# Can fewer electrodes, or electrodes in the ear, match a full scalp montage?

Montage · two paired comparisons and a simulated cap shift · BETA and sleep montages

On the same 10 people and the same 30-second epochs, six scalp electrodes staged sleep better than four in-ear channels — for every one of the 10. A second comparison — four posterior electrodes against all sixteen for telling eyes open from closed — found no consistent difference across 19 people. On BETA, single-channel and channel-wise foundation encoders lost accuracy going from eight electrodes to four. A last section simulates the whole cap shifted by one electrode spacing on BETA.

- [Reviewed aggregate JSON ↓](https://bci.report/data/evidence-update.json)
- 

Measured on: [BETA](https://bci.report/datasets/beta/); [EESM19 scalp subset](https://bci.report/datasets/eesm19/); [EESM23 · Ear-EEG Sleep Monitoring 2023](https://bci.report/datasets/eesm23/); [EEG Alpha Waves dataset](https://bci.report/datasets/alpha-waves/) · Methods: [EEGNet](https://bci.report/methods/eegnet/); [LaBraM](https://bci.report/methods/labram/); [CBraMod](https://bci.report/methods/cbramod/); [CCA](https://bci.report/methods/cca/); [REVE](https://bci.report/methods/reve/); [LUNA](https://bci.report/methods/luna/); [BrainOmni](https://bci.report/methods/brainomni/); [CodeBrain](https://bci.report/methods/codebrain/); [EEGMamba](https://bci.report/methods/eegmamba/); [ST-EEGFormer](https://bci.report/methods/st-eegformer/); [eeg-fm-masking](https://bci.report/methods/eeg-fm-masking/); [ERP-FM](https://bci.report/methods/erp-fm/); [SingLEM](https://bci.report/methods/singlem/); [ZUNA](https://bci.report/methods/zuna/); [EEGPT](https://bci.report/methods/eegpt/)

## Short answer

In-ear channels did not, for sleep staging in this cohort: on the same **10** people and epochs, four in-ear channels scored **53.6%** balanced accuracy on five-stage sleep against **69.0%** for six scalp electrodes (chance **20%**), a paired gap of **+15.4 pp**. In a separate eyes-open-or-closed task on **19** other people, four posterior electrodes showed no consistent difference from all sixteen: all sixteen minus four came to **−1.6 pp** (paired 95% interval **−10.0 pp** to **+6.8 pp**). Both are channel sets from research recordings: the sleep gap mixes electrode position, count and hardware, and the four posterior electrodes are a software subset of one recording. On the core matrix's BETA protocol (**70** people), the further frozen foundation encoders show no small-montage advantage: the single-channel SingLEM went from **26.6%** with eight electrodes to **20.5%** with four and the channel-wise ZUNA 1.1 from **11.9%** to **8.4%**, and no new encoder lies above training-free CCA at four electrodes (**57.6%**).

**Two separate paired comparisons, each on its own people and task.** Paired difference in person-mean balanced accuracy, scalp or all sixteen minus in-ear or four posterior. Dot: the mean difference; line: descriptive person-bootstrap 95% interval; dashed line: zero, no difference. Right of zero, the scalp or sixteen-electrode configuration scored higher. Chance is 20% for five-stage sleep and 50% for eyes open or closed.

- Sleep staging · six scalp minus four in-ear · 10 people — 95% interval +10.6 to +20.3 pp: +15.4 pp
- Eyes open or closed · all sixteen minus four posterior · 19 people — 95% interval −10.0 to +6.8 pp: −1.6 pp

**+15.4 pp** Scalp minus in-ear balanced accuracy, same 10 people

### Every person scored higher on scalp

10 of 10 people scored higher with six scalp electrodes than with four in-ear channels. The descriptive person-bootstrap interval for the mean difference is +10.6 to +20.3 pp.

Two configurations, identical epochs

## Four in-ear channels against six scalp electrodes

Five-stage sleep, scored in 30-second epochs; chance is 20%. Ten leave-one-person-out folds and one fixed model for both configurations.

Person-mean balanced accuracy and macro F1. Descriptive person-bootstrap 95% intervals below each balanced accuracy.

| Configuration | Channels | Balanced accuracy | Macro F1 |
| --- | --- | --- | --- |
| Four in-ear channels — physical in-ear channels | RB · RT · LB · LT | 53.6% (47.4%–59.7%) | 47.6% |
| Six scalp electrodes — mastoid slots M1 and M2 used only to screen for complete epochs | F3 · C3 · O1 · F4 · C4 · O2 | 69.0% (63.4%–74.2%) | 64.9% |

Usable data

## What the scores are conditioned on

An epoch was kept only if both configurations had complete, finite data for it; that rule dropped 3,849 epochs. The scores describe this subset.

Paired epochs kept

75.2%

11,674 of 15,523

People

10

all retained

Nights

17

17 of 19

Second comparison · eyes open and closed

## Four posterior electrodes against all sixteen

A different task on different people: telling eyes open from eyes closed in 6-second windows, where chance is 50%. Both configurations are subsets of the same 16-electrode research recordings, scored with one fixed model over 19 leave-one-person-out folds.

**−1.6 pp** All sixteen minus four posterior, same 19 people

### No consistent difference

7 of 19 people scored higher with all sixteen electrodes, 7 with the four posterior ones, and 5 tied. The descriptive person-bootstrap interval for the mean difference is −10.0 to +6.8 pp. It spans zero, which is not evidence that four electrodes are as good as sixteen.

