{
  "schema_version": "bci-report-random-init-controls-update-v1",
  "release_id": "random-init-controls-update-20261106",
  "generated_at": "2026-11-06",
  "status": "reviewed aggregate",
  "title": "Random-initialization controls for five further encoder checkpoints",
  "question": "For REVE Large, ST-EEGFormer Base, ST-EEGFormer Large, SingLEM and EEGMamba, on motor imagery and rest, mental arithmetic and rest, and sleep staging: the paired difference, over the same held-out people, between the published pretrained encoder and the same architecture with constructor-random weights (the mean of three initializations), both read by the published frozen ridge head.",
  "placement": {
    "page": "/topics/does-pretraining-help/",
    "anchor": "random-init-further",
    "matrix": false,
    "leaderboard": false,
    "beside": "the published pretrained-minus-random rows of LaBraM, CBraMod and EEGPT"
  },
  "metric_units": "Balanced accuracy is the equal-person mean of per-person balanced accuracy, a proportion in [0, 1]; differences and interval bounds are differences of proportions (100 times for percent or percentage points). Percent blocks hold 100 times the value rounded to 12 decimals. People and windows are whole numbers.",
  "print_formats": {
    "balanced_accuracy": "pct1",
    "difference": "pp2",
    "interval_95": "sgn2",
    "percent": {
      "balanced_accuracy": "pct1raw",
      "difference": "ppr2",
      "interval_95": "sgr2"
    },
    "counts": "count"
  },
  "design": {
    "backend": "Local Ubuntu / CUDA",
    "frozen_recipe": "The published frozen-probe recipe with only the weights changed: the published 250 Hz microvolt windows (2 s; a 30 s sleep epoch is the mean of the pooled features of 15 two-second segments), recorded channels only, each encoder’s published input view and pooling, strict fp32, and the published participant-disjoint folds, five per task.",
    "readouts": {
      "fixed": {
        "label": "Fixed readout",
        "role": "primary",
        "text": "The published rows’ head: ridge, alpha 100, on features standardized on the training folds, class-balanced."
      },
      "selected": {
        "label": "Train-selected readout",
        "role": "sensitivity",
        "text": "Alpha chosen among 100, 10,000 and 1 on inner-validation people drawn from the outer-training people only, then refit on all outer-training people."
      }
    },
    "random_init": {
      "count": 3,
      "rule": "Each encoder’s own upstream constructor, built on CPU after fixed seeds (the published controls’ seeds); every parameter and buffer of the encoder is replaced, and the input view, channel tables, electrode positions, pooling, head and folds stay as published.",
      "only_mean_published": true
    },
    "contrast": "Per person, the pretrained balanced accuracy minus the mean over the three random initializations; the cohort value is the equal-person mean of these differences.",
    "interval": {
      "kind": "paired participant bootstrap",
      "statistic": "equal-person mean of per-person differences",
      "level": 0.95,
      "draws": 10000,
      "seed": 20260920,
      "percentiles": [
        2.5,
        97.5
      ],
      "multiplicity_correction": false,
      "text": "10,000 paired participant draws (seed 20260920), 2.5 and 97.5 percentiles; each draw keeps a person’s pretrained and random values together."
    },
    "reading_rules": {
      "above": {
        "label": "pretrained higher",
        "rule": "interval wholly above zero",
        "phrase": "the pretrained encoder scored higher than its random initializations under this frozen readout"
      },
      "below": {
        "label": "random initializations higher",
        "rule": "interval wholly below zero",
        "phrase": "the random initializations scored higher than the pretrained encoder under this frozen readout"
      },
      "overlap": {
        "label": "no difference shown",
        "rule": "interval includes zero",
        "phrase": "no difference shown"
      }
    },
    "roles": {
      "rule": "primary when the encoder's published frozen score on that protocol exceeds the published frozen scores of LaBraM, CBraMod and EEGPT; secondary otherwise; fixed from published values before the run; reporting priority only, not confirmatory.",
      "primary": 7,
      "secondary": 8,
      "use": "Roles set reporting priority only: all fifteen fixed-readout contrasts are printed with equal prominence, and the roles are not confirmatory."
    },
    "multiplicity": {
      "contrasts": 15,
      "note": "With 15 uncorrected intervals, about one in twenty can exclude zero by chance."
    }
  },
  "encoders": [
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      "id": "reve-large",
      "name": "REVE Large",
      "family_slug": "reve",
      "revision": "brain-bzh/reve-large @ 317531c7",
      "checkpoint_file": "model.safetensors",
      "checkpoint_sha256": "f519428404aab27f7db816722c7fb8c73d08daf10986c9c21b0066be9e93bb31",
      "licence": "REVE Responsible Use License v1.0 (owner-approved 2026-10-04)",
      "paper": "https://arxiv.org/abs/2510.21585",
      "pooled_width": {
        "mi-rest": 1216,
        "arithmetic-rest": 1216,
        "sleep-scalp": 1216
      },
      "row_note": "Input removes the per-segment per-channel mean, as in the published row."
