start of the game. They were to undergo 144 rounds of making a prediction about the outcome of a dice roll. They were to also place a bet ranging from 10 cents to 80 cents for each prediction; they win the bet if the prediction was true and lose it if it was false.\nParticipants were also informed that they were the only ones who knew the outcome of the dice roll and were responsible for reporting if their predictions were true to the system, and were debriefed at the end regarding this cover story.\n## Cognitive Control (CC)\nParticipants performed 60 trials of a simple processing speed task, 80 trials of a simple response selection task, 160 trials of a response inhibition task, and 160 trials of a conflict resolution task. See details of the task https://github.com/neuropsychology/CognitiveControl.\n# Data acquisition\n## EEG data acquisition\nEEG signals were recorded using the TruScan 128 Research EEG system and TruScan Aquisition software (DeyMed Diagnostics s.r.o). Electrodes were placed on the EEG cap according to the standard 10-5 system of electrode placement (Oostenveld & Praamsrta, 2001) and impedance was kept below 20 kOhm for each subject. The ground electrode was placed on the zygomatic bone and two electrodes were fixed on the mastoids to be used as references. During recording, the sampling rate was 3000Hz. Note that channels 124 and 125 were placed above and below the eyes respectively for vertical EOG signals.\n### Note\nsub-S200203 does not have any EEG acquisition file pertaining to the Gambling Game task due to technical errors during the recording.\n## Physiological data acquisition\nParticipants' physiological signals, that is their electrocardiogram (*ECG*), respiration signals (*RSP*), electrodermal activity (*EDA*) and electromyography (*EMG*), were obtained at a sampling frequency of 4000Hz. All physiological signals were recorded via the BioPac MP160 system (BioPac Systems Inc., USA) and the AcqKnowledge 5.0 software.\nECG was collected using three ECG electrodes placed according to a modified Lead II configuration, and RSP was acquired using a respiration belt tightened over participants' upper abdomen. EDA, a measure of skin conductance, was acquired using electrodes placed on the middle and index fingers of subjects' non-dominant hands and EMG was obtained by measuring the electrical activity of the corrugator muscles.\n### Note\nWith regards to the Cognitive Control task, physiological data was collected over 2 sessions for sub-S200303 as a result of technical errors during the recording.","recording_modality":["eeg"],"senior_author":"Lau, Zen Juen","sessions":["01"],"size_bytes":217194708760,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["CC","GG","Rest"],"timestamps":{"digested_at":"2026-04-22T12:26:41.215195+00:00","dataset_created_at":"2023-02-22T14:36:22.532Z","dataset_modified_at":"2023-06-05T23:55:41.000Z"},"total_files":134,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004511","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"3bc96f492b695d7e","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:41:09.617542+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Decision-making"],"confidence":{"pathology":0.9,"modality":0.7,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot conventions are gambling/choice tasks. The few-shot example \"EEG: Three armed bandit gambling task\" shows that a gambling paradigm is typically labeled with Visual modality (stimuli on screen) and a higher-level purpose label related to value/choice (they used Affect because their stated goal was reward processing). The few-shot example \"EEG: Reinforcement Learning in Parkinson's\" maps a choice+feedback paradigm to Type=Decision-making. These examples guide mapping this dataset’s gambling/deception paradigm to Decision-making (rather than Motor) and suggest Visual as the most likely stimulus modality for computerized tasks.","metadata_analysis":"Key explicit facts from the README: (1) population: \"acquired from 44 healthy individuals (ages: 21-40)\" and \"includes both resting-state (eyes closed)\". (2) tasks: \"The tasks administered to subjects include a spontaneous deception task (Gambling Game; GG)\" and \"a task assessing cognitive control (CC).\" (3) GG paradigm implies choice/value reporting: \"144 rounds of making a prediction about the outcome of a dice roll\" and \"place a bet ranging from 10 cents to 80 cents\" and \"responsible for reporting if their predictions were true to the system.\" (4) CC paradigm is explicitly executive control: \"task assessing cognitive control (CC)\" and includes \"response inhibition\" and \"conflict resolution\" tasks.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says participants are \"44 healthy individuals\" (explicit). Few-shot pattern would label such normative cohorts as Healthy. ALIGN.\nModality: Metadata does not explicitly state the sensory modality (no direct mention of visual/auditory stimuli), but tasks are standard computerized gambling/cognitive-control paradigms (and linked to an online task repo) implying on-screen prompts. Few-shot gambling/choice examples label these as Visual. ALIGN (inference-based).\nType: Metadata says there is a \"spontaneous deception task (Gambling Game; GG)\" involving betting and reporting outcomes, plus a \"task assessing cognitive control (CC)\" with inhibition/conflict trials. Few-shot gambling/choice examples map to Decision-making (or sometimes Affect when explicitly framed as reward/affect processing). Partially ALIGN: both suggest a higher-level choice/value construct; however CC could also support Attention as runner-up. No conflict with any explicit pathology facts.","decision_summary":"Pathology top-2: (1) Healthy — supported by \"44 healthy individuals\" and no clinical recruitment stated; (2) Unknown — only if health status were unclear (it is not). Selected Healthy. Confidence=0.9 based on multiple explicit population statements (\"44 healthy individuals\", age range, and general overview of healthy cohort).\nModality top-2: (1) Visual — implied computerized gambling and cognitive control tasks; aligned with few-shot gambling/choice datasets labeled Visual; (2) Unknown — because README never explicitly says 'visual'/'screen'. Selected Visual. Confidence=0.7 (inference from task nature + few-shot convention, but lacking explicit modality quote).\nType top-2: (1) Decision-making — GG is a betting/prediction task and deception/choice reporting (\"place a bet\", \"making a prediction\", \"responsible for reporting\"), matching few-shot choice paradigms mapped to Decision-making; (2) Attention — CC includes inhibition/conflict tasks (executive control). Selected Decision-making because gambling/deception is a central named task and is inherently choice/value-based. Confidence=0.7 (clear task description but mixed aims across GG and CC)."}},"nemar_citation_count":2,"computed_title":"Deception_data","nchans_counts":[{"val":139,"count":134}],"sfreq_counts":[{"val":3000.0,"count":134}],"stats_computed_at":"2026-04-22T23:16:00.307750+00:00","total_duration_s":230908.67233333332,"author_year":"Makowski2023_Deception","canonical_name":null}}