{"dataset":{"id":"201","dataset_id":"nm000169","name":"BNCI 2014-008 P300 dataset (ALS patients)","description":"This dataset comprises EEG recordings from 8 patients with amyotrophic lateral sclerosis (ALS) performing a P300-based brain-computer interface (BCI) speller task. Participants completed a copy-spelling paradigm using a 6×6 matrix with row-column intensification, generating 35 trials per subject across a single session. The preprocessed data, acquired at 256 Hz from 8 electrodes using active g.Ladybird sensors, demonstrates high classification accuracy (97.5%) and represents a valuable resource for developing and benchmarking BCI systems for communication assistance in severely paralyzed populations.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000169","concept_doi":"10.82901/nemar.nm000169","latest_version_doi":"10.82901/nemar.nm000169.v1.0.2","created_at":"2026-03-23 13:13:26","updated_at":"2026-08-18 18:16:25","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"BNCI 2014-008 P300 dataset (ALS patients)\",\n  \"description\": \"This dataset comprises EEG recordings from 8 patients with amyotrophic lateral sclerosis (ALS) performing a P300-based brain-computer interface (BCI) speller task. Participants completed a copy-spelling paradigm using a 6×6 matrix with row-column intensification, generating 35 trials per subject across a single session. The preprocessed data, acquired at 256 Hz from 8 electrodes using active g.Ladybird sensors, demonstrates high classification accuracy (97.5%) and represents a valuable resource for developing and benchmarking BCI systems for communication assistance in severely paralyzed populations.\",\n  \"methods_description\": \"EEG data were acquired from 8 ALS patients using a g.MOBILAB system with 8 active electrodes (Fz, Cz, Pz, Oz, P3, P4, PO7, PO8) in a 10-10 montage, sampled at 256 Hz with reference to the right earlobe and ground at the left mastoid. Online filtering included 0.1-10 Hz bandpass and 50 Hz notch filters. Preprocessing involved Butterworth bandpass filtering (0.1-10 Hz, order 4), notch filtering at 50 Hz, amplitude-based artifact rejection (±70 μV threshold), and baseline correction using 200 ms pre-stimulus intervals. Participants performed a P300 speller task with visual row-column intensification feedback, spelling seven predefined five-character words with 10 repetitions per target.\",\n  \"license\": \"CC-BY-NC-ND-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Angela Riccio\": {},\n    \"Luca Simione\": {},\n    \"Francesca Schettini\": {},\n    \"Alessia Pizzimenti\": {},\n    \"Maurizio Inghilleri\": {},\n    \"Marta Olivetti Belardinelli\": {},\n    \"Donatella Mattia\": {},\n    \"Febo Cincotti\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D062207\"\n    },\n    {\n      \"term\": \"Amyotrophic Lateral Sclerosis\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D000690\"\n    },\n    {\n      \"term\": \"P300\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"event-related potential\"\n    },\n    {\n      \"term\": \"BCI speller\"\n    },\n    {\n      \"term\": \"attention\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.3389/fnhum.2013.00732\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000169\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000169\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Italian Agency for Research on ALS-ARiSLA project 'Brindisys'\"\n    },\n    {\n      \"funder_name\": \"Italian Agency for Research on ALS\",\n      \"award_title\": \"Brindisys\"\n    },\n    {\n      \"funder_name\": \"Sapienza University of Rome\",\n      \"award_number\": \"C26I12AJZZ\",\n      \"award_title\": \"FARI project\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"251.1 MB (20 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".html\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"8f37a65c1fdba6b773c1575e4c07be32276a13e357f0db35d2097f8f38192d46\"\n}","last_activity_at":"2026-08-16 13:27:16","source":null,"source_id":null,"subject_count":8,"modalities":"eeg","age_min":40,"age_max":75,"file_size":252199678,"total_files":110,"tasks":"p300","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Angela Riccio, Luca Simione, Francesca Schettini, Alessia Pizzimenti, Maurizio Inghilleri, Marta Olivetti Belardinelli, Donatella Mattia, Febo Cincotti","license":"CC-BY-NC-ND-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000169-blue)](https://doi.org/10.82901/nemar.nm000169)\n\n# BNCI 2014-008 P300 dataset (ALS patients)\n\nBNCI 2014-008 P300 dataset (ALS patients).\n\n## Dataset Overview\n\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\n## Acquisition\n\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\n## Participants\n\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\n## Experimental Protocol\n\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\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  Target\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Target\n\n  NonTarget\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Non-target\n\n```\n## Paradigm-Specific Parameters\n\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\n## Data Structure\n\n- **Trials**: 35\n- **Blocks per session**: 7\n- **Trials context**: per subject (7 words, 5 characters each)\n\n## Preprocessing\n\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\n## Signal Processing\n\n- **Classifiers**: SWLDA\n- **Feature extraction**: temporal features, decimation\n\n## Cross-Validation\n\n- **Method**: 7-fold\n- **Folds**: 7\n- **Evaluation type**: within_subject\n\n## Performance (Original Study)\n\n- **Accuracy**: 97.5%\n- **Binary Accuracy Offline**: 87.4\n- **P300 Amplitude Mean Uv**: 3.3\n\n## BCI Application\n\n- **Applications**: communication\n- **Environment**: laboratory\n- **Online feedback**: True\n\n## Tags\n\n- **Pathology**: ALS\n- **Modality**: P300\n- **Type**: ERP\n\n## Documentation\n\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\n## References\n\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\n\nNotes\n\n.. note::\n\n``BNCI2014_008`` was previously named ``BNCI2014008``. ``BNCI2014008`` will be removed in version 1.1.\n\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\n\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. 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