Person-mean balanced accuracy and macro F1. Descriptive person-bootstrap 95% intervals below each balanced accuracy.

| Configuration | Channels | Balanced accuracy | Macro F1 |
| --- | --- | --- | --- |
| Four posterior electrodes — selected in software from the same recording | Pz · O1 · Oz · O2 | 79.5% (72.6%–85.8%) | 76.6% |
| All sixteen electrodes — every electrode the cap recorded | Fp1 · Fp2 · Fc5 · Fz · Fc6 · T7 · Cz · T8 · P7 · P3 · Pz · P4 · P8 · O1 · Oz · O2 | 77.9% (70.5%–85.3%) | 74.5% |

Third comparison · core-matrix protocols

## Eight or four electrodes on BETA, and six-channel sleep: the new frozen encoders

The core matrix scores BETA’s 40-target SSVEP twice, with eight and with four posterior electrodes selected from the same 64-channel recordings (70 people, chance 2.5%), and sleep staging on six scalp channels (EESM19 scalp subset, 20 people, chance 20.0%). Eleven further foundation models joined these protocols as frozen probes, with the published recipe and only the encoder swapped, and so did EEGPT, in the same runs as the released LaBraM and CBraMod rows. Unlike the two comparisons above, each montage here is trained and tested on its own, and the released and the new rows share the people and folds.

### No small-montage advantage for single-channel or channel-wise encoders

SingLEM, which encodes each channel on its own, went from 26.6% with eight electrodes to 20.5% with four (−6.1 pp); ZUNA 1.1, a channel-wise encoder, from 11.9% to 8.4% (−3.5 pp). For both, the two intervals do not overlap. SingLEM lost about as much as the montage encoders that lie above CBraMod at both montages (REVE Large −6.4 pp, eeg-fm-masking MAE (r=9 cm, L=2) −7.7 pp); ZUNA 1.1 started from a much lower score. On six-channel sleep SingLEM is the lowest new row (35.3%, 33.7%–37.0%), below every other row, released or new.

### No new encoder lies above standard CCA at four electrodes

Standard CCA, which needs no training, scores 57.6% with four electrodes (51.8%–63.5%). No new row lies above it. Of the new rows, only eeg-fm-masking MAE (r=9 cm, L=2) has an interval that overlaps it (47.1%); of the released rows, so does spectral ridge (48.2%, 43.1%–53.3%).

Frozen encoders with a ridge head beside the released rows · balanced accuracy with its descriptive 95% interval.

| Method | BETA, 8 electrodes | BETA, 4 electrodes | Eight to four | Sleep, 6 channels |
| --- | --- | --- | --- | --- |
| Released rows · core matrix |  |  |  |  |
| [Standard CCA](https://bci.report/methods/cca/) | 63.1% (57.2%–69.0%) | 57.6% (51.8%–63.5%) | Intervals overlap | — Not in the sleep protocol |
| Spectral ridge | 50.7% (45.6%–55.8%) | 48.2% (43.1%–53.3%) | Intervals overlap | 72.2% (70.7%–73.8%) |
| [LaBraM](https://bci.report/methods/labram/) | 10.8% (9.5%–12.2%) | 12.9% (11.2%–14.6%) | Intervals overlap | 71.1% (69.0%–73.2%) |
| [CBraMod](https://bci.report/methods/cbramod/) | 33.7% (29.8%–37.6%) | 27.6% (24.1%–31.3%) | Intervals overlap | 71.3% (68.7%–73.7%) |
| [EEGNet](https://bci.report/methods/eegnet/) | 55.8% (50.1%–61.3%) | 44.1% (38.6%–49.6%) | **Intervals do not overlap** | 56.5% (54.9%–58.3%) |
| New frozen encoders |  |  |  |  |
| [REVE Base](https://bci.report/methods/reve/) — Input: multi-channel montage | 37.5% (33.3%–41.9%) | 25.5% (22.4%–28.8%) | −12.0 pp **Intervals do not overlap** | 74.6% (72.9%–76.4%) |
| [REVE Large](https://bci.report/methods/reve/) — Input: multi-channel montage | 52.7% (47.7%–57.8%) | 46.4% (41.7%–51.2%) | −6.4 pp — Intervals overlap | 76.6% (75.2%–78.0%) |
| [LUNA Base](https://bci.report/methods/luna/) — Input: multi-channel montage | 8.6% (7.7%–9.5%) | 9.0% (8.1%–10.1%) | +0.5 pp — Intervals overlap | 67.5% (65.3%–69.5%) |
| [LUNA Large](https://bci.report/methods/luna/) — Input: multi-channel montage | 10.0% (8.9%–11.1%) | 11.2% (9.8%–12.6%) | +1.2 pp — Intervals overlap | 72.4% (70.4%–74.0%) |
| [BrainOmni Base](https://bci.report/methods/brainomni/) — Input: multi-channel montage | 21.7% (18.6%–25.0%) | 13.0% (10.6%–15.6%) | −8.8 pp **Intervals do not overlap** | 67.7% (65.8%–69.5%) |
| [CodeBrain](https://bci.report/methods/codebrain/) — Input: multi-channel montage | 11.9% (10.5%–13.3%) | 11.2% (9.8%–12.7%) | −0.7 pp — Intervals overlap | 70.7% (68.8%–72.7%) |
| [EEGMamba](https://bci.report/methods/eegmamba/) — Input: multi-channel montage | 40.2% (35.7%–44.7%) | 32.7% (28.9%–36.7%) | −7.5 pp — Intervals overlap | 64.1% (61.6%–66.4%) |
| [ST-EEGFormer Base](https://bci.report/methods/st-eegformer/) — Input: multi-channel montage | 17.1% (15.5%–18.8%) in the authors' published pretraining list | 17.5% (15.8%–19.3%) in the authors' published pretraining list | +0.4 pp — Intervals overlap | 77.3% (75.6%–78.8%) |
| [ST-EEGFormer Large](https://bci.report/methods/st-eegformer/) — Input: multi-channel montage | 22.6% (20.1%–25.3%) in the authors' published pretraining list | 22.4% (19.9%–25.0%) in the authors' published pretraining list | −0.2 pp — Intervals overlap | 79.6% (78.2%–81.0%) |
| [eeg-fm-masking MAE (r=9 cm, L=2)](https://bci.report/methods/eeg-fm-masking/) — Input: multi-channel montage | 54.8% (49.4%–60.0%) | 47.1% (42.1%–52.1%) | −7.7 pp — Intervals overlap | 67.9% (65.4%–70.4%) |
| [ERP-FM Base](https://bci.report/methods/erp-fm/) — Input: multi-channel montage | 18.4% (16.3%–20.5%) | 20.9% (18.4%–23.5%) | +2.5 pp — Intervals overlap | 71.0% (69.5%–72.6%) |
| [SingLEM](https://bci.report/methods/singlem/) — Input: single-channel | 26.6% (23.7%–29.5%) | 20.5% (17.8%–23.3%) | −6.1 pp **Intervals do not overlap** | 35.3% (33.7%–37.0%) |
| [ZUNA 1.1](https://bci.report/methods/zuna/) — Input: channel-wise — Model card: research use only, not for diagnosis or clinical use. | 11.9% (10.8%–13.0%) Exposure unknown | 8.4% (7.5%–9.4%) Exposure unknown | −3.5 pp **Intervals do not overlap** | 60.1% (57.8%–62.3%) Exposure unknown |
| EEGPT, frozen · the same runs as the released rows |  |  |  |  |
| [EEGPT](https://bci.report/methods/eegpt/) | 20.3% (18.2%–22.6%) | 17.3% (15.5%–19.2%) | Intervals overlap | 75.5% (73.9%–77.1%) |

