Preprint: Simultaneous Decoding of Kinetic and Kinematic Movement Parameters by Noninvasive Brain Imaging

Summary A preprint proposes three regression models — a partial least squares regressor, a multilayer perceptron and an attention-based regressor — to decode multiple kinematic and kinetic parameters of grasp-and-lift tasks simultaneously from EEG signals. Evaluated on the WAY EEG GAL dataset, the attention-based regressor performed best with an R² of 0.8 and 29.2 ms latency, markedly improving simultaneous multi-parameter decoding, though per-parameter decoding declined; the multilayer perceptron was more consistent across the two settings but less accurate (R² = 0.49). The study has not been peer reviewed.