{"success":true,"database":"eegdash","data":{"_id":"69de3cac897a7725c66ff16c","dataset_id":"nm000253","associated_paper_doi":null,"authors":["Christopher Wang","Adam Yaari","Aaditya K Singh","Vighnesh Subramaniam","Dana Rosenfarb","Jan DeWitt","Pranav Misra","Joseph R Madsen","Scellig Stone","Gabriel Kreiman","Boris Katz","Ignacio Cases","Andrei Barbu"],"bids_version":"1.9.0","canonical_name":null,"contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"10.82901/nemar.nm000253","datatypes":["ieeg"],"demographics":{"subjects_count":10,"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://nemar.org/dataexplorer/detail/nm000253","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"42c6b2074646d18fc8f33c72d71872ab071e4b457800f5f55159cf8c667f81e0","license":"CC BY 4.0","n_contributing_labs":null,"name":"Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000253-blue)](https://doi.org/10.82901/nemar.nm000253)\n# Brain Treebank: large-scale intracranial (iEEG) recordings from naturalistic language stimuli\n## Summary\nThe Brain Treebank is a large-scale dataset of intracranial electrophysiological (iEEG /\nstereo-EEG) recordings collected from **10 epilepsy patients** (at Boston Children's\nHospital) while they watched Hollywood movies. Recordings were acquired at **2048 Hz**\nfrom on average **168 electrodes per subject (1,688 electrodes total)**, totalling\nroughly **43 hours across 26 trials** (one trial = one movie). The audio of each movie\nwas transcribed and word onsets manually annotated, and each transcript was parsed into\nUniversal Dependencies (UD) syntax trees — making this one of the largest datasets of\nintracranial recordings grounded in naturalistic language.\nThis NEMAR record provides the dataset converted to **BIDS-iEEG** (BrainVision) format:\neach trial is one BIDS run (`task-movie`, `run-01` …), with signals in `sub-*/ieeg/`.\n## Modality and paradigm\n- **Modality:** Intracranial EEG / stereo-EEG (iEEG-BIDS), 2048 Hz, BrainVision\n  (IEEE_FLOAT_32, multiplexed)\n- **Task / paradigm:** Passive naturalistic viewing of Hollywood movies (`task-movie`),\n  with time-aligned word-level language annotations (transcripts and UD syntax trees in\n  `code/transcripts.zip` and `code/trees.zip`)\n- **Population:** 10 epilepsy patients undergoing intracranial monitoring\n## Participants and data structure\n- **10 subjects** (`sub-01` … `sub-10`), 26 movie-viewing runs in total.\n- Electrode labels and per-subject electrode information are in each `sub-*/ieeg/`\n  (`electrodes.tsv`, `coordsystem.json`); corrupted electrodes are flagged as `bad` in\n  `channels.tsv` (from the Brain Treebank `corrupted_elec.json`).\n- Electrode coordinates are provided as-is from the Brain Treebank `localization.zip`\n  (`code/localization.zip`); see each `coordsystem.json` for important caveats about\n  units and the absence of a published transform to a standard template.\n## Original dataset / data paper\nPlease cite the original publication when using this dataset:\n> Wang, C., Yaari, A. U., Singh, A. K., Subramaniam, V., Rosenfarb, D., DeWitt, J.,\n> Misra, P., Madsen, J. R., Stone, S., Kreiman, G., Katz, B., Cases, I., & Barbu, A.\n> (2024). *Brain Treebank: Large-scale intracranial recordings from naturalistic language\n> stimuli.* Advances in Neural Information Processing Systems 37 (NeurIPS 2024, Datasets\n> and Benchmarks Track). arXiv:2411.08343.\n- **Preprint / DOI:** [arXiv:2411.08343](https://doi.org/10.48550/arXiv.2411.08343)\n- **NeurIPS 2024 proceedings:** https://proceedings.neurips.cc/paper_files/paper/2024/hash/aefa2385b3f33abf1526ae4e2c208cd9-Abstract-Datasets_and_Benchmarks_Track.html\n- **Project site / source data:** https://braintreebank.dev/\n- **Original code release:** https://github.com/czlwang/brain_treebank_code_release\n- **Ethics:** Boston Children's Hospital / Harvard IRB; all subjects gave informed consent.\n## BIDS conversion\nPer-trial HDF5 recordings from braintreebank.dev were converted to BIDS-iEEG (BrainVision)\nwith the EEGDash conversion script in `code/convert_braintreebank.py`. Each trial (one\nmovie) becomes one BIDS run. EEG-BIDS / MNE-BIDS were used only for standardisation; the\ndata themselves are from the original Brain Treebank release. Please credit the original\ncreators (Wang et al.) and cite the paper above.\n## License\nCC BY 4.0 (see `dataset_description.json`).","recording_modality":["ieeg"],"senior_author":"Andrei Barbu","sessions":[],"size_bytes":276286984484,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000253","raw_key":"dataset_description.json","dep_keys":["README.md","electrodes.json","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["movie"],"timestamps":{"digested_at":"2026-08-26T11:04:06.374830+00:00","dataset_created_at":null,"dataset_modified_at":"2026-06-08 03:32:35"},"total_files":26,"computed_title":"Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli","nchans_counts":[{"val":164,"count":8},{"val":136,"count":3},{"val":190,"count":3},{"val":166,"count":3},{"val":156,"count":3},{"val":218,"count":2},{"val":248,"count":2},{"val":108,"count":1},{"val":158,"count":1}],"sfreq_counts":[{"val":2048.0,"count":26}],"stats_computed_at":"2026-08-27T07:51:42.858196+00:00","total_duration_s":6535.1552734375,"author_year":"Wang2024_et_al_Brain","name_source":"canonical","associated_paper_meta":{"channel":"nemar/IsDerivedFrom","confidence":"high","author_overlap":13,"is_oa":true,"oa_status":"preprint","source":"paper_resolver"},"ethics_approvals":["Boston Children's Hospital / Harvard IRB (all subjects gave informed consent)"],"generated_by":[{"Name":"convert_braintreebank.py (EEGDash)","Description":"Converted per-trial HDF5 files (raw 2048 Hz iEEG) to BIDS-iEEG BrainVision format. Each trial (one movie) becomes one BIDS run. Electrode labels from the Brain Treebank metadata (electrode_labels/sub_X/). Corrupted electrodes flagged from corrupted_elec.json.","CodeURL":"https://github.com/bruaristimunha/EEGDash"}],"how_to_acknowledge":"Please cite: Wang, C., Yaari, A. U., Singh, A. K., Subramaniam, V., Rosenfarb, D., DeWitt, J., Misra, P., Madsen, J. R., Stone, S., Kreiman, G., Katz, B., Cases, I., & Barbu, A. (2024). Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli. Advances in Neural Information Processing Systems 37 (NeurIPS 2024, Datasets and Benchmarks Track). arXiv:2411.08343. https://braintreebank.dev/","references_and_links":["https://arxiv.org/abs/2411.08343","https://proceedings.neurips.cc/paper_files/paper/2024/hash/aefa2385b3f33abf1526ae4e2c208cd9-Abstract-Datasets_and_Benchmarks_Track.html","https://braintreebank.dev/","https://github.com/czlwang/brain_treebank_code_release"],"source_datasets":[{"URL":"https://braintreebank.dev/"},{"DOI":"doi:10.48550/arXiv.2411.08343"}],"bad_channels_info":null}}