{
  "schema_version": "bci-report-signal-jepa-eegsimpleconv-update-v1",
  "release_id": "signal-jepa-eegsimpleconv-update-20261106",
  "generated_at": "2026-11-06",
  "status": "reviewed aggregate",
  "title": "Signal-JEPA and EEGSimpleConv",
  "placement": {
    "pages": [
      "/protocols/mi-rest/",
      "/protocols/arithmetic-rest/"
    ],
    "anchor": "signal-jepa-eegsimpleconv",
    "position": "the last section before methods-and-limits",
    "methods_anchors": [
      "signal-jepa",
      "eegsimpleconv"
    ],
    "directory_entries": [
      "Signal-JEPA",
      "EEGSimpleConv"
    ],
    "core_matrix_row": false,
    "foundation_encoder_table": false,
    "matrix_plot": false,
    "leaderboard": false,
    "text": "A section of its own on the motor imagery & rest and arithmetic & rest protocol pages, beside the core matrix, with one row per model grouped by family; the core matrix, its plot and the further-encoder panel keep their rows."
  },
  "models": {
    "signal_jepa": {
      "id": "signal-jepa",
      "name": "Signal-JEPA",
      "family": "foundation",
      "mode": "Frozen encoder + ridge head",
      "input_contract": "Each two-second 250 Hz microvolt window of the matrix is band-passed 0.5-40 Hz (fourth-order Butterworth, zero phase, per window) and resampled to 128 Hz (256 samples), the format Signal-JEPA was pretrained on; microvolt scale kept; only recorded channels, legacy T3/T4/T5/T6 read as T7/T8/P7/P8; no padding, interpolation or re-referencing.",
      "input_short": "0.5–40 Hz band-pass, 128 Hz",
      "input_adapter": {
        "band_pass_hz": [
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          40
        ],
        "sampling_rate_hz": 128,
        "window_samples": 256
      },
      "parameters": 3456852,
      "head": {
        "kind": "ridge",
        "alpha": 100,
        "standardised_on": "the training people",
        "class_weight": "balanced",
        "fitted_on": "the training people only"
      },
      "checkpoint": {
        "repository": "braindecode/signal-jepa",
        "revision": "51232ee0795a60e4378c17befe1e2ea5e94450c4",
        "file": "model.safetensors",
        "sha256": "7d30b0dd781f08de72bfbd59ac93100da6615b36f2d9d522dd4a3220078c34c2"
      },
      "weights_licence": "MIT",
      "weights_licence_basis": "model-card metadata at the pinned revision",
      "code_licence": "BSD-3-Clause",
      "code_basis": "braindecode 1.5.1, SignalJEPA",
      "weights_distributed": false,
      "pretraining_data": "Lee2019 (OpenBMI), as the model card names it",
      "pretraining_data_terms": "not checked",
      "paper": "https://arxiv.org/abs/2403.11772",
      "model_card": "https://huggingface.co/braindecode/signal-jepa"
    },
    "eegsimpleconv": {
      "id": "eegsimpleconv",
      "name": "EEGSimpleConv",
      "family": "small",
      "mode": "Trained from scratch, 20 epochs",
      "input_contract": "the matrix's published 250 Hz microvolt windows, per-channel standardisation from training-fold people (the model resamples internally to 80 Hz)",
      "input_short": "the matrix’s 250 Hz windows",
      "input_adapter": {
        "sampling_rate_hz": 250
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      "seeds": 3,
      "configuration": {
        "feature_maps": 64,
        "n_convs": 2,
        "resampling_freq_hz": 80,
        "kernel_size": 8,
        "training_epochs": 20,
        "optimizer": "AdamW",
        "lr": 0.001,
        "weight_decay": 0.01,
        "batch": 64
      },
      "code_licence": "BSD-3-Clause",
      "code_basis": "braindecode 1.5.1, EEGSimpleConv",
      "author_repository_licence": "unclear",
      "weights": null,
      "weights_distributed": false,
      "paper": "https://arxiv.org/abs/2309.07159",
      "docs": "https://braindecode.org/stable/generated/braindecode.models.EEGSimpleConv.html"
    }
  },
  "protocols": [
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      "id": "mi-rest",
      "title": "Motor imagery & rest",
      "dataset": "ds003810",
      "people": 10,
      "windows": 1200,
      "channels": 15,
      "window_seconds": 2,
      "folds": 5,
      "chance_level": 0.5,
      "signal_jepa": {
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        "macro_f1": 0.6685115997408043,
        "interval_95": [
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          0.705
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        "percent": {
          "balanced_accuracy": 67.25,
          "interval_95": [
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            70.5
