BCI ReportOptical values checked
48,502,600
Every channel at each of the 242,513 time points of the 60 recordings: 12,000 recording-channels, no recording excluded.
Other signals · human fNIRS and macaque intracortical spiking
Do simple baselines work on signals other than scalp EEG?
Three locally executed analyses, each with its own species, signal and metric. Human fNIRS: executed finger and foot tapping, classified in a person left out of training. One macaque’s motor-cortex spiking: finger velocity, decoded within one session. Human fNIRS recordings: a numerical check of every optical value. Two more sources are prepared, not scored.
Measured on: Bak et al. fNIRS finger- and foot-tapping dataset; MC_RTT (Neural Latents Benchmark); OpenNeuro ds007420 · multi-distance fNIRS
Short answer
Partly, and each result stands on its own. On human fNIRS, a fixed shrinkage LDA told right-hand, left-hand and foot tapping apart in people it never saw: with each of 30 people left out in turn, its participant-macro balanced accuracy was 66.8% (descriptive 95% interval 60.6% to 72.9%), well above the training prior’s 33.3%. These were executed movements, not motor imagery, and systemic or motion effects are not excluded. On one macaque’s motor-cortex spiking, a ridge decoder recovered part of the finger velocity on held-out trials of the same session: R² 0.240 against −0.00175 for the training mean, and a lower root-mean-square error, but a higher mean absolute error (0.038 against 0.033 m/s). It is not an official Neural Latents Benchmark score. On a second fNIRS dataset, all 48,502,600 optical values are finite and positive: numerical coverage, which says nothing about signal quality or decoding.
Human · fNIRS · executed movement · Bak et al.
fNIRS: which movement did a person the classifier never saw make?
Locally executed experiment · offline · leave one whole person out
30 people each tapped the fingers of the right hand, the fingers of the left hand or a foot when cued: executed movement, not motor imagery. That is 2,250 trials, 75 per person and 25 of each class. The signals are the supplied oxy- and deoxyhemoglobin series, 40 channels in mM·cm as delivered, not absolute concentrations. Each person is held out once: a fixed classifier is fitted on the other 29 people’s 2,175 trials and predicts all 75 of the held-out person’s, from the first 15 s after each task onset. The comparator is the training prior, which predicts the most frequent training class every time.
Shrinkage LDA95% interval 60.6%–72.9%
66.8%
Training prior (comparator)no interval: every person gets the same score
33.3%
| Decoder | Balanced accuracy | Recall · right hand | Recall · left hand | Recall · foot |
|---|---|---|---|---|
| Shrinkage LDAstandardization and shrinkage LDA, fitted on the training people only | 66.8%95% interval 60.6%–72.9% | 69.2% | 63.6% | 67.7% |
| Training prior (comparator)predicts right-hand tapping every time: the classes are balanced, and ties go to the fixed class order | 33.3%no interval: every person gets the same score | 100.0% | 0.0% | 0.0% |
66.8%Shrinkage LDA, participant-macro balanced accuracy, 30 people each held out once; the training prior scores 33.3%
Well above the training prior, in people the classifier never saw
Its descriptive interval, 60.6% to 72.9%, lies wholly above chance. The interval resamples the fixed participant scores, 2,000 draws: it describes variation between people given the fitted folds, and because the folds share training people it is not a refit bootstrap and not an independent clinical confidence interval.
It shows neither online control nor a device’s response time, because the supplied series’ upstream preprocessing may use future samples. Nor does it show a brain-only signal or cortical localization: task-correlated systemic or motion effects are not excluded, and the supplied files carry no probe coordinates. The protocol is this site’s own: it does not reproduce the authors’ within-person scores.
Data: Sujin Bak, Jinwoo Park, Jaeyoung Shin and Jichai Jeong, doi:10.6084/m9.figshare.9783755.v2, CC BY 4.0. Dataset page → · Credit, licence and consent ↓
Independently audited: every input file hash, the marker-to-label order, whole-person folds, training-only scaling, the comparator’s tie rule and prediction alignment checked, and the class recalls, the mean and the interval recomputed without the frozen scoring code. The features were not regenerated from the raw channels, and the classifier was not refit.
