###### Abstract  
Recent advancements in lightweight electroencephalogram(EEG) signal classification have enabled real-time human-robot interaction, yet challenges persist in balancing computational efficiency and safety in dynamic path planning. This study proposes an EEG-based inverse reinforcement learning (EIRL) framework to simulate human navigation strategies by decoding neural decision preferences. The method integrates a pruned WNFG-SSCNet-ADMM classifier for EEG signal mapping, apprenticeship learning for reward function extraction, and Q-learning for policy optimization. Experimental validation in an \(8\times 8\) FrozenLake-v1 environment demonstrates that EIRL reduces average path risk values by 50% compared with traditional reinforcement learning, achieving expert-level safety (\(\Delta=4\)) while maintaining optimal path lengths. The framework enhances adaptability in