{"dataset":{"id":"282","dataset_id":"nm000249","name":"BNCI 2022-001 EEG Correlates of Difficulty Level dataset","description":"This dataset comprises EEG recordings from 13 healthy subjects performing a visuomotor learning task involving simulated drone piloting through waypoints of varying difficulty levels. The study investigates real-time decoding of subjective difficulty from EEG signals to enable adaptive closed-loop learning, comparing algorithmic difficulty adjustment with subject-controlled progression. Data include 1 offline session and 2 online sessions (online_session_2, online_session_3) with preprocessed EEG recordings (64 channels + 3 EOG, 25 central EEG channels retained after preprocessing) sampled at 256 Hz, along with behavioral markers of task performance including waypoint hits/misses and trajectory events.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000249","concept_doi":"10.82901/nemar.nm000249","latest_version_doi":"10.82901/nemar.nm000249.v1.0.2","created_at":"2026-03-26 07:29:27","updated_at":"2026-08-18 21:22:53","zenodo_concept_id":"20521965","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"BNCI 2022-001 EEG Correlates of Difficulty Level dataset\",\n  \"description\": \"This dataset comprises EEG recordings from 13 healthy subjects performing a visuomotor learning task involving simulated drone piloting through waypoints of varying difficulty levels. The study investigates real-time decoding of subjective difficulty from EEG signals to enable adaptive closed-loop learning, comparing algorithmic difficulty adjustment with subject-controlled progression. Data include 1 offline session and 2 online sessions (online_session_2, online_session_3) with preprocessed EEG recordings (64 channels + 3 EOG, 25 central EEG channels retained after preprocessing) sampled at 256 Hz, along with behavioral markers of task performance including waypoint hits/misses and trajectory events.\",\n  \"methods_description\": \"EEG data were acquired using a Biosemi ActiveTwo system with 64 EEG channels and 3 EOG channels at 256 Hz sampling rate in a 10-10 montage. Subjects performed a visuomotor task controlling simulated drone flight via joystick across three sessions (1 offline, 2 online) with varying difficulty levels. Preprocessing included downsampling from 2048 Hz, bandpass filtering (1-40 Hz), SPHARA 20th order spatial filtering, common-average re-referencing, ICA-based artifact removal, peripheral electrode removal (retaining 25 central channels), and amplitude thresholding (>50 µV rejection). Classification employed LDA and elastic net regularization on power spectral density features with leave-one-pair-out cross-validation.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Ping-Keng Jao\": {\n      \"orcid\": \"0000-0003-1715-8472\"\n    },\n    \"Ricardo Chavarriaga\": {\n      \"orcid\": \"0000-0002-8879-2860\"\n    },\n    \"Jose del R. Millan\": {\n      \"orcid\": \"0000-0001-5819-1522\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"visuomotor learning\"\n    },\n    {\n      \"term\": \"real-time decoding\"\n    },\n    {\n      \"term\": \"difficulty decoding\"\n    },\n    {\n      \"term\": \"closed-loop adaptation\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D062207\"\n    },\n    {\n      \"term\": \"workload assessment\"\n    },\n    {\n      \"term\": \"simulated flying\"\n    },\n    {\n      \"term\": \"challenge point\"\n    },\n    {\n      \"term\": \"brain-machine interface\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1109/THMS.2020.3038339\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000249\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1109/TAFFC.2021.3059688\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000249\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Swiss National Centres of Competence in Research (NCCR) Robotics\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"10.1 GB (27 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"bb647de53b61fda8fc04180e540e32002fd71069f826cbeab080f27f89b05ef6\"\n}","last_activity_at":"2026-08-16 13:36:06","source":null,"source_id":null,"subject_count":13,"modalities":"eeg","age_min":22.6,"age_max":22.6,"file_size":10115654613,"total_files":167,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Ping-Keng Jao, Ricardo Chavarriaga, Jose del R. Millan","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000249-blue)](https://doi.org/10.82901/nemar.nm000249)\n\n# BNCI 2022-001 EEG Correlates of Difficulty Level dataset\n\nBNCI 2022-001 EEG Correlates of Difficulty Level dataset.