**EEGPT on these protocols:** PO5 and PO6 use channel embeddings that EEGPT’s pretraining never trained: they are two of the four names in its 62-name channel dictionary (AF7, AF8, PO5, PO6) that its 58-electrode pretraining montage leaves out. The other six channels here, and all four of the 4-channel subset, use trained embeddings. For context: BETA is not in EEGPT’s published pretraining list, but the Tsinghua 40-target SSVEP benchmark (Wang et al., 2017) is, as TSU. BETA’s authors describe BETA as an extension of that benchmark, from the same group with the same 40-target layout and its own recordings; whether anyone took part in both is not stated. Every core window is shorter than EEGPT’s 4-second pretraining window: 1 or 2 seconds, and sleep as fifteen 2-second segments. The encoder computes each 0.25-second patch the same way at any length; a shorter window gives fewer patches to average. The readout is the site’s fixed probe on the frozen summary tokens, as for the published LaBraM and CBraMod rows: ridge (alpha 100) on 2,048 pooled values, and on idle a per-person logistic head. It is not the authors’ downstream probe, a learnable spatial filter with a linear head, and the windows are fed without the average reference EEGPT was pretrained with.

Eight to four: for a new row, the change and whether the two intervals overlap, as the foundation-model export states them; for a released row and for EEGPT only whether the two intervals overlap, because no file of theirs states that difference. The changes are differences of marginal means, with no paired test. Unless a row is marked, its dataset is not in the model authors’ published pretraining list (checked 2026-10-04; EEGPT’s on 2026-10-12).

BETA and sleep · the new frozen encoders

### Small cohorts, descriptive intervals

BETA has 70 people and the sleep protocol 20. Each interval is a participant bootstrap of one row on its own: it ignores cross-validation dependence and carries no multiplicity correction, and with 126 frozen cells from the further encoders and EEGPT’s eight more an occasional non-overlap is expected by chance.

### Method comparisons

BETA is uniform over its 40 targets and the sleep protocol is a balanced sample of epochs: these rows compare methods; they are not detection, false-alarm or latency estimates for real use.

### New people, same task and setup

The folds hold out people. Nothing here is cross-day, cross-device or cross-dataset evidence, and nothing is a clinical claim.

### Short windows

Every encoder sees the published 1–2 s windows (sleep as 15 two-second segments), usually shorter than its pretraining context; each model’s input handling is in its row footnote on the protocol pages.

### Software subsets of one recording

The eight and four electrodes are channels selected in software from the same 64-channel laboratory recordings. A subset picked this way does not validate a physical low-channel cap, and near-floor scores are not ordered reliably between the two subsets.

### Scalp sleep from EESM19

The six-channel column is the core matrix’s sleep protocol, a balanced, quality-screened scalp sample of EESM19 — not EESM23 above and not ear-EEG.

### One fixed recipe, family order

Every new row is the published frozen probe with only the encoder swapped. Rows keep the export’s family order; one row is above another only where their 95% intervals do not overlap.

### Pretraining exposure

BETA is in ST-EEGFormer’s published pretraining list, so its BETA cells are marked; ZUNA 1.1 publishes no list, so every ZUNA 1.1 cell is unknown; EEGMamba’s list is read from its official code (its paper was not read), with medium confidence. The other cells are not in the authors’ published lists (checked 2026-10-04; EEGPT’s on 2026-10-12), which is not proof that the recordings were never seen.

