Evidence for decoder-label-regime-dependent effects of complementary leaky integrate-and-fire temporal-state decoding in motor-imagery EEG
Abstract
BackgroundMotor-imagery electroencephalography often provides limited subject-specific supervision. This study tested whether complementary leaky integrate-and-fire (CLIF)-family temporal-state decoding shows a decoder-level advantage after the EEG representation is fixed, how that advantage changes with decoder-label availability, and whether hard binary spikes are required.MethodsANN-SEW, the artificial neural network comparator with spike-element-wise (SEW) residual topology, and SEW-CLIF received identical frozen attention-based temporal convolutional network (ATCNet) and filter-bank convolutional network (FBCNet) representations. The evidence chain combined BCI Competition IV Dataset 2a (BCI2a) development, retrospective OpenBMI replication, an internally pre-access-held-out BCI Competition IV Dataset 2b (BCI2b) evaluation, cross-entropy-only label-budget analyses, and a separate internally pre-access-locked BNCI2015-001 Session-A-to-Session-B evaluation. Additional robustness analyses included shared whole-sequence readout for parameter-matched gated recurrent unit (GRU) and temporal convolutional network (TCN) decoders, exact-gradient sigmoid-gated Soft-CLIF, equal optimizer-update budgets, a within-BCI2a binary sensitivity analysis, and computational profiling.ResultsUnder cross-entropy-only training, SEW-CLIF exceeded ANN-SEW by 10.082 percentage points (pp) on BCI2a and 5.470 pp. on OpenBMI, while full-budget BCI2b remained near parity (+0.181 pp). The ANN-relative gap decreased as decoder labels increased and persisted under equal-update training. At 25 labels/class, exact-gradient Soft-CLIF exceeded ANN-SEW by 11.085, 8.479, and 5.809 pp. on BCI2a, BCI2b, and OpenBMI, respectively. With matched whole-sequence readout, hard SEW-CLIF was broadly comparable to GRU-Stat and TCN-Stat on BCI2a and BCI2b and modestly higher on OpenBMI. The internally pre-access-locked BNCI2015-001 evaluation showed the same attenuation, with a slope of −2.319 pp. per label doubling (95% CI, −4.118 to −0.604).ConclusionCLIF-family temporal-state dynamics show a reproducible decoder-label-regime effect under frozen supervised representations. Hard forward discretization is not required for the ANN-relative effect, and matched temporal readout substantially reduces differences between CLIF and other continuous temporal decoders.
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BibTeX
@article{sha2026evidence,
title = {{Evidence for decoder-label-regime-dependent effects of complementary leaky integrate-and-fire temporal-state decoding in motor-imagery EEG}},
author = {Yijun Sha and Jiangdi Xu and Fengming An and Xiangyong Kong and Dong Zhang},
journal = {Frontiers in Neuroscience},
year = {2026},
month = nov,
doi = {10.3389/fnins.2026.1966366},
url = {https://www.frontiersin.org/articles/10.3389/fnins.2026.1966366},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Evidence for decoder-label-regime-dependent effects of complementary leaky integrate-and-fire temporal-state decoding in motor-imagery EEG
AU - Yijun Sha
AU - Jiangdi Xu
AU - Fengming An
AU - Xiangyong Kong
AU - Dong Zhang
PY - 2026
DA - 2026/11/02
JO - Frontiers in Neuroscience
DO - 10.3389/fnins.2026.1966366
UR - https://www.frontiersin.org/articles/10.3389/fnins.2026.1966366
AB - BackgroundMotor-imagery electroencephalography often provides limited subject-specific supervision. This study tested whether complementary leaky integrate-and-fire (CLIF)-family temporal-state decoding shows a decoder-level advantage after the EEG representation is fixed, how that advantage changes with decoder-label availability, and whether hard binary spikes are required.MethodsANN-SEW, the artificial neural network comparator with spike-element-wise (SEW) residual topology, and SEW-CLIF received identical frozen attention-based temporal convolutional network (ATCNet) and filter-bank convolutional network (FBCNet) representations. The evidence chain combined BCI Competition IV Dataset 2a (BCI2a) development, retrospective OpenBMI replication, an internally pre-access-held-out BCI Competition IV Dataset 2b (BCI2b) evaluation, cross-entropy-only label-budget analyses, and a separate internally pre-access-locked BNCI2015-001 Session-A-to-Session-B evaluation. Additional robustness analyses included shared whole-sequence readout for parameter-matched gated recurrent unit (GRU) and temporal convolutional network (TCN) decoders, exact-gradient sigmoid-gated Soft-CLIF, equal optimizer-update budgets, a within-BCI2a binary sensitivity analysis, and computational profiling.ResultsUnder cross-entropy-only training, SEW-CLIF exceeded ANN-SEW by 10.082 percentage points (pp) on BCI2a and 5.470 pp. on OpenBMI, while full-budget BCI2b remained near parity (+0.181 pp). The ANN-relative gap decreased as decoder labels increased and persisted under equal-update training. At 25 labels/class, exact-gradient Soft-CLIF exceeded ANN-SEW by 11.085, 8.479, and 5.809 pp. on BCI2a, BCI2b, and OpenBMI, respectively. With matched whole-sequence readout, hard SEW-CLIF was broadly comparable to GRU-Stat and TCN-Stat on BCI2a and BCI2b and modestly higher on OpenBMI. The internally pre-access-locked BNCI2015-001 evaluation showed the same attenuation, with a slope of −2.319 pp. per label doubling (95% CI, −4.118 to −0.604).ConclusionCLIF-family temporal-state dynamics show a reproducible decoder-label-regime effect under frozen supervised representations. Hard forward discretization is not required for the ANN-relative effect, and matched temporal readout substantially reduces differences between CLIF and other continuous temporal decoders.
SN - 1662-453X
ER -