抄録
Accurate classification of biosignals from surface electrodes is challenging because signals are noisy and many actions have similar patterns. Reliable recognition of upper-limb actions is important for rehabilitation and human–machine interaction systems. This paper presents a crosssubject classification method for similar appearing yet distinct upper-limb actions using high-dimensional frequency-domain features extracted from EEG, EMG, and IMU signals in the Multimodal Upper-Limb Movement Dataset. Seven actions are analyzed using standardized preprocessing and modality-specific feature extraction, with models evaluated under subjectwise and cross-subject protocols to assess generalization. EMG+IMU features achieve subject-wise accuracy above 95%, showing strong and repeatable neuromuscular and motion patterns. In cross-subject testing, performance decreases for all models, but Subspace KNN remains robust with 94.64% accuracy. In contrast, EEG-only models show high variability and poor cross-subject performance because of strong inter-subject differences and weak class separability. While subject-wise EEG classification achieves accuracies up to 83.33%, cross-subject generalization remains limited, likely due to constrained dataset size, limited channel coverage, and inadequate motor cortex information. The results highlight the limitations of standalone EEG for subject-independent action classification due to dominant noise in low-amplitude surface signals, supporting multimodal approaches for reliable real-world deployment. This work is part of the Multimodal Upper-Limb Movement Intent Detection Challenge at the Activity and Behavior Computing Conference.
https://ror.org/02797hn66 