### Weights licences and terms

- **REVE Base · REVE Large**: REVE Responsible Use License v1.0 (owner-approved 2026-10-04) REVE Responsible Use License v1.0: aggregate scientific results only; no re-identification, model inversion, membership inference or non-consensual profiling; cite the REVE paper and name the model version; no adapted weights shared. Model versions: REVE Base @ dc2a075c · REVE Large @ 317531c7. [paper ↗](https://arxiv.org/abs/2510.21585)
- **LUNA Base · LUNA Large**: CC BY-ND 4.0 (owner-approved: internal adaptation, aggregate scores only, no adapted weights or LoRA deltas shared) CC BY-ND 4.0 weights: scores from internal probing/adaptation only; no modified weights, LoRA or adapter deltas are shared; no implied endorsement by the LUNA authors; the LUNA name is not used to suggest an official release. [paper ↗](https://arxiv.org/abs/2510.22257)
- **BrainOmni Base**: MIT [paper ↗](https://arxiv.org/abs/2505.18185)
- **CodeBrain**: Apache-2.0 [paper ↗](https://arxiv.org/abs/2506.09110)
- **EEGMamba**: MIT [paper ↗](https://doi.org/10.1016/j.neunet.2025.107816)
- **ST-EEGFormer Base · ST-EEGFormer Large**: MIT (repo LICENSE; README reserves the paper, diagrams and the name) MIT (repository LICENSE; the README reserves the paper, diagrams and the ST-EEGFormer name). [paper ↗](https://openreview.net/forum?id=5Xwm8e6vbh)
- **eeg-fm-masking MAE (r=9 cm, L=2)**: CC-BY-4.0 (cite arXiv:2609.33487) CC-BY-4.0 weights: attribution required (cite arXiv:2609.33487). [paper ↗](https://arxiv.org/abs/2609.33487)
- **ERP-FM Base**: CC BY-NC-SA 4.0 (non-commercial; aggregate scores only) CC BY-NC-SA 4.0: non-commercial; aggregate scores only; attribution required; no weights redistributed. [paper ↗](https://arxiv.org/abs/2609.32796)
- **SingLEM**: MIT (checkpoint committed in the MIT repository) MIT. [paper ↗](https://arxiv.org/abs/2509.17920)
- **ZUNA 1.1**: Apache-2.0; model card: research use only, not for diagnosis or clinical use Apache-2.0 weights; model card states research use only, not for diagnosis or clinical use. [paper ↗](https://arxiv.org/abs/2607.27308)

No model’s authors endorse these results, and no adapted weights or LoRA deltas were kept or shared.

EEGPT: the checkpoint’s licence is unresolved, and no original or converted weights are distributed; Apache-2.0 covers the repository’s code only.

The full rows, with footnotes and exposure sources, are on the protocol pages: [SSVEP · 8 channels](https://bci.report/protocols/beta-8ch/#foundation-v9), [SSVEP · 4 channels](https://bci.report/protocols/beta-4ch/#foundation-v9), [Sleep staging](https://bci.report/protocols/sleep-scalp/#foundation-v9). The four-minus-eight comparison is in [foundation-models-update.json](https://bci.report/data/foundation-models-update.json).

EEGPT’s rows are in their own section of the same protocol pages, from [eegpt-core-update.json](https://bci.report/data/eegpt-core-update.json), release [`eegpt-core-update-20261013`](https://bci.report/releases/#eegpt-core-update-20261013).

Simulated cap shift · BETA · six decoders

## What if the cap sits one electrode off?

A simulation on BETA’s 70 people (40 targets, 2-second windows, chance 2.5%). Six decoders were fitted at the eight posterior electrodes the site uses and then tested with every electrode read one position over: one row forward, or one column to the left or right. The shifted readings are signals recorded at the neighbouring cap sites; nothing was re-recorded.

One row forward, training-free CCA changed by −17.6 pp and filter-bank CCA by −14.3 pp; one column to the left, by −5.9 pp and −4.3 pp; to the right, by −1.0 pp and −1.2 pp, both unresolved. eTRCA, calibrated per person at the original positions, fell from 83.5% to 41.2%–55.3%, and EEGNet, trained across people, from 56.0% to 28.6%–40.9%. Recalibrated on the shifted cap, eTRCA reached 77.7%–82.5%.

The decoders, shifts, windows and contrasts were prespecified and independently reviewed before any shifted score; an independent audit recomputed every person’s accuracy, the twelve primary intervals and the counts of people from the sealed predictions.

The twelve prespecified contrasts: shifted minus original accuracy, paired by person, decoders fitted at the original positions, 2-second windows. Intervals are 99.6% percentile bootstrap intervals over the 70 people (Bonferroni over the twelve), with the fitted models held fixed.

| Decoder | Original | Shifted | Change (99.6% interval) | Reading | People losing ≥10 pp, of 70 |
| --- | --- | --- | --- | --- | --- |
| Forward one row |  |  |  |  |  |
| [CCA](https://bci.report/methods/cca/) | 63.1% | 45.5% | −17.6 pp — (−22.5 to −13.0 pp) | Lower | 49 |
| [Filter-bank CCA](https://bci.report/methods/fbcca/) | 79.1% | 64.7% | −14.3 pp — (−20.5 to −8.7 pp) | Lower | 38 |
| [eTRCA](https://bci.report/methods/etrca/) | 83.5% | 55.3% | −28.2 pp — (−36.9 to −20.7 pp) | Lower | 54 |
| [EEGNet](https://bci.report/methods/eegnet/) | 56.0% | 40.9% | −15.0 pp — (−22.2 to −8.0 pp) | Lower | 36 |
| Left one column |  |  |  |  |  |
| [CCA](https://bci.report/methods/cca/) | 63.1% | 57.2% | −5.9 pp — (−8.5 to −3.5 pp) | Lower | 19 |
| [Filter-bank CCA](https://bci.report/methods/fbcca/) | 79.1% | 74.7% | −4.3 pp — (−6.7 to −2.2 pp) | Lower | 12 |
| [eTRCA](https://bci.report/methods/etrca/) | 83.5% | 46.5% | −36.9 pp — (−45.9 to −28.6 pp) | Lower | 58 |
| [EEGNet](https://bci.report/methods/eegnet/) | 56.0% | 30.4% | −25.6 pp — (−33.2 to −18.3 pp) | Lower | 50 |
| Right one column |  |  |  |  |  |
| [CCA](https://bci.report/methods/cca/) | 63.1% | 62.1% | −1.0 pp — (−2.9 to +0.7 pp) | Unresolved | 4 |
| [Filter-bank CCA](https://bci.report/methods/fbcca/) | 79.1% | 77.9% | −1.2 pp — (−2.7 to +0.3 pp) | Unresolved | 2 |
| [eTRCA](https://bci.report/methods/etrca/) | 83.5% | 41.2% | −42.2 pp — (−50.8 to −34.2 pp) | Lower | 64 |
| [EEGNet](https://bci.report/methods/eegnet/) | 56.0% | 28.6% | −27.4 pp — (−36.0 to −18.8 pp) | Lower | 49 |