    },
    {
      "id": "steegformer-base",
      "name": "ST-EEGFormer Base",
      "family_slug": "st-eegformer",
      "revision": "official GitHub release checkpoint-288.pth (code @ 542ee17)",
      "checkpoint_file": "checkpoint-288.pth",
      "checkpoint_sha256": "3c04469072b1e2acf9333ee48ee1002f2db42f46ffb0daab6269686ebc6fe6c1",
      "licence": "MIT (repo LICENSE; README reserves the paper, diagrams and the name)",
      "paper": "https://openreview.net/forum?id=5Xwm8e6vbh",
      "pooled_width": {
        "mi-rest": 768,
        "arithmetic-rest": 768,
        "sleep-scalp": 768
      },
      "row_note": null
    },
    {
      "id": "steegformer-large",
      "name": "ST-EEGFormer Large",
      "family_slug": "st-eegformer",
      "revision": "official GitHub release large_weights_only_196.pth",
      "checkpoint_file": "large_weights_only_196.pth",
      "checkpoint_sha256": "7b2ed01dec88938ea0a8aba5621eab283bb61ec12b88c6475722fffae8afda83",
      "licence": "MIT (as Base)",
      "paper": "https://openreview.net/forum?id=5Xwm8e6vbh",
      "pooled_width": {
        "mi-rest": 1024,
        "arithmetic-rest": 1024,
        "sleep-scalp": 1024
      },
      "row_note": "The authors’ downstream class uses a 1-based temporal index and Large may have been pretrained with a 0-based one, so its temporal sinusoid may sit one position off; this concerns the pretrained arm’s input handling."
    },
    {
      "id": "singlem",
      "name": "SingLEM",
      "family_slug": "singlem",
      "revision": "ttlabtuat/SingLEM @ 6db5b5af",
      "checkpoint_file": "singlem_downstream_excluded.pt",
      "checkpoint_sha256": "cb4c1b4f4a6fae99a984b883177e09b8c2fe44ed0d4c9a0ded3928dc425584bb",
      "licence": "MIT (checkpoint committed in the MIT repository)",
      "paper": "https://arxiv.org/abs/2509.17920",
      "pooled_width": {
        "mi-rest": 480,
        "arithmetic-rest": 608,
        "sleep-scalp": 192
      },
      "row_note": "Each channel is encoded independently, and the tokenizer leaves the last 0.25 s of each 2 s segment unseen, as in the published row."
    },
    {
      "id": "eegmamba",
      "name": "EEGMamba",
      "family_slug": "eegmamba",
      "revision": "weighting666/EEGMamba @ 0b060d87",
      "checkpoint_file": "pretrained_EEGMamba.pth",
      "checkpoint_sha256": "b452bb29ecf1d6131ba82a50c6e13823ec1d660d9009d013e691d19b2916f4fe",
      "licence": "MIT",
      "paper": "https://doi.org/10.1016/j.neunet.2025.107816",
      "pooled_width": {
        "mi-rest": 3000,
        "arithmetic-rest": 3800,
        "sleep-scalp": 1200
      },
      "row_note": null
    }
  ],
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    {
      "id": "mi-rest",
      "title": "Motor imagery & rest",
      "dataset": "ds003810",
      "dataset_id": "ds003810",
      "people": 10,
      "windows": 1200,
      "window_seconds": 2,
      "classes": 2,
      "chance": 0.5
    },
    {
      "id": "arithmetic-rest",
      "title": "Arithmetic & rest",
      "dataset": "EEGMAT",
      "dataset_id": "physionet-eegmat-1.0.0",
      "people": 36,
      "windows": 2160,
      "window_seconds": 2,
      "classes": 2,
      "chance": 0.5
    },
    {
      "id": "sleep-scalp",
      "title": "Sleep staging",
      "dataset": "EESM19 scalp subset",
      "dataset_id": "eesm19-scalp-sleep",
      "people": 20,
      "windows": 3000,
      "window_seconds": 30,
      "classes": 5,
      "chance": 0.2
    }
  ],
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      "id": "machine",
      "text": "Every arm ran on Local Ubuntu / CUDA with deterministic settings; the pretrained and random arms of a cell share the device and the session."
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    {
      "id": "pretrained-arm",
      "text": "Each pretrained arm was re-extracted and matched its published cell before its random arms ran: identical predictions in all 15 cells, features bitwise equal in 12 and within 2.6e-6 for EEGMamba (CUDA kernel arithmetic). Only the random arms are new."
    },
    {
      "id": "checkpoints",
      "text": "SingLEM is its downstream-excluded checkpoint (singlem_downstream_excluded.pt), the checkpoint of the published row; ST-EEGFormer Base and Large are the official release files checkpoint-288.pth and large_weights_only_196.pth."
