This study presents a novel method to assess the learning effectiveness using Electroencephalography (EEG)-based Multi-Time Scale Spatiotemporal Compound Model (MTSC). Due to the evaluation of navigation learning, which is centered on the practice innovation ability devel?opment, it is difficult to assess the learning effectiveness in cultivating students’ ability objectively by questionnaire or other assessment methods. Research in the field of brain science has shown that innovation ability can be reflected from cognitive ability which can be embodied by EEG signal features. Therefore, a navigation learning assessment method was applied to detect the cognitive training traces by EEG-based MTSC. To verify the validity of the method, three navigation missions with increasing cog?nitive difficulty were designed, and 41 participants attended the experiment. The features of participants’ EEG signals were extracted and classified by MTSC. Comparing the results between experimental group and control group, it can be seen that MTSC can effectively distinguish the cog?nitive training traces of students in the navigation problem?solving process. In addition, MTSC achieved 89.2% accu?racy and 0.883 F1-Score on features classification, which had an average improvement of 15.7% over other three tra?ditional EEG classification models in accuracy. The results indicate that the navigation learning assessment method provides a useful evaluation approach for learning effec?tiveness using EEG-based MTSC.

In practical applications, commercial-grade Micro-Electro-Mechanical System (MEMS) gyros tend to exhibit dynamics sensitivities which are resistant to modeling by linear drift. To address the problem, a new nonlinear robust bias observer (NRBO) is proposed in this paper. Unlike the existing attitude and gyro bias nonlinear observers, we propose a dynamics-sensitive gyro bias decomposition method to tackle unknown nonlinear dynamic models of MEMS gyro bias. We expose the potential advantages of the proposed method: the global asymptotic stability of the NBRO and the robustness to the bias instability of MEMS gyros can be guaranteed through a rational design of the attitude-angular rate nonlinear dynamic coupling (AARNDC) term and the attitude-linear coupling (ALC) term. In addition, the attitude estimation with bias correction is given in the framework of NBRO. The experiment of a cable-parallel robot demonstrate the robustness to the bias instability and the accuracy improvement of MEMS gyros.