Abstract
Multimodal biosensing is a promising route to reliable upper-limb intent detection for rehabilitation and humanrobot interaction. In the ABC 2026 Multimodal Upper-limb Movement Intent Detection Challenge (MUMIDC), synchronized EEG, EMG, and IMU recordings are provided together with trigger timestamps that delineate task stages. We propose a lightweight, trigger-aligned pipeline that (i) robustly parses raw MindRove CSV files (including corrupted/header-only trials), (ii) reconciles timestamp timebases and aligns multimodal streams to trigger events, (iii) extracts fixed-length statistical descriptors from cue and action windows, and (iv) compares unimodal and fusion strategies under two official protocols: Experiment 1 (within-subject) and Experiment 2 (leave-one-subject-out, LOSO). Across LOSO action decoding, EMG+IMU achieved 0.695 ± 0.208 accuracy and 0.694 ± 0.208 macro-F1, while lightweight late fusion with weight w=0.85 achieved 𝟎. 𝟔 𝟗 𝟔 ± 𝟎. 𝟐 𝟎 𝟖 accuracy and 0.694 ± 0.208 macro-F1. LOSO cue decoding was substantially more challenging; the best model (late fusion) reached 0.375 ± 0.101 accuracy and 0.362 ± 0.097 macro-F1. We analyze practical data issues encountered in the raw recordings and show that within-subject feature normalization and late fusion improve robustness, while cross-subject generalization remains the primary limitation for early-stage intention decoding.