## 6 Conclusions  
This study proposes an electroencephalogram (EEG)-based inverse reinforcement learning framework (EIRL) that constructs expert strategies for global path planning by decoding human neural activities, which subsequently drives the inverse reinforcement learning process to derive optimal paths. Experimental results demonstrate that EIRL-generated paths not only satisfy the optimality criteria of inverse reinforcement learning but also reveal significant consistency between robotic decisions and human path selection behaviors through expert strategy integration. The proposed method achieves 100% enhancement in path safety performance, particularly validating the reinforcement effect of expert prior knowledge on safe path selection in scenarios containing unmodeled risks (e.g., dynamic environmental disturbances). The staged methodology provides critical insights into neurocognitive-inspired robotics. The success of Phase 1 highlights that aggressive network pruning need not sacrifice temporal precision when guided by domain-specific constraints. Phase 2's reward abstraction demonstrates that human risk sensitivity can be encoded as spatial feature expectations, offering a generalizable alternative to handcrafted penalty functions. Most notably, Phase 3's policy convergence behavior suggests that neural-driven exploration strategies naturally balance safety and efficiency--a property notoriously difficult to achieve through manual reward shaping. These findings collectively advance the design of autonomous systems requiring minimal prior environmental knowledge. Two main limitations should be acknowledged: First, constrained by experimental apparatus, epilepsy-related datasets were employed as substitutes for standard motor imagery data in classification validation, though the methodological framework remains compatible with typical motor imagery paradigms. Second, to ensure EEG classification accuracy, robot motion was restricted to a four-direction discrete action space under Manhattan distance constraints, with current experiments only validating EIRL's efficacy in low-resolution grid maps. Future research will focus on advancing path planning methodologies through the development of high-dimensional continuous state space models, coupled with multi-scale algorithmic frameworks capable of adapting to dynamically evolving 2D/3D environments. Concurrent investigations will explore multi-modal biosignal-integrated decision architectures to enhance autonomous system responsiveness. The current comparisons, while illustrative, underscore a broader challenge in neural-integrated navigation research: the absence of standardized benchmarks to disentangle EEG's specific contributions. Future studies must bridge this gap by developing quantitative metrics that isolate neural features' impact on risk perception versus spatial reasoning. Such efforts could involve constructing open-source testbeds with human-in-the-loop baselines, where EEG's role in encoding safety priors can be systematically compared against other biosignals (e.g., EMG or EOG). Additionally, large-scale validation in unmodeled scenarios--such as post-disaster environments with collapsed structural maps--will be critical to assess generalizability beyond laboratory-controlled settings. Collaborations with neuroscientists will further elucidate the biological underpinnings of observed EEG-safety correlations, advancing toward explainable human-AI symbiosis. **Author Contributions:** Conceptualization, H.Z. and J.W. ; methodology, H.Z. and J.W. ; software, H.Z. and J.W. ; validation, J.W. ; formal analysis, H.Z. and R.G. ; investigation, H.Z. ; writing--original draft preparation, H.Z. ; writing--review and editing, J.W. and R.G. ; supervision, R.G. All authors have read and agreed to the published version of the manuscript.