Macaque · intracortical spiking · MC_RTT (Neural Latents Benchmark)
Spikes: how much finger velocity does a linear decoder recover within one session?
Locally executed experiment · offline · one macaque, one session · not an official Neural Latents Benchmark score
One rhesus macaque made self-paced reaches to targets on a grid while an electrode array in primary motor cortex recorded sorted units, in one session. A fixed ridge decoder reads the spike counts of the 98 held-in neurons (the benchmark’s 32 held-out neurons are not inputs) in 20 ms bins, with 160 ms of trailing history, and predicts the x/y finger velocity 140 ms later. It is fitted on the session’s stored 810 training trials and scored on its 270 validation trials, interleaved with them in time: 12,960 and 4,320 rows, none excluded. The comparator is the training mean: the training rows’ mean velocity, predicted for every row.
Ridge on spike-count history · mean of x and y
0.240
Ridge on spike-count history · x
0.202
Ridge on spike-count history · y
0.278
Training mean (comparator) · mean of x and y
−0.00175
Training mean (comparator) · x
−0.000104
Training mean (comparator) · y
−0.00340
Ridge on spike-count history
0.056
Training mean (comparator)
0.064
Ridge on spike-count history
0.038
Training mean (comparator)
0.033
| Metric | Ridge on spike-count history | Training mean (comparator) | Better on this metric |
|---|---|---|---|
| R², mean of x and yunitless; higher is better | 0.240 | −0.00175 | The ridge |
| R², xunitless; higher is better | 0.202 | −0.000104 | The ridge |
| R², yunitless; higher is better | 0.278 | −0.00340 | The ridge |
| RMSE (m/s)root-mean-square error; lower is better | 0.056 | 0.064 | The ridge |
| MAE (m/s)mean absolute error; lower is better | 0.038 | 0.033 | The training mean: the ridge’s error is larger |
0.240R², mean of x and y, ridge on spike-count history; the training mean scores −0.00175
A lower squared error, but a higher absolute error
The ridge lowers the root-mean-square error from 0.064 to 0.056 m/s, but its mean absolute error is higher, 0.038 against 0.033 m/s: it is not better on every metric, and both directions are shown. No interval is reported: the validation rows are temporally correlated samples from one animal in one session, not independent subjects.
One macaque and one session, with training and validation trials interleaved: no cross-day, cross-animal or human evidence, and with one animal every figure is that animal’s. A separate supervised diagnostic on the stored trials: not an official Neural Latents Benchmark score, not co-smoothing and not a latent-model comparison. The velocity labels were derived offline from finger position, and averaging within each bin differs from the benchmark’s own resampling. Offline: not a real-time or closed-loop controller.
Data: Joseph E. O’Doherty, Mariana M. B. Cardoso, Joseph G. Makin and Philip N. Sabes (Sabes Lab, UCSF), prepared for the Neural Latents Benchmark, DANDI 000129 · 0.241017.1444, CC BY 4.0. Animal data: collected under procedures approved by institutional animal care and use committees; informed consent does not apply. Dataset page → · Credit, licence and consent ↓
Independently audited: every feature and target row reconstructed independently of the code that produced it, the ridge coefficients checked against the normal equations, and the training-only normalization, prediction alignment and every metric recomputed. The stored velocity itself, which the dataset derives offline from finger position, was not checked.
Human · fNIRS · ds007420 · counts only
Optical recordings: are the values numerically usable?
Locally executed check · label-independent · counts only
12 people, 60 recordings of resting state, left- and right-hand ball squeezing and instructed head and face movements, each with 200 optical channels: raw intensity at 760 and 850 nm for each source–detector pair. Every optical value of every recording was checked without labels: is it finite, is it positive, and is any channel exactly constant?
Finite and positive
48,502,600
Finite: 48,502,600, 100.0%. Positive: 100.0%. The denominator is every optical value.
Zero · negative · non-finite
0 · 0 · 0
And 0 exactly constant channels: none whose samples are all finite and exactly equal, with no tolerance.
