{"success":true,"database":"eegdash","data":{"_id":"69d16e04897a7725c66f4c89","dataset_id":"nm000169","associated_paper_doi":null,"authors":["Angela Riccio","Luca Simione","Francesca Schettini","Alessia Pizzimenti","Maurizio Inghilleri","Marta Olivetti Belardinelli","Donatella Mattia","Febo Cincotti"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":true,"dataset_doi":null,"datatypes":["eeg"],"demographics":{"subjects_count":8,"ages":[55,59,43,75,60,40,61,72],"age_min":40,"age_max":75,"age_mean":58.125,"species":null,"sex_distribution":{"m":5,"f":3},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000169","osf_url":null,"github_url":null,"paper_url":null},"funding":["Italian Agency for Research on ALS-ARiSLA project 'Brindisys'","FARI project C26I12AJZZ at the Sapienza University of Rome"],"ingestion_fingerprint":"e608bcb02e8b4f938759a6c213e434899779648311d15a02f5e43b2a59e7b80e","license":"CC-BY-NC-ND-4.0","n_contributing_labs":null,"name":"BNCI 2014-008 P300 dataset (ALS patients)","readme":"# BNCI 2014-008 P300 dataset (ALS patients)\nBNCI 2014-008 P300 dataset (ALS patients).\n## Dataset Overview\n- **Code**: BNCI2014-008\n- **Paradigm**: p300\n- **DOI**: 10.3389/fnhum.2013.00732\n- **Subjects**: 8\n- **Sessions per subject**: 1\n- **Events**: Target=2, NonTarget=1\n- **Trial interval**: [0, 1.0] s\n- **File format**: Unknown\n- **Data preprocessed**: True\n## Acquisition\n- **Sampling rate**: 256.0 Hz\n- **Number of channels**: 8\n- **Channel types**: eeg=8\n- **Channel names**: Fz, Cz, Pz, Oz, P3, P4, PO7, PO8\n- **Montage**: 10-10\n- **Hardware**: g.MOBILAB\n- **Software**: BCI2000\n- **Reference**: right earlobe\n- **Ground**: left mastoid\n- **Sensor type**: active electrodes\n- **Line frequency**: 50.0 Hz\n- **Online filters**: 0.1-10 Hz bandpass, 50 Hz notch\n- **Electrode type**: g.Ladybird\n- **Electrode material**: Ag/AgCl\n## Participants\n- **Number of subjects**: 8\n- **Health status**: ALS patients\n- **Clinical population**: amyotrophic lateral sclerosis\n- **Age**: mean=58.0, std=12.0, min=40, max=72\n- **Gender distribution**: M=5, F=3\n- **BCI experience**: naive\n- **Species**: human\n## Experimental Protocol\n- **Paradigm**: p300\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n- **Study design**: P300 speller with 6x6 matrix for copy-spelling task in ALS patients\n- **Feedback type**: visual\n- **Stimulus type**: row-column intensification\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: online\n- **Training/test split**: True\n- **Instructions**: Copy spell seven predefined words of five characters each by focusing attention on desired letters\n## HED Event Annotations\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n```\n  Target\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Target\n  NonTarget\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Non-target\n```\n## Paradigm-Specific Parameters\n- **Detected paradigm**: p300\n- **Number of targets**: 36\n- **Number of repetitions**: 10\n- **Inter-stimulus interval**: 125.0 ms\n- **Stimulus onset asynchrony**: 250.0 ms\n## Data Structure\n- **Trials**: 35\n- **Blocks per session**: 7\n- **Trials context**: per subject (7 words, 5 characters each)\n## Preprocessing\n- **Data state**: preprocessed\n- **Preprocessing applied**: True\n- **Steps**: bandpass filtering, notch filtering, artifact rejection, baseline correction\n- **Highpass filter**: 0.1 Hz\n- **Lowpass filter**: 10.0 Hz\n- **Bandpass filter**: {'low_cutoff_hz': 0.1, 'high_cutoff_hz': 10.0}\n- **Notch filter**: [50] Hz\n- **Filter type**: Butterworth\n- **Filter order**: 4\n- **Artifact methods**: amplitude threshold rejection\n- **Re-reference**: right earlobe\n- **Epoch window**: [0.0, 1.0]\n- **Notes**: Epochs with peak amplitude >70 μV or <-70 μV were rejected. Baseline correction based on 200 ms preceding each epoch.\n## Signal Processing\n- **Classifiers**: SWLDA\n- **Feature extraction**: temporal features, decimation\n## Cross-Validation\n- **Method**: 7-fold\n- **Folds**: 7\n- **Evaluation type**: within_subject\n## Performance (Original Study)\n- **Accuracy**: 97.5%\n- **Binary Accuracy Offline**: 87.4\n- **P300 Amplitude Mean Uv**: 3.3\n## BCI Application\n- **Applications**: communication\n- **Environment**: laboratory\n- **Online feedback**: True\n## Tags\n- **Pathology**: ALS\n- **Modality**: P300\n- **Type**: ERP\n## Documentation\n- **DOI**: 10.3389/fnhum.2013.00732\n- **License**: CC-BY-NC-ND-4.0\n- **Investigators**: Angela Riccio, Luca Simione, Francesca Schettini, Alessia Pizzimenti, Maurizio Inghilleri, Marta Olivetti Belardinelli, Donatella Mattia, Febo Cincotti\n- **Senior author**: Febo Cincotti\n- **Contact**: a.riccio@hsantalucia.it\n- **Institution**: Fondazione Santa Lucia\n- **Department**: Neuroelectrical Imaging and BCI Laboratory\n- **Address**: Via Ardeatina, 306, 00179 Rome, Italy\n- **Country**: Italy\n- **Repository**: BNCI Horizon\n- **Publication year**: 2013\n- **Funding**: Italian Agency for Research on ALS-ARiSLA project 'Brindisys'; FARI project C26I12AJZZ at the Sapienza University of Rome\n- **Ethics approval**: Fondazione Santa Lucia ethic committee\n- **Keywords**: brain computer interface, amyotrophic lateral sclerosis, P300, attention, working memory\n## References\nRiccio, A., Simione, L., Schettini, F., Pizzimenti, A., Inghilleri, M., Belardinelli, M. O., & Mattia, D. (2013). Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis. Frontiers in human neuroscience, 7, 732. https://doi.org/10.3389/fnhum.2013.00732\nNotes\n.. note::\n``BNCI2014_008`` was previously named ``BNCI2014008``. ``BNCI2014008`` will be removed in version 1.1.\n.. versionadded:: 0.4.0\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":["0"],"size_bytes":79622187,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000169","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:08:46.878936+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-02T21:18:35Z"},"total_files":8,"computed_title":"BNCI 2014-008 P300 dataset (ALS patients)","nchans_counts":[{"val":8,"count":8}],"sfreq_counts":[{"val":256.0,"count":8}],"stats_computed_at":"2026-05-01T13:49:34.645339+00:00","total_duration_s":10865.71875,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"34d75e9dbf47d609","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Other"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.8,"modality":0.9,"type":0.75},"reasoning":{"few_shot_analysis":"Most similar few-shot convention is the Cross-modal Oddball Task (Parkinson’s) example: an oddball/target-vs-nontarget ERP paradigm maps to an attention-oriented construct rather than pure sensory perception, and when a specific clinical recruitment is stated it drives Pathology labeling. Another helpful convention is the TBI DPX cognitive control example, where the label 'Attention' is used when the task emphasizes goal-directed cue/target processing. This dataset is a P300 speller (classic target detection/selection) with explicit ALS recruitment, so we follow the same style: (1) pathology from stated clinical population, (2) modality from stimulus channel (visual), (3) type reflecting attentional target selection demands typical of P300 spellers.","metadata_analysis":"Key recruitment/pathology facts: (1) title: \"BNCI 2014-008 P300 dataset (ALS patients)\"; (2) Participants section: \"Health status: ALS patients\" and \"Clinical population: amyotrophic lateral sclerosis\".\nKey task/modality facts: (1) \"Study design: P300 speller with 6x6 matrix for copy-spelling task\"; (2) \"Feedback type: visual\"; (3) \"Stimulus modalities: visual\" and \"Primary modality: visual\"; (4) Events are \"Target\" vs \"NonTarget\" with HED tags including \"Visual-presentation\".\nKey construct/type hints: (1) Instructions emphasize attentional selection: \"Copy spell ... by focusing attention on desired letters\"; (2) Keywords include \"attention, working memory\"; (3) Reference title: \"Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis.\"","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata SAYS participants are \"ALS patients\" and \"amyotrophic lateral sclerosis\"; few-shot pattern SUGGESTS using the explicitly named clinical recruitment as Pathology. ALIGNMENT: aligns. However, ALS is not an available Pathology label, so it must be mapped to the closest allowed category ('Other').\nModality: Metadata SAYS \"Stimulus modalities: visual\" / \"Primary modality: visual\" and HED includes \"Visual-presentation\"; few-shot pattern SUGGESTS labeling modality by stimulus channel (e.g., visual oddball/discrimination -> Visual). ALIGNMENT: aligns.\nType: Metadata SAYS it is a P300 speller with target vs nontarget events and instruction to \"focus attention\"; few-shot pattern SUGGESTS oddball/P300 target detection tasks map naturally to Attention (target selection/attentional orienting), unless the primary aim is clinical intervention. ALIGNMENT: mostly aligns; despite being a clinical cohort, the paradigm is a cognitive/BCI attention task rather than a treatment study, so 'Attention' fits better than 'Clinical/Intervention'.","decision_summary":"Top-2 candidates per category:\n- Pathology: (A) Other — supported by \"ALS patients\" and \"amyotrophic lateral sclerosis\" but ALS not in allowed list; (B) Unknown — would apply only if pathology not stated, but it is explicitly stated. Winner: Other. Evidence quotes: \"ALS patients\"; \"Clinical population: amyotrophic lateral sclerosis\"; title includes \"ALS patients\".\n- Modality: (A) Visual — supported by \"Stimulus modalities: visual\", \"Primary modality: visual\", and HED \"Visual-presentation\"; (B) Other — only if stimulus channel unclear, but it is explicit. Winner: Visual.\n- Type: (A) Attention — supported by \"Target, NonTarget\" P300 speller structure and instruction \"focusing attention on desired letters\" plus keywords \"attention\"; (B) Clinical/Intervention — plausible because cohort is ALS, but metadata emphasizes BCI performance/attention rather than intervention. Winner: Attention.\nConfidence basis: Pathology high because 3 explicit ALS mentions but mapped to 'Other' due to label set; Modality very high due to multiple explicit 'visual' statements; Type moderate-high because attention is explicitly mentioned and P300 speller convention supports it, though 'Clinical/Intervention' is a reasonable runner-up given the patient cohort."}},"canonical_name":null,"name_confidence":0.9,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Riccio2014"}}