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        },
        "interval_includes_chance": false,
        "comparison": {
          "comparator": "eegnet-cell",
          "comparator_row": "eegnet",
          "comparator_file": "mi-rest-results.csv",
          "comparator_percent": 71.0,
          "comparator_label": "against the matrix's one-seed EEGNet cell; training variance not included",
          "eegnet_mean_file": "mi-rest-protocol.json",
          "eegnet_three_seed_mean_percent": 69.47222222222221,
          "difference_pp": -3.75,
          "interval_pp": [
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          "paired_on_same_people": true,
          "bootstrap": {
            "draws": 10000,
            "unit": "participant"
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          "multiplicity": "none"
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        "seed_mean_macro_f1": 0.530474908530242,
        "percent": {
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        "parameters": 185022,
        "comparison": {
          "comparator": "eegnet-three-seed-mean",
          "comparator_file": "mi-rest-protocol.json",
          "comparator_percent": 69.47222222222221,
          "mean_of_seed_matched_differences_pp": -10.138888888888888,
          "interval": null,
          "interval_reason": "the earlier study computed intervals per seed only"
        }
      }
    },
    {
      "id": "arithmetic-rest",
      "title": "Arithmetic & rest",
      "dataset": "EEGMAT",
      "people": 36,
      "windows": 2160,
      "channels": 19,
      "window_seconds": 2,
      "folds": 5,
      "chance_level": 0.5,
      "signal_jepa": {
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        "interval_includes_chance": true,
        "comparison": {
          "comparator": "eegnet-cell",
          "comparator_row": "eegnet",
          "comparator_file": "arithmetic-rest-results.csv",
          "comparator_percent": 67.63888888888889,
          "comparator_label": "against the matrix's one-seed EEGNet cell; training variance not included",
          "eegnet_mean_file": "arithmetic-rest-protocol.json",
          "eegnet_three_seed_mean_percent": 67.51543209876543,
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          "interval_pp": [
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          "paired_on_same_people": true,
          "bootstrap": {
            "draws": 10000,
            "unit": "participant"
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          "multiplicity": "none"
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      "eegsimpleconv": {
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        "seed_mean_macro_f1": 0.5772629696842942,
        "percent": {
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        "parameters": 187070,
        "comparison": {
          "comparator": "eegnet-three-seed-mean",
          "comparator_file": "arithmetic-rest-protocol.json",
          "comparator_percent": 67.51543209876543,
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          "interval": null,
          "interval_reason": "the earlier study computed intervals per seed only"
        }
      }
    }
  ],
  "comparison_rule": "Each difference is a paired comparison with EEGNet on the same people.",
  "notes": [
    {
      "id": "input",
      "text": "Signal-JEPA’s input: each window band-passed 0.5–40 Hz and resampled to 128 Hz, the format it was pretrained on, in microvolts, recorded channels only. The further-encoder panel follows its own input contract, so this row sits apart from it."
    },
    {
      "id": "sj-comparator",
      "text": "Signal-JEPA’s difference is paired, on the same people, against this page’s EEGNet cell: 10,000 participant bootstrap draws, no multiplicity correction. EEGNet’s mean over its three seeds here is {eegnet_three_seed_mean}.",
      "slots": {
        "eegnet_three_seed_mean": {
          "leaf": "protocols[].signal_jepa.comparison.eegnet_three_seed_mean_percent",
          "fmt": "pct2raw",
          "file": "<id>-protocol.json",
          "field": "seedSensitivity.meanPercent"
        }
      }
    },
    {
      "id": "esc-seeds",
      "text": "EEGSimpleConv: the mean of three seeds, at width 64 and the matrix’s 20-epoch budget, fixed before scoring. Its difference is from EEGNet’s mean over the same three seeds and is printed without an interval."
    },
    {
      "id": "pretraining-data",
      "text": "Signal-JEPA’s model card names Lee2019 (OpenBMI) as its pretraining data."