    },
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      "id": "gates",
      "text": "The published head reproduced all 15 published cells from their saved features; every random state matched its digest, frozen before the run; the train-selected head reproduced the 4 published LaBraM and CBraMod train-selected values."
    },
    {
      "id": "export-sample",
      "text": "An export check rebuilt one of REVE Large’s three random initializations on CPU, re-extracted 64 of the 1,200 motor-imagery windows and matched the sealed features.",
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      "result": "matched"
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    "text": "None of the three datasets is in these four models’ published pretraining lists (checked 2026-10-04; EEGMamba’s list read from its official code)."
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        "licence": "REVE Responsible Use License v1.0",
        "version": "reve-large @ 317531c7",
        "conditions": [
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          "cite the REVE paper and name the model version",
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        "paper": "https://arxiv.org/abs/2510.21585",
        "positions": "brain-bzh/reve-positions @ befa5b57"
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        "note": "The repository README reserves the paper, the diagrams and the ST-EEGFormer name; none is reproduced.",
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        "paper": "https://doi.org/10.1016/j.neunet.2025.107816"
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        "licence": "CC0-1.0",
        "licence_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/"
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        "source": "https://physionet.org/content/eegmat/1.0.0/",
        "version": "PhysioNet EEG During Mental Arithmetic Tasks 1.0.0 · 1.0.0",
        "licence": "Open Data Commons Attribution License 1.0",
        "licence_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."
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        "source": "https://doi.org/10.18112/openneuro.ds005185.v1.0.2",
        "version": "Hugging Face processed mirror of OpenNeuro ds005185 1.0.2 · {\"mirror\": \"fb045012b58a0b755f9fedf5931dff9b64a9797f\", \"upstream\": \"0857858f7a2ba1582930f23eca3ec56f90a96da9\"}",
        "licence": "CC0-1.0 declared by upstream and mirror",
        "licence_url": "https://creativecommons.org/publicdomain/zero/1.0/",
        "attribution": "Kaare B. Mikkelsen et al. · Accurate whole-night sleep monitoring with dry-contact ear-EEG (2019), doi:10.1038/s41598-019-53115-3; OpenNeuro ds005185 v1.0.2. Processed mirror: Zachary1150/EESM19-Processed."
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    ],
    "weights_distributed": false,
    "random_weights_distributed": false,
    "text": "Aggregate results only; downloaded and random weights stay local."
  },
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      "id": "frozen-readout",
      "text": "Each difference is for frozen encoders read by the published ridge head (alpha 100 on features standardized on the training folds, class-balanced); fine-tuning and adaptation are separate questions."
    },
    {
      "id": "random-baseline",
      "text": "The random arm is each upstream constructor’s own initialization in the pinned environment (for example, REVE’s linear weights drawn from N(0, 0.02) and ST-EEGFormer’s channel-embedding projection starting at zero); three initializations with the published controls’ seeds, mean only, and the intervals are paired participant bootstraps conditional on those three."
    },
    {
      "id": "within-encoder",
      "text": "Both arms of an encoder share its input view, pooling and feature width. Widths differ across encoders (EEGMamba keeps channels × 200 dimensions; SingLEM concatenates per-channel tokens), and random features can score well under a wide linear readout, so each difference is read within its encoder."
    },
    {
      "id": "readout-dependence",
      "text": "The train-selected readout changes the reading of three cells: ST-EEGFormer Large on MI/rest and on mental workload, and EEGMamba on mental workload."
    },
    {
      "id": "cohorts-multiplicity",
      "text": "Cohorts of 10 people on MI/rest, 36 on mental workload and 20 on sleep staging; the intervals are descriptive participant bootstraps that ignore cross-validation dependence, and with 15 intervals without multiplicity correction about one in twenty can exclude zero by chance."
    },
    {
      "id": "scope",
      "text": "Same task and recording setup with new people; cross-dataset, cross-day and deployment questions lie outside these results."
    }
  ],
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    "Per-seed values, seed ranges and seed standard deviations: only the mean of the three random initializations is published.",
    "Per-person, per-fold and per-window values; features, predictions, fitted heads and participant identifiers.",
    "Counts of people helped or harmed: none were computed.",
    "Random or adapted weights of any encoder: random states stay local, and downloaded weights are never redistributed.",
    "The alpha the train-selected readout chose in each fold.",
    "Timings, memory and device readings from the run.",
    "Bridge and gate details beyond the methods notes.",
    "The research records themselves, pinned by SHA-256 in the release manifest because several carry private storage paths."
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  "release_record": {
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    "decision": "Publish the random-initialization controls for REVE Large, ST-EEGFormer Base and Large, SingLEM and EEGMamba on three tasks as aggregates: one section of the does-pretraining-help page beside the published rows, outside the core matrix.",
    "rights": "REVE Large under the REVE Responsible Use License v1.0 (reve-large @ 317531c7): aggregate scientific results only. ST-EEGFormer, SingLEM and EEGMamba under MIT. Downloaded and random weights stay local."
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