Coverage counts only. They say nothing about signal-to-noise, scalp coupling, saturation, motion, wavelength-pair integrity, physiological plausibility, neural origin, task validity or decoding: positive, non-constant values alone cannot establish signal quality.
The classifier is held, so no score exists. The numeric task codes have no verified lookup to hands or movements, and the event durations disagree: 1 s in the events table, 10 s in the optical files and 5 s in the cited paper. The 17 recordings held for their unverified auxiliary motion-sensor clocks stay held: optical coverage does not repair them.
Data: Shibo Zhou, De’Ja Rogers, David A. Boas, Meryem A. Yücel and Yuanyuan Gao, OpenNeuro ds007420 · 1.0.5, CC0. Dataset page → · Credit, licence and consent ↓
Independently audited: every channel count reconciled against the recording totals and the aggregate, and the code and its synthetic tests reviewed. The raw-signal pass was not repeated.
Status only · no score
Not yet evaluated
Two more sources are prepared for a decoder and not scored, and the ds007420 classifier is held. The counts below describe what was prepared from the public files; none is a result.
FALCON H2 · human intracortical handwriting
Prepared, not scored. Human, one participant · intracortical spiking · attempted handwriting of cued sentences, across recording days. DANDI 000950 · 0.241029.1403, CC BY 4.0.
No model has been fitted. The public release has 47 files over 26 recording dates, with 192 channels; its held-in calibration split has 671 trials. All 42 public minival trials were found inside those calibration trials, so a leakage-safe diagnostic must first remove them from training. That leaves 629 training trials, which carry 31 symbols (32 classes for a CTC decoder, with the blank). The 15 held-out calibration trials are inputs for few-shot recalibration, not independent evaluation data.
The minival trials were inspected to find the overlap, so a later diagnostic on them would be exposed, not blinded, and would not be the official FALCON cross-day score.
Rabut et al. · human functional ultrasound through a cranial window
Prepared, not analyzed. Human, one participant · functional ultrasound through an acoustically transparent cranial window · drawing, guitar playing, and speech or reading, against rest. doi:10.22002/f3y3k-em558, CC BY-NC 4.0.
Frame labels, timestamps and image axes are verified; no Doppler value has been read and no decoder has been fitted. Prepared: 9 runs of one person, not of nine participants — 7 drawing, 1 guitar and 1 mixed speech and reading — with 2,880 frames, 1,800 at rest and 1,080 during activity.
Only rest against activity is verified; the speech and reading codes of the mixed run have no verified individual meaning. Measured timestamps are used, because the nominal block durations in the run labels disagree with the measured frame timing. Drawing, guitar and speech or reading are different paradigms and are not pooled into one task. The data are licensed for non-commercial use only.
ds007420 · fNIRS classifier · No score. Its numeric task codes have no verified lookup to hands or movements, and its event durations disagree between the events table, the optical files and the cited paper, so no classifier was scored. Its optical coverage counts are published. Its row in the holds register →
Methods & limits
What these results can and cannot say
How each result was produced
Human fNIRS, Bak et al.
The supplied 40 hemodynamic series. For each trial, the channel mean in three consecutive 5 s windows from task onset to 15 s, each minus the mean of the second before onset: 120 features, with no filtering beyond the supplied series. Standardization and shrinkage LDA (lsqr solver, automatic shrinkage, empirical training priors) fitted on the training people only, with no channel, feature or hyperparameter search; 30 folds, each leaving one whole person out.
Macaque spiking, MC_RTT
Spike counts of the held-in neurons in 20 ms bins, smoothed over 3 trailing bins, with 6 history lags: 588 features, reset at every trial and observation gap. The target is the mean stored x/y finger velocity in a bin 140 ms ahead, 16 bins kept from each 600 ms trial. Standardization fitted on the training rows, then ridge regression (alpha 10, Cholesky solver, with intercept), training-constant features dropped, no hyperparameter search. Only the training file is used, with its stored training and validation trials; the test file was not opened.
