Spectral smoothing explains the offline gain from cross-order single-trial MEG movement decoding for non-invasive BCI

OBJECTIVE: Acceleration, velocity, and position can be combined in non-invasive movement decoding, but in this dataset only acceleration is measured; velocity and position are deterministic transforms obtained by integration. We therefore tested whether cross-order decoder fusion improves estimation in position space and whether any apparent gain remains after matching the spectral consequences of those transforms. Approach. We analysed single-trial MEG recordings from nine participants (18 runs; 2,160 trials) using a deterministic five-fold split. The primary outcome was within-trial Pearson correlation in position-residual space after removing trial-invariant trajectory. Acceleration-, velocity-, and position-based routes were fused and compared with same-order bagging, decorrelation-matched controls, and a bandwidth-matched pseudo-fusion of filtered copies of the direct position decoder. We additionally tested implementation sensitivity, residual-space R², non-positive-lag and causal pipelines, artefact sensitivity, multi-task architectures, and between-run transfer. Main results. Cross-order fusion exceeded same-order bagging by +0.0389 per-trial r (95% CI [+0.0288, +0.0499]; 9/9 participants). The bandwidth-matched pseudo-fusion exceeded bagging by +0.0424 and reproduced 109.0% of the offline margin, leaving E_cross - E_spec = -0.0035 (95% CI [-0.0105, +0.0032]; upper one-sided 95% bound +0.003). A nested-cross-validated smoothed single decoder likewise matched the ensemble. On residual-space R² with a training-fitted gain, the ordering reversed (E_bag -0.099, E_cross -0.120), showing that the correlation gain did not improve amplitude fidelity. With non-positive lags only, bandwidth matching explained 79% of the margin; under a fully causal zero-look-ahead pipeline, a smaller residual persisted and its mechanism was underpowered to resolve. Between-run transfer within the same visit retained only about 18% of within-run accuracy. Significance. In the primary offline regime, cross-order fusion gains can arise from integration-induced spectral smoothing without additional cross-order neural information. A bandwidth-matched surrogate should precede mechanistic interpretation when decoder routes are linked by deterministic transforms. In the causal regime, the advantage remains available online, but its mechanism remains unresolved.

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Publication Details

Journal
Journal of Neural Engineering
Published
2026-10-29
DOI
https://doi.org/10.1088/1741-2552/aebb69
Primary Topic
EEG and Brain-Computer Interfaces
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article
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article

Spectral smoothing explains the offline gain from cross-order single-trial MEG movement decoding for non-invasive BCI

Youmin Shin
1 links
Journal of Neural Engineering
EEG and Brain-Computer Interfaces
article

Spectral smoothing explains the offline gain from cross-order single-trial MEG movement decoding for non-invasive BCI

Youmin Shin
article en

Abstract

OBJECTIVE: Acceleration, velocity, and position can be combined in non-invasive movement decoding, but in this dataset only acceleration is measured; velocity and position are deterministic transforms obtained by integration. We therefore tested whether cross-order decoder fusion improves estimation in position space and whether any apparent gain remains after matching the spectral consequences of those transforms. Approach. We analysed single-trial MEG recordings from nine participants (18 runs; 2,160 trials) using a deterministic five-fold split. The primary outcome was within-trial Pearson correlation in position-residual space after removing trial-invariant trajectory. Acceleration-, velocity-, and position-based routes were fused and compared with same-order bagging, decorrelation-matched controls, and a bandwidth-matched pseudo-fusion of filtered copies of the direct position decoder. We additionally tested implementation sensitivity, residual-space R², non-positive-lag and causal pipelines, artefact sensitivity, multi-task architectures, and between-run transfer. Main results. Cross-order fusion exceeded same-order bagging by +0.0389 per-trial r (95% CI [+0.0288, +0.0499]; 9/9 participants). The bandwidth-matched pseudo-fusion exceeded bagging by +0.0424 and reproduced 109.0% of the offline margin, leaving E_cross - E_spec = -0.0035 (95% CI [-0.0105, +0.0032]; upper one-sided 95% bound +0.003). A nested-cross-validated smoothed single decoder likewise matched the ensemble. On residual-space R² with a training-fitted gain, the ordering reversed (E_bag -0.099, E_cross -0.120), showing that the correlation gain did not improve amplitude fidelity. With non-positive lags only, bandwidth matching explained 79% of the margin; under a fully causal zero-look-ahead pipeline, a smaller residual persisted and its mechanism was underpowered to resolve. Between-run transfer within the same visit retained only about 18% of within-run accuracy. Significance. In the primary offline regime, cross-order fusion gains can arise from integration-induced spectral smoothing without additional cross-order neural information. A bandwidth-matched surrogate should precede mechanistic interpretation when decoder routes are linked by deterministic transforms. In the causal regime, the advantage remains available online, but its mechanism remains unresolved.

Journal of Neural Engineering
Seoul School of Integrated Sciences and Technologies (KR)
Openalex Percentile: Top 14%
EEG and Brain-Computer Interfaces
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