People losing 10 pp or more are counted from each person’s trials, as a description; a change of exactly 10 pp (16 of 160 trials; 48 of 480 for EEGNet, which pools three training seeds) counts.

Mean accuracy over the 70 people, 2-second windows.

| Decoder | Training | Original | Forward one row | Left one column | Right one column |
| --- | --- | --- | --- | --- | --- |
| Deployed: fitted at the original positions |  |  |  |  |  |
| [CCA](https://bci.report/methods/cca/) | none | 63.1% | 45.5% | 57.2% | 62.1% |
| [Filter-bank CCA](https://bci.report/methods/fbcca/) | none | 79.1% | 64.7% | 74.7% | 77.9% |
| [eTRCA](https://bci.report/methods/etrca/) | per person, leave one block out (three training trials per target) | 83.5% | 55.3% | 46.5% | 41.2% |
| Spectral ridge | across people, seven participant folds | 50.7% | 37.4% | 40.2% | 40.4% |
| [EEGNet](https://bci.report/methods/eegnet/) | across people, seven participant folds, from scratch; three training seeds averaged | 56.0% | 40.9% | 30.4% | 28.6% |
| [CBraMod](https://bci.report/methods/cbramod/) | frozen encoder, ridge readout across people, seven participant folds | 33.7% | 24.3% | 18.4% | 17.5% |
| Recalibrated on the shifted cap |  |  |  |  |  |
| [eTRCA](https://bci.report/methods/etrca/) | per person, leave one block out (three training trials per target) | — | 77.7% | 79.3% | 82.5% |
| Spectral ridge | across people, seven participant folds | — | 45.6% | 47.0% | 49.8% |
| [EEGNet](https://bci.report/methods/eegnet/) | across people, seven participant folds, from scratch; three training seeds averaged | — | 42.1% | 49.7% | 55.1% |
| [CBraMod](https://bci.report/methods/cbramod/) | frozen encoder, ridge readout across people, seven participant folds | — | 28.9% | 29.2% | 35.2% |

The neighbour maps: which recorded sites stand in for the eight original ones.

| Shift | Sites read, in the order PO5 PO3 POZ PO4 PO6 O1 OZ O2 | Original sites kept, of eight | Mean read-site displacement |
| --- | --- | --- | --- |
| forward one row | P3 P1 PZ P2 P4 PO3 POZ PO4 | 3 | 3.48 cm |
| left one column | P5 PO5 PO3 POZ O2 PO7 O1 OZ | 6 | 2.82 cm |
| right one column | O1 POZ PO4 PO6 P6 OZ O2 PO8 | 6 | 2.82 cm |

Without the three people flagged by a label-free channel check made before decoding (67 people), every primary mean moves by at most 1.1 pp and keeps its sign.

At the original positions CCA (63.1%), spectral ridge (50.7%) and CBraMod (33.7%) reproduce their published two-second, eight-electrode cells exactly, and filter-bank CCA (79.1%) equals its [core-matrix cell](https://bci.report/protocols/beta-8ch/#core-rows); EEGNet’s mean over three training seeds is 56.0% beside the published one-seed cell, 55.8%, which ran on another device.

Secondary results, each with a nominal 95.0% interval

The secondary results carry no correction across contrasts; their intervals are nominal and support no claim across the family.

### Spectral ridge and CBraMod: shifted minus original

| Decoder | Forward one row | Left one column | Right one column |
| --- | --- | --- | --- |
| Spectral ridge | −13.3 pp — (−16.8 to −10.0 pp) | −10.5 pp — (−12.7 to −8.3 pp) | −10.3 pp — (−13.3 to −7.5 pp) |
| [CBraMod](https://bci.report/methods/cbramod/) | −9.3 pp — (−12.7 to −5.9 pp) | −15.2 pp — (−17.8 to −12.8 pp) | −16.2 pp — (−19.6 to −13.0 pp) |

### Mismatch: deployed minus recalibrated

| Decoder | Forward one row | Left one column | Right one column |
| --- | --- | --- | --- |
| [eTRCA](https://bci.report/methods/etrca/) | −22.5 pp — (−28.8 to −16.7 pp) | −32.7 pp — (−38.8 to −27.0 pp) | −41.3 pp — (−47.1 to −35.7 pp) |
| Spectral ridge | −8.2 pp — (−10.4 to −6.0 pp) | −6.8 pp — (−8.9 to −4.7 pp) | −9.4 pp — (−12.2 to −6.7 pp) |
| [EEGNet](https://bci.report/methods/eegnet/) | −1.2 pp — (−3.6 to +1.4 pp) | −19.3 pp — (−24.0 to −14.8 pp) | −26.4 pp — (−32.0 to −21.0 pp) |
| [CBraMod](https://bci.report/methods/cbramod/) | −4.5 pp — (−6.3 to −2.7 pp) | −10.7 pp — (−12.8 to −8.8 pp) | −17.8 pp — (−21.6 to −14.1 pp) |

Recalibration uses the person’s own shifted trials (eTRCA) or other people recorded with the same shift (ridge, EEGNet, CBraMod); both assume the shift is known in advance, so they show what was lost, not a practical fix.