\n\n## Dataset Overview\n\n- **Code**: BNCI2022-001\n- **Paradigm**: imagery\n- **DOI**: 10.1109/THMS.2020.3038339\n- **Subjects**: 13\n- **Sessions per subject**: 1\n- **Events**: trajectory_start=1, waypoint_miss=16, waypoint_hit=48, trajectory_end=255\n- **Trial interval**: [0, 90] s\n- **Session IDs**: offline, online_session_2, online_session_3\n- **File format**: gdf\n- **Data preprocessed**: True\n\n## Acquisition\n\n- **Sampling rate**: 256.0 Hz\n- **Number of channels**: 64\n- **Channel types**: eeg=64, eog=3\n- **Channel names**: AF3, AF4, AF7, AF8, AFz, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, EOG1, EOG2, EOG3, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT7, FT8, Fp1, Fp2, Fpz, Fz, Iz, O1, O2, Oz, P1, P10, P2, P3, P4, P5, P6, P7, P8, P9, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP7, TP8\n- **Montage**: 10-10\n- **Hardware**: Biosemi ActiveTwo\n- **Software**: EEGLAB\n- **Reference**: car\n- **Sensor type**: active\n- **Line frequency**: 50.0 Hz\n- **Auxiliary channels**: EOG (3 ch, horizontal, vertical), ppg\n\n## Participants\n\n- **Number of subjects**: 13\n- **Health status**: patients\n- **Clinical population**: normal or corrected-to-normal vision, no history of motor or neurological disease (one subject with history of vasovagal syncope)\n- **Age**: mean=22.6, std=1.04\n- **Gender distribution**: female=8, male=5\n- **Handedness**: {'right': 12, 'left': 1}\n\n## Experimental Protocol\n\n- **Paradigm**: imagery\n- **Number of classes**: 4\n- **Class labels**: trajectory_start, waypoint_miss, waypoint_hit, trajectory_end\n- **Trial duration**: 90.0 s\n- **Study design**: Subjects piloted a simulated drone through circular waypoints using a flight joystick, controlling roll and pitch while the drone maintained constant velocity. In offline session: 32 trajectories each with constant difficulty level (v-shape design from level 16 to 1 and back to 16), each trajectory had 32 waypoints and lasted ~90 seconds. In online sessions: each condition consisted of 12 trajectories with 33 waypoints and 8 decision points per trajectory.\n- **Feedback type**: visual\n- **Stimulus type**: visual\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: cue-based\n- **Mode**: both\n- **Instructions**: Subjects piloted a simulated drone through a series of circular waypoints. Subjects controlled the roll and pitch while the drone had a constant velocity of 11.8 arbitrary units per second when flying straight. They were instructed to press a button when the current level was easy as a way to collect ground truth for decoding or to proceed with self-paced learning.\n- **Stimulus presentation**: screen_size=twenty-inch screen, screen_resolution=1680x1050, input_device=Logitech Extreme 3D Pro joystick, waypoint_colors=green (current), blue (next), yellow (decision point), waypoint_distance_pitch=32 A.U. (at least 2.7 seconds), waypoint_distance_roll=24 A.U. (at least 2.0 seconds)\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  trajectory_start\n    ├─ Experiment-structure\n    └─ Label/trajectory_start\n\n  waypoint_miss\n    ├─ Experiment-structure\n    └─ Label/waypoint_miss\n\n  waypoint_hit\n    ├─ Experiment-structure\n    └─ Label/waypoint_hit\n\n  trajectory_end\n    ├─ Experiment-structure\n    └─ Label/trajectory_end\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: right_hand, left_hand, feet\n\n## Data Structure\n\n- **Trials**: {'offline_session': '32 trajectories of 32 waypoints each (~90 seconds per trajectory)', 'online_session_per_condition': '12 trajectories of 33 waypoints each with 8 decision points'}\n- **Blocks per session**: 2\n- **Trials context**: Offline session: v-shape difficulty design (level 16→1→16). Online sessions: each condition had 12 trajectories, starting at level 1 for 1st trajectory, then 4 levels lower than final level of previous trajectory. Average 10.3 seconds per decision group (4 waypoints).\n\n## Preprocessing\n\n- **Data state**: preprocessed\n- **Preprocessing applied**: True\n- **Steps**: downsampling from 2048 Hz to 256 Hz, casual bandpass filtering between 1 and 40 Hz, SPHARA 20th order spatial low-pass filter for interpolation and artifact reduction, common-average re-referencing, ICA for EOG artifact removal, peripheral electrodes removed (25 central channels kept), artifact rejection: windows with peak value > 50 µV rejected\n- **Highpass filter**: 1.0 Hz\n- **Lowpass filter**: 40.0 Hz\n- **Bandpass filter**: [1.0, 40.0]\n- **Filter type**: Butterworth\n- **Filter order**: 14\n- **Artifact methods**: ICA, SPHARA, amplitude thresholding\n- **Re-reference**: car\n- **Downsampled to**: 256.0 Hz\n- **Notes**: Out of 39 recordings, P2 was removed twice from offline or online sessions due to short-circuit with the CMS or DRL electrode. On average, 15.8 ICA components were returned and 1.07 components were removed during construction of online decoders (correlation > 0.7 with EOG).