Human fNIRS coverage, ds007420
Every optical value of the 60 recordings read once, without labels or events. Shares use every optical value as the denominator; a constant channel is one whose samples are all finite and exactly equal, with no tolerance.
What the interval covers
Only the fNIRS result has an interval: a descriptive 95% percentile interval from 2,000 resamples, with replacement, of the 30 fixed participant scores. It describes variation between people given the fitted folds; because the folds share training people, it is not a refit bootstrap and not an independent clinical confidence interval. The MC_RTT figures have none, because its validation rows are correlated samples from one animal in one session, and the ds007420 figures are counts.
Limits that hold for all three
- Locally executed experiments, not scores reported in a paper or on a benchmark leaderboard. Each protocol is this site’s own and reproduces no author’s benchmark.
- Nothing pooled, nothing ranked. The species, signals, tasks and metrics differ, so the three results share no scale and no ranking; none is compared with a scalp-EEG result, and none joins the eight-protocol core matrix.
- Offline only: no online or closed-loop control, device, clinical or deployment claim.
- Fixed before scoring: no channel, feature or hyperparameter search, and no score here was used to choose a setting.
- Audit scope. Each result passed an independent audit; the fNIRS features were not regenerated from the raw channels and its classifier was not refit, MC_RTT’s stored velocity was not re-derived, and the ds007420 raw-signal pass was not repeated.
Not published here
Per-person, per-recording, per-channel, per-trial, per-fold and per-row values of any kind; the confusion matrices; participant, recording, file and session identifiers and recording dates; signals, features, predictions, model coefficients, normalization statistics, cue texts, labels, timestamps and images; measured compute; demographics; and any score for FALCON H2, the functional-ultrasound recordings or a ds007420 classifier, none of which exists. Why so little is published for one animal and for small cohorts: small cohorts, on the data-use page →
Credits, licences, and consent or animal care
Bak 2019 fNIRS dataset · unilateral finger and foot tapping
Sujin Bak, Jinwoo Park, Jaeyoung Shin and Jichai Jeong · Open Access fNIRS Dataset for Classification of Unilateral Finger- and Foot-Tapping, figshare, doi:10.6084/m9.figshare.9783755.v2, CC BY 4.0. Paper: Sujin Bak, Jinwoo Park, Jaeyoung Shin and Jichai Jeong, Open-Access fNIRS Dataset for Classification of Unilateral Finger- and Foot-Tapping, Electronics 8(12), 1486 (2019), doi:10.3390/electronics8121486. Derived analysis by BCI Report; not endorsed by the authors.
Ethics and consent, as the source states them: Approved by the Korea University Institutional Review Board (KUIRB-2019-0254-01); the experiments followed the criteria of the Declaration of Helsinki. The participants' written consent, approved by that board, was submitted before data recording. Read from an Internet Archive capture of the publisher’s article page, because the publisher refused automated requests.
Dataset record ↗ · Paper ↗ · CC BY 4.0 · Statement read from ↗
MC_RTT · macaque motor cortex during self-paced reaching (Neural Latents Benchmark)
O'Doherty, Joseph (2024) MC_RTT: macaque motor cortex spiking activity during self-paced reaching (Version 0.241017.1444) [Data set]. DANDI archive. https://doi.org/10.48324/dandi.000129/0.241017.1444, CC BY 4.0. Recorded by Joseph E. O'Doherty, Mariana M. B. Cardoso, Joseph G. Makin and Philip N. Sabes in the Sabes Lab at UCSF (Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology, Zenodo, doi:10.5281/zenodo.3854034; procedure: Makin et al., Journal of Neural Engineering 15(2), 026010 (2018), doi:10.1088/1741-2552/aa9e95). Prepared for the Neural Latents Benchmark: Felix Pei, Joel Ye et al., Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity, NeurIPS 2021 Track on Datasets and Benchmarks, arXiv:2109.04463. Derived analysis by BCI Report; not endorsed by the authors.
Animal care, as the Neural Latents Benchmark paper states it: Collected under procedures and experiments approved by the Institutional Animal Care and Use Committees at the respective institutions; MC_RTT was collected in the Sabes Lab at UCSF. Informed consent: not applicable, animal data.