    }
  ],
  "rights": {
    "data": [
      {
        "protocol": "mi-rest",
        "dataset": "ds003810",
        "license": "CC0-1.0",
        "license_url": "https://creativecommons.org/publicdomain/zero/1.0/",
        "attribution": "Peterson et al. · OpenNeuro ds003810, version 2.0.2. Study: https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/",
        "source": "https://doi.org/10.18112/openneuro.ds003810.v2.0.2"
      },
      {
        "protocol": "arithmetic-rest",
        "dataset": "EEGMAT",
        "license": "Open Data Commons Attribution License 1.0",
        "license_url": "https://opendatacommons.org/licenses/by/1-0/",
        "attribution": "Igor Zyma, Ivan Seleznov, Anton Popov, Mariia Chernykh, Oleksii Shpenkov · EEG During Mental Arithmetic Tasks 1.0.0, PhysioNet, doi:10.13026/C2JQ1P. Study: Zyma et al. (2019), doi:10.3390/data4010014. PhysioNet platform: Pollard et al. (2026), doi:10.1038/s44360-026-00096-z.",
        "source": "https://physionet.org/content/eegmat/1.0.0/"
      }
    ],
    "signal_jepa": {
      "weights": "MIT",
      "weights_basis": "the model card at revision 51232ee0",
      "weights_distributed": false,
      "code": "BSD-3-Clause",
      "code_basis": "braindecode 1.5.1",
      "pretraining_data": "Lee2019 (OpenBMI)",
      "pretraining_data_terms": "not checked"
    },
    "eegsimpleconv": {
      "code": "BSD-3-Clause",
      "code_basis": "braindecode 1.5.1",
      "author_repository_licence": "unclear",
      "weights": "none; trained from scratch",
      "weights_distributed": false
    }
  },
  "correction": {
    "date": "2026-11-06",
    "files": [
      "experiments.json"
    ],
    "released_status": {
      "Signal-JEPA": "Research candidate",
      "EEGSimpleConv": "Research candidate"
    },
    "released_notes": {
      "Signal-JEPA": "not downloaded or executed for this release",
      "EEGSimpleConv": "not executed for this release"
    },
    "text_en": "The model directory’s notes say Signal-JEPA was not downloaded or executed, and EEGSimpleConv not executed, for that release. Both were run the same day in a separate study on motor imagery & rest and arithmetic & rest; their results are on those two protocol pages.",
    "href": "/protocols/mi-rest/#signal-jepa-eegsimpleconv"
  },
  "directory_status": {
    "released_status": "Research candidate",
    "suggested_status": "Evaluated (two protocols, beside the matrix)",
    "page": "/protocols/mi-rest/#signal-jepa-eegsimpleconv",
    "notes": {
      "Signal-JEPA": "Scored as a frozen encoder with a ridge head on motor imagery & rest and arithmetic & rest, beside the core matrix, with its own input adapter (0.5–40 Hz, 128 Hz). Aggregate results only; the weights are not redistributed. The note above is the released one.",
      "EEGSimpleConv": "Trained from scratch for 20 epochs on motor imagery & rest and arithmetic & rest, three seeds, beside the core matrix; the pages print the mean of the three seeds. The note above is the released one."
    }
  },
  "release_record": {
    "date": "2026-11-06",
    "decided": "2026-11-05",
    "placement_decided": "2026-11-06",
    "decision": "Publish Signal-JEPA as a frozen encoder with a ridge head and EEGSimpleConv trained from scratch as their audited aggregates, beside the core matrix on the motor imagery & rest and arithmetic & rest protocol pages, each compared with EEGNet on the same people, with a correction entry for the released directory notes.",
    "rights": "ds003810 (CC0-1.0) and EEGMAT (Open Data Commons Attribution License 1.0) keep their released records; aggregate results only. Signal-JEPA’s weights are MIT as its model card at revision 51232ee0 states; its code is BSD-3-Clause (braindecode 1.5.1); the weights are not redistributed. EEGSimpleConv’s code is BSD-3-Clause (braindecode 1.5.1) and the separate author repository’s licence is unclear; it was trained from scratch, and no trained weights are shared.",
    "research_release": "The study’s records mark it complete and audited: an audit from outside the original study re-derived Signal-JEPA to every printed digit on the device type of the original run and retrained every EEGSimpleConv seed exactly, and an independent interpretation check passed with required wording. These records do not themselves authorise publication; publication on the site rests on the site owner’s decisions of 2026-11-05 and 2026-11-06."
  },
  "audits": {
    "replication": "Signal-JEPA re-derived to every printed digit on the device type of the original run; every EEGSimpleConv seed retrained exactly",
    "interpretation_review": "independent",
    "descriptive_interval": "Signal-JEPA’s descriptive interval recomputed by the export audit from the sealed predictions"
  },
  "not_published": [
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    "EEGSimpleConv’s per-seed balanced accuracies, their spread and range, and its per-seed intervals: only the mean of three seeds is published.",
    "An interval for EEGSimpleConv’s difference: the earlier study computed intervals per seed only.",
    "Measured run times, and the machines and storage paths of the runs.",
    "A partial fine-tuning arm, which was not run.",
    "The research aggregates, audits, reviews and handoffs themselves, pinned by SHA-256 in the release manifest because some carry private storage paths."
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