\n\n## Signal Processing\n\n- **Classifiers**: LDA, Generalized Linear Model with elastic net regularization\n- **Feature extraction**: PSD, ICA, log-PSD\n- **Frequency bands**: analyzed=[2.0, 28.0] Hz; theta=[4.0, 8.0] Hz; alpha=[10.5, 13.0] Hz\n- **Spatial filters**: SPHARA, common-average reference\n\n## Cross-Validation\n\n- **Method**: leave-one-pair-out cross-validation (4x or 64x depending on class balance)\n- **Folds**: 4\n- **Evaluation type**: within_subject, cross_session\n\n## Performance (Original Study)\n\n- **Accuracy**: 76.7%\n- **Offline Validation Accuracy Mean**: 76.7\n- **Offline Validation Accuracy Std**: 5.1\n- **Online Session 2 Accuracy Mean**: 56.2\n- **Online Session 2 Accuracy Std**: 8.6\n- **Online Session 3 Accuracy Mean**: 54.7\n- **Online Session 3 Accuracy Std**: 11.0\n- **Online Above Chance Recordings**: 16 out of 26 (~62%)\n\n## BCI Application\n\n- **Applications**: drone control, adaptive learning, difficulty regulation, visuomotor learning\n- **Environment**: indoor laboratory\n- **Online feedback**: True\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: EEG\n- **Type**: Experimental/Research\n\n## Documentation\n\n- **DOI**: 10.1109/TAFFC.2021.3059688\n- **Associated paper DOI**: 10.1109/THMS.2020.3038339\n- **License**: CC-BY-4.0\n- **Investigators**: Ping-Keng Jao, Ricardo Chavarriaga, Jose del R. Millan\n- **Senior author**: Jose del R. Millan\n- **Contact**: ping-keng.jao@alumni.epfl.ch; ricardo.chavarriaga@zhaw.ch; jose.millan@austin.utexas.edu\n- **Institution**: Ecole Polytechnique Federale de Lausanne\n- **Address**: 1015 Geneva, Switzerland\n- **Country**: Switzerland\n- **Repository**: BNCI Horizon\n- **Publication year**: 2021\n- **Funding**: Swiss National Centres of Competence in Research (NCCR) Robotics\n- **Acknowledgements**: The authors would like to thank Alexander Cherpillod for his help in the implementation of the simulator and Ruslan Aydarkhanov for his suggestions in designing the protocol. Some figures were drawn with the Gramm MATLAB toolbox.\n- **Keywords**: EEG, real-time decoding of difficulty, closed-loop adaptation, (simulated) flying, workload, challenge point, brain-machine interface\n\n## Abstract\n\nAdaptively increasing the difficulty level in learning was shown to be beneficial than increasing the level after some fixed time intervals. To efficiently adapt the level, we aimed at decoding the subjective difficulty level based on Electroencephalography (EEG) signals. We designed a visuomotor learning task that one needed to pilot a simulated drone through a series of waypoints of different sizes, to investigate the effectiveness of the EEG decoder. The EEG decoder was compared with another condition that the subjects decided when to increase the difficulty level. We examined the decoding performance together with behavioral outcomes. The online accuracies were higher than the chance level for 16 out of 26 cases, and the behavioral results, such as task scores, skill curves, and lear","bids_version":"1.9.0","sessions_count":1,"publish_date":"2026-03-26 07:29:27","embedding_dirty":0,"license_tier":"attribution","zarr_status":"ready","zarr_converted_at":"2026-09-04 08:53:59","zarr_store_count":13,"zarr_index_etag":"33ec44aa4c456955894bea8e821b96c1","zarr_source_commit":"74c0fcf59a6dd03edce3b0337b411a49b7812317","archive_status":"ready","archive_size":9438235177,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":26,"n_channels":64,"electrode_system":"10-10","has_hed":1,"hed_version":"8.4.0","is_exemplar":0,"bytes_present":10097846284,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":1,"archive_absent_files":0,"archive_declared_files":167,"zarr_pool_breaks":0,"total_recording_duration":58288,"recording_duration_min":4232,"recording_duration_max":4815,"recording_count":13,"recordings_unavailable":0,"recordings_measured":13,"channel_count_min":64,"channel_count_max":64,"sampling_frequency":256,"power_line_frequency":50,"eeg_reference":"car","placement_scheme":"10-10 system","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 18:15:59\",\"metadata_updated_at\":\"2026-08-18 21:22:51\",\"archive_checked_at\":\"2026-08-18 21:30:27\",\"zarr_checked_at\":\"2026-06-07 17:58:36\",\"records_checked_at\":\"2026-08-18 21:23:54\",\"citations_updated_at\":\"2026-09-08 03:00:48\",\"channel_montage_checked_at\":\"2026-06-28 23:00:27\",\"hed_checked_at\":\"2026-06-30 07:32:59\",\"data_checked_at\":null,\"availability_report_at\":\"2026-08-22 03:01:00\",\"signal_defaults_at\":\"2026-09-02 11:48:50\",\"recording_stats_at\":\"2026-09-05 03:01:53\"}","participants":13,"num_citations":26,"latest_version":"v1.0.2","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"9.42 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000249/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}