Dataset record ↗ · Paper ↗ · CC BY 4.0 · statement read from the paper
ds007420 · multi-distance fNIRS during ball squeezing and motion-artifact tasks
Shibo Zhou, De’Ja Rogers, David A. Boas, Meryem A. Yücel and Yuanyuan Gao · Open-access Multi-Distance fNIRS Dataset Generated During Ball-Squeezing and Motion-Artifact Induction Tasks, OpenNeuro, doi:10.18112/openneuro.ds007420.v1.0.5, CC0. The dataset asks to acknowledge: Gao, Y., Rogers, D., von Lühmann, A., Ortega-Martinez, A., Boas, D.A., & Yücel, M.A. (2023). Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density functional near-infrared spectroscopy. Neurophotonics, 10(2), 025007. https://doi.org/10.1117/1.NPh.10.2.025007. Derived analysis by BCI Report; not endorsed by the authors.
Ethics and consent, as the source states them: Approved by the Institutional Review Board of Boston University (Protocol #4502, as the dataset description records it). All participants provided written informed consent to take part in the study.
Dataset record ↗ · Paper ↗ · CC0-1.0 · Statement read from ↗
FALCON H2 · human intracortical handwriting
Fan, Chaofei; Hahn, Nick; Kamdar, Foram; Avansino, Donald; Wilson, Guy; Hochberg, Leigh; Shenoy, Krishna V; Henderson, Jaime; Willett, Frank (2024) FALCON Benchmark H2: Human Handwriting iBCI (Version 0.241029.1403) [Data set]. DANDI archive. https://doi.org/10.48324/dandi.000950/0.241029.1403, CC BY 4.0. Described in: Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication, NeurIPS 2023. Part of the FALCON benchmark. Derived analysis by BCI Report; not endorsed by the authors.
Ethics and consent, as the source states them: Permission from the Institutional Review Boards of Stanford University (protocol #20804); the BrainGate2 pilot trial ran under FDA Investigational Device Exemption #G090003 (ClinicalTrials.gov NCT00912041). The participant gave informed consent and was enrolled in the BrainGate2 trial.
Rabut et al. human functional ultrasound through a cranial window
Claire Rabut, Sumner Norman, Whitney Griggs, Kay Jann, Jonathan Russin, Vasileios Christopoulos, Charles Liu, Richard A. Andersen and Mikhail G. Shapiro · Dataset for: A window to the brain: ultrasound imaging of human neural activity through an acoustically transparent cranial prosthetic, CaltechData, doi:10.22002/f3y3k-em558, CC BY-NC 4.0. Paper: Claire Rabut, Sumner L. Norman, Whitney S. Griggs, Jonathan J. Russin, Kay Jann, Vasileios Christopoulos, Charles Liu, Richard A. Andersen and Mikhail G. Shapiro, Functional ultrasound imaging of human brain activity through an acoustically transparent cranial window, Science Translational Medicine 16, eadj3143 (2024), doi:10.1126/scitranslmed.adj3143. Derived analysis by BCI Report; not endorsed by the authors.
Ethics and consent, as the source states them: Approved by the Institutional Review Boards of the University of Southern California, Caltech (IR19-0902) and Rancho Los Amigos National Rehabilitation Hospital. The participant's consent was obtained.
Dataset record ↗ · Paper ↗ · CC BY-NC 4.0 · Statement read from ↗
Derived analyses by BCI Report, aggregates only; not endorsed by the data’s authors, and no raw data are redistributed.
Data source: beyond-scalp-update.json · schema bci-report-beyond-scalp-update-v1.
Cite this page
BCI Report (2026). Do simple baselines work on signals other than scalp EEG? https://bci.report/topics/beyond-scalp-eeg/
Figures from release beyond-scalp-update-20261012 (2026-10-12). Cite the upstream datasets as well: their credits are on this page.
Every release is archived on Zenodo: doi:10.5281/zenodo.23123296. BibTeX for the site and its releases → · CITATION.cff ↗