### Loss remaining after recalibrating

| Decoder | Forward one row | Left one column | Right one column |
| --- | --- | --- | --- |
| [eTRCA](https://bci.report/methods/etrca/) | −5.8 pp — (−8.3 to −3.2 pp) | −4.2 pp — (−5.5 to −3.0 pp) | −0.9 pp — (−1.8 to −0.1 pp) |
| Spectral ridge | −5.1 pp — (−7.5 to −2.8 pp) | −3.7 pp — (−4.9 to −2.5 pp) | −0.9 pp — (−2.0 to +0.3 pp) |
| [EEGNet](https://bci.report/methods/eegnet/) | −13.9 pp — (−18.7 to −9.1 pp) | −6.3 pp — (−8.5 to −4.1 pp) | −0.9 pp — (−2.9 to +1.0 pp) |
| [CBraMod](https://bci.report/methods/cbramod/) | −4.8 pp — (−7.2 to −2.5 pp) | −4.5 pp — (−6.0 to −3.0 pp) | +1.6 pp — (+0.2 to +3.0 pp) |

### Spline doses: interpolated cap minus original

| Decoder | 1 cm | 2 cm | 3 cm |
| --- | --- | --- | --- |
| Forward one row |  |  |  |
| [CCA](https://bci.report/methods/cca/) | −1.9 pp — (−2.8 to −1.0 pp) | −6.7 pp — (−8.3 to −5.1 pp) | −14.9 pp — (−17.7 to −12.1 pp) |
| [Filter-bank CCA](https://bci.report/methods/fbcca/) | −0.9 pp — (−1.7 to −0.1 pp) | −4.3 pp — (−6.0 to −2.7 pp) | −11.9 pp — (−15.6 to −8.3 pp) |
| [eTRCA](https://bci.report/methods/etrca/) | −4.8 pp — (−7.8 to −2.6 pp) | −12.1 pp — (−16.9 to −8.0 pp) | −25.2 pp — (−31.1 to −19.8 pp) |
| Spectral ridge | −1.7 pp — (−3.3 to −0.04 pp) | −4.7 pp — (−7.2 to −2.2 pp) | −8.9 pp — (−12.0 to −5.9 pp) |
| [EEGNet](https://bci.report/methods/eegnet/) | −1.1 pp — (−3.1 to +0.7 pp) | −5.7 pp — (−8.9 to −2.5 pp) | −12.2 pp — (−16.7 to −7.8 pp) |
| [CBraMod](https://bci.report/methods/cbramod/) | −1.7 pp — (−2.7 to −0.6 pp) | −3.5 pp — (−5.5 to −1.5 pp) | −8.3 pp — (−11.3 to −5.3 pp) |
| Left one column |  |  |  |
| [CCA](https://bci.report/methods/cca/) | −1.0 pp — (−1.6 to −0.4 pp) | −3.5 pp — (−4.9 to −2.3 pp) | −7.5 pp — (−9.4 to −5.7 pp) |
| [Filter-bank CCA](https://bci.report/methods/fbcca/) | −0.8 pp — (−1.3 to −0.3 pp) | −2.7 pp — (−3.7 to −1.8 pp) | −5.6 pp — (−7.2 to −4.0 pp) |
| [eTRCA](https://bci.report/methods/etrca/) | −5.8 pp — (−8.2 to −3.8 pp) | −18.5 pp — (−22.7 to −14.5 pp) | −38.7 pp — (−44.6 to −33.0 pp) |
| Spectral ridge | −1.6 pp — (−2.6 to −0.5 pp) | −5.1 pp — (−6.8 to −3.3 pp) | −10.2 pp — (−12.3 to −8.2 pp) |
| [EEGNet](https://bci.report/methods/eegnet/) | −3.5 pp — (−5.5 to −1.5 pp) | −12.5 pp — (−16.6 to −8.6 pp) | −26.2 pp — (−31.8 to −20.8 pp) |
| [CBraMod](https://bci.report/methods/cbramod/) | −2.5 pp — (−3.7 to −1.4 pp) | −8.7 pp — (−10.8 to −6.6 pp) | −15.0 pp — (−17.9 to −12.3 pp) |
| Right one column |  |  |  |
| [CCA](https://bci.report/methods/cca/) | −0.4 pp — (−1.2 to +0.3 pp) | −0.6 pp — (−1.7 to +0.6 pp) | −1.5 pp — (−2.8 to −0.3 pp) |
| [Filter-bank CCA](https://bci.report/methods/fbcca/) | −0.2 pp — (−0.8 to +0.3 pp) | −0.6 pp — (−1.5 to +0.4 pp) | −1.6 pp — (−2.7 to −0.5 pp) |
| [eTRCA](https://bci.report/methods/etrca/) | −4.5 pp — (−6.3 to −2.9 pp) | −16.0 pp — (−19.7 to −12.5 pp) | −38.7 pp — (−44.6 to −32.8 pp) |
| Spectral ridge | −0.8 pp — (−1.8 to +0.2 pp) | −4.1 pp — (−6.1 to −2.4 pp) | −10.7 pp — (−13.4 to −8.1 pp) |
| [EEGNet](https://bci.report/methods/eegnet/) | −2.8 pp — (−4.9 to −0.8 pp) | −14.7 pp — (−19.4 to −10.4 pp) | −31.7 pp — (−37.8 to −25.7 pp) |
| [CBraMod](https://bci.report/methods/cbramod/) | −2.3 pp — (−3.4 to −1.2 pp) | −8.7 pp — (−11.1 to −6.4 pp) | −16.6 pp — (−20.0 to −13.3 pp) |

An interpolated site is a weighted mix of recorded sites, so for decoders fitted to positions the spline doses mix displacement with interpolation and are indicative only.

