Toward a Standardized Benchmark for EEG-Based Mobile Robots: A Fuzzy Comprehensive Evaluation Framework

The absence of a unified performance evaluation standard significantly hinders technological progress and the practical deployment of electroencephalography (EEG)-based mobile robots (EEG-WMRs). To address this gap, this paper introduces a novel fuzzy-based comprehensive evaluation framework designed for the holistic assessment and direct comparison of EEG-WMR systems. The proposed hierarchical architecture integrates multi-dimensional metrics spanning three critical domains: brain-computer interface (BCI) performance, robotic system execution, and human factors engineering. We establish a systematic scoring model through a process of indicator normalization, a hybrid analytic hierarchy process-entropy weight method for objective-subjective weight allocation, and a fuzzy logic-based synthesis. This framework is subsequently applied to evaluate 28 representative systems, encompassing the major EEG paradigms of motor imagery, P300, steady-state visual evoked potential, and hybrid approaches. The comparative analysis yields quantified performance scores, revealing the relative maturity of different control paradigms and providing insightful guidance for future research directions. The proposed standard offers a much-needed benchmark for the community, facilitating objective cross-study comparisons and accelerating the development of practical brain-controlled robots.