The EEG-fNIRS-based Handwriting Trajectory Dataset is the data release of an imagined-handwriting competition, posted on Hugging Face by the account lasfk: raw EEG and fNIRS recordings with trial tables, twenty labelled training participants and ten test participants, and four class labels mapped from released event codes. The card names no organisers and no paper; a participating team's challenge paper identifies the competition as the ACM MM 2026 Multimodal Brain-Computer Interface Grand Challenge. The card declares CC BY-NC-SA 4.0, behind a gate. Only the EEG of five training participants is used here, and the official test data are not.
Also known aslasfk/EEG-fNIRS-based-Handwriting-Trajectory-Dataset · ACM MM 2026 Multimodal Brain-Computer Interface Grand Challenge
A person left out of training · four released coded labels
Balanced accuracy
26.81%26.30%–27.20%
5
Missingness mask only (no EEG)reference or comparator, not a decoding model
A person left out of training · four released coded labels
Balanced accuracy
24.99%Interval not published: with five people, its bounds would show individual people’s scores.
5
Elapsed time / trial order (no EEG)reference or comparator, not a decoding model
A person left out of training · four released coded labels
Balanced accuracy
25.44%24.03%–26.79%
5
Mask + time (no EEG)reference or comparator, not a decoding model
A person left out of training · four released coded labels
Balanced accuracy
25.01%23.90%–26.12%
5
Training majorityreference or comparator, not a decoding model
A person left out of training · four released coded labels
Balanced accuracy
25.00%Chance for every person, by construction.
5
Mask-aware relative EEG + logistic regression
Minus the missingness mask only, the same people
Paired difference
+1.82 pp+1.30–+2.21 pp
5
Mask-aware relative EEG + logistic regression
Minus elapsed time / trial order, the same people
Paired difference
+1.37 pp−0.12–+3.07 ppThe interval crosses zero: not separated from this control.
5
Mask-aware relative EEG + logistic regression
Minus mask + time, the same people
Paired difference
+1.80 pp+0.43–+3.05 pp
5
Training-label shuffles · Mask-aware relative EEG + logistic regressionreference or comparator, not a decoding model
A person left out of training · four released coded labels
Balanced accuracy
25.20%The mean of 5 shuffles, whose means ranged from 23.85% to 26.69%: an observed range, not a confidence interval.
5
5 people and 1,618 trials from a reused development cohort, whose earlier scores were seen before this design: a quality-informed diagnostic, not independent confirmation. Every trial keeps 27 to 38 of 59 recorded channels under a fixed numerical availability rule, which is not certified clean EEG. Four released coded labels, not verified characters or trajectories: not handwriting recognition. The intervals are descriptive, resampling the five people over overlapping folds, not population confidence intervals. The official test data were not used. No fNIRS, fusion, online, physiological-origin, competition-standing or medical claim; outside every leaderboard.
Source and licence
EEG-fNIRS-based Handwriting Trajectory Dataset
Creditlasfk on Hugging Face · EEG-fNIRS-based Handwriting Trajectory Dataset, revision 4036680b5fb0015472d55589da2f34c5a87a3c10, CC BY-NC-SA 4.0 as declared on the dataset card, gated access. Derived analysis by BCI Report; not endorsed by the uploader.
BCI Report does not redistribute any recording. These are aggregate measurements computed by BCI Report under the licence above; the data remain their creators’.
Ethics and consent
No ethics-approval or informed-consent statement was found, on the dataset card or in a participating team’s challenge paper; the aggregates are published with that gap stated, by the owner’s decision. The full statement on the data-use page →
BCI Report