Rebuilding each original site from the other sites with no shift changes CCA by −1.7 pp, Filter-bank CCA by −1.2 pp, eTRCA by −22.6 pp, Spectral ridge by −2.6 pp, EEGNet by −14.5 pp and CBraMod by −11.7 pp. At the neighbour maps’ angles the spline gave eTRCA +10.2 pp more than the right-hand map and +3.8 pp more on the left.

### 1-second windows: shifted minus original

| Decoder | Forward one row | Left one column | Right one column |
| --- | --- | --- | --- |
| [CCA](https://bci.report/methods/cca/) | −8.9 pp — (−11.1 to −6.8 pp) | −3.0 pp — (−4.4 to −1.7 pp) | −0.3 pp — (−1.4 to +0.9 pp) |
| [Filter-bank CCA](https://bci.report/methods/fbcca/) | −15.2 pp — (−18.1 to −12.4 pp) | −7.1 pp — (−8.5 to −5.6 pp) | −1.2 pp — (−2.7 to +0.2 pp) |
| [eTRCA](https://bci.report/methods/etrca/) | −31.9 pp — (−37.1 to −27.0 pp) | −38.7 pp — (−43.9 to −33.6 pp) | −44.5 pp — (−49.8 to −39.3 pp) |

### Differences in drop: the first decoder’s change minus the second’s, same shift

| Decoder | Forward one row | Left one column | Right one column |
| --- | --- | --- | --- |
| eTRCA − Filter-bank CCA | −13.9 pp — (−21.0 to −7.1 pp) | −32.6 pp — (−38.4 to −27.0 pp) | −41.1 pp — (−46.8 to −35.5 pp) |
| EEGNet − CCA | +2.6 pp — (−1.0 to +6.2 pp) | −19.7 pp — (−24.6 to −15.0 pp) | −26.3 pp — (−32.1 to −20.6 pp) |

Limits of the simulation

The simulation moves the whole cap as one piece with the reference fixed; a real slip can be partial, change contact and impedance, and move the reference too. Backward slips and slides toward one ear are not simulated.

BETA gives template positions, so each person’s true displacement is unknown. The neighbour maps move the read sites a mean of 3.48 cm forward and 2.82 cm sideways and keep 3 and 6 of the eight original sites, so the directions are not compared with each other.

Intervals resample people with the fitted models held fixed; the variance of training is not included.

One dataset and offline 40-target classification, with no latency or online ITR measured; nothing follows about any headset.

The decoders start from very different accuracies, so a change in points is read beside its baseline.

### Data and method credits

BETA · Bingchuan Liu et al. · BETA: A Large Benchmark Database Toward SSVEP-BCI Application (2020), doi:10.3389/fnins.2020.00627. Figshare 12264401 v3; mirror Bingchuan/BETA.

[CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/) · participant consent described non-commercial scientific research · [Bingchuan/BETA ↗](https://huggingface.co/datasets/Bingchuan/BETA)

CBraMod, frozen: code MIT, weights Apache-2.0 as its official model card states; not distributed. [Method page](https://bci.report/methods/cbramod/)

Nakanishi et al. 2018 (ensemble TRCA) · Chen et al. 2015, doi:10.1088/1741-2560/12/4/046008 (filter-bank CCA)

Data source: [reviewed aggregate JSON](https://bci.report/data/electrode-shift-update.json) · schema bci-report-electrode-shift-update-v1.

Methods & limits

## Limits of the two paired comparisons

Sleep · in-ear and scalp

### Six scalp electrodes, not eight

The scalp files also carry two mastoid slots. They were used only in the completeness screen and never entered the model.

### Position, count and hardware move together

The in-ear and scalp configurations differ in placement, geometry and number of electrodes. The difference is descriptive, not an estimate of what the ear hardware alone costs.

### One simple, untuned model

Five log-bandpower features per channel and an L2 logistic regression with no hyperparameter search; a stronger model could score higher on either configuration.

Eyes open and closed · four and sixteen

### A pipeline check

Closing the eyes produces a strong alpha rhythm over the back of the head, which is why four posterior electrodes can carry this task. Concentration, workload and intent detection each need their own test.

### Chosen in software

Four channels selected in software from the 16-electrode wet-cap recordings do not validate a physical four-channel device.

### 19 of 20 recordings

The source has 20 recordings. The authors’ own data loader leaves out recording 07, and this comparison keeps that fixed cohort. Our event audit also flagged overlapping events in that recording, but the authors’ reason for leaving it out is not established.

Filter-bank CCA, CCA and learned heads on CCA’s statistics are also compared with fewer of BETA’s posterior electrodes and with shorter windows, on SSVEP frequencies held out of training: [What lets a decoder answer an SSVEP frequency it never trained on?](https://bci.report/topics/new-ssvep-frequencies/#practical-settings)

Two frozen encoders and a log band-power ridge are also read with some channels zeroed (channels zeroed after preprocessing, not a physical montage), with the head kept or refitted: [Channels zeroed on two published encoders, head kept or refitted →](https://bci.report/topics/pilot-studies/#nsp-components-a)

### EESM23 · Ear-EEG Sleep Monitoring 2023

Dataset: Yousef Rezaei Tabar, Kaare Mikkelsen, Laura Birch, Nelly Shenton, Simon L. Kappel, Astrid R. Bertelsen, Reza Nikbakht, Hans O. Toft, Chris H. Henriksen, Martin C. Hemmsen, Mike L. Rank, Marit Otto and Preben Kidmose · Ear-EEG Sleep Monitoring 2023 (EESM23), OpenNeuro ds005178 v1.0.0, doi:10.18112/openneuro.ds005178.v1.0.0. Study: Kaare Bjarke Mikkelsen, Yousef Rezai Tabar, Laura Rævsbæk Birch, Simon Lind Kappel, Christian Bech Christensen, Lars Dalskov Mosgaard, Marit Otto, Martin Christian Hemmsen, Mike Lind Rank and Preben Kidmose · Ear-EEG sleep monitoring data sets, Scientific Data 12, 301 (2025), doi:10.1038/s41597-025-04579-8. Processed export: Zachary1150/EESM23-Processed on Hugging Face, which did not create the original cohort.

Consent covered the study; the data were released after the GDPR office of Region Midt judged them fully anonymized. Only cohort aggregates appear here.

[OpenNeuro dataset ↗](https://doi.org/10.18112/openneuro.ds005178.v1.0.0) · [Scientific Data paper ↗](https://doi.org/10.1038/s41597-025-04579-8) · [Processed export ↗](https://huggingface.co/datasets/Zachary1150/EESM23-Processed/tree/aed9526a1a56c6644b13a8ef95e03e6a6729a87f) · [CC0-1.0](https://creativecommons.org/publicdomain/zero/1.0/)

### EEG Alpha Waves dataset

Grégoire Cattan, Pedro L. C. Rodrigues and Marco Congedo · EEG Alpha Waves dataset, Zenodo, doi:10.5281/zenodo.2605110. Report: Grégoire Cattan, Pedro Luiz Coelho Rodrigues and Marco Congedo · EEG Alpha Waves Dataset, research report, GIPSA-lab, Grenoble, 2018, hal-02086581.

According to the primary report, every participant gave written informed consent covering the experimental process, the data management procedures and the right to withdraw at any moment. The report names no ethics committee. Only cohort aggregates appear here.

[Zenodo record ↗](https://doi.org/10.5281/zenodo.2605110) · [HAL report ↗](https://hal.science/hal-02086581/document) · [Authors’ code ↗](https://github.com/plcrodrigues/py.ALPHA.EEG.2017-GIPSA) · [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)

Data source: [reviewed aggregate JSON](https://bci.report/data/evidence-update.json) · schema bci-report-evidence-update-v1.

Keep exploring

Transfer

- [— Sensor transfer **Dry vs. wet electrodes**](https://bci.report/topics/dry-vs-wet/)
- [— Context transfer **Screen to VR**](https://bci.report/topics/screen-to-vr/)
- [— Montage **Fewer electrodes**](https://bci.report/topics/fewer-electrodes/)
- [— Motion robustness **On the move**](https://bci.report/topics/on-the-move/)
- [— Session transfer · same person **Later sessions**](https://bci.report/topics/later-sessions/)

Adapting models

- [— Calibration budget **How much calibration?**](https://bci.report/topics/calibration-budget/)
- [— Model adaptation **Which part to update?**](https://bci.report/topics/model-adaptation/)
- [— Representation controls **Does pretraining help?**](https://bci.report/topics/does-pretraining-help/)
- [— Shared encoder · route 2 **One model, several questions**](https://bci.report/topics/shared-encoder/)
- [— Questions in language · route 3 **Questions in language**](https://bci.report/topics/questions-in-language/)
- [— Route 3 follow-ups **New SSVEP frequencies**](https://bci.report/topics/new-ssvep-frequencies/)

Reliability & clinical

- [— Abstention · decision research **When not to act**](https://bci.report/topics/when-not-to-act/)
- [— Sleep staging · simple baselines **Sleep-stage balance**](https://bci.report/topics/sleep-staging/)
- [— Clinical research **Clinical groups**](https://bci.report/topics/clinical-groups/)
- [— Data quality and reproducibility **Checked end to end**](https://bci.report/topics/data-quality/)

Other signals & pilots

- [— Other signals · human and macaque **Beyond scalp EEG**](https://bci.report/topics/beyond-scalp-eeg/)
- [— Pilot studies · small cohorts **Pilot studies**](https://bci.report/topics/pilot-studies/)

[All questions and the transfer-coverage map →](https://bci.report/topics/)

## Cite this page

BCI Report (2026). *Can fewer electrodes, or electrodes in the ear, match a full scalp montage?* https://bci.report/topics/fewer-electrodes/

Figures from releases [`electrode-shift-update-20261106`](https://bci.report/releases/#electrode-shift-update-20261106) (2026-11-06), [`eegpt-core-update-20261013`](https://bci.report/releases/#eegpt-core-update-20261013) (2026-10-13), [`foundation-models-update-20261004`](https://bci.report/releases/#foundation-models-update-20261004) (2026-10-04), [`evidence-update-20260922`](https://bci.report/releases/#evidence-update-20260922) (2026-09-22), [`research-preview-20260920`](https://bci.report/releases/#research-preview-20260920) (2026-09-20). Cite the upstream datasets as well: their credits are on this page.

Every release is archived on Zenodo: [doi:10.5281/zenodo.23123296](https://doi.org/10.5281/zenodo.23123296). [BibTeX for the site and its releases →](https://bci.report/api/#cite-heading) · [CITATION.cff ↗](https://raw.githubusercontent.com/twu3202/bci-report/main/CITATION.cff)

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Markdown copy of https://bci.report/topics/fewer-electrodes/, generated from the published page. Figures are aggregate results; terms of use: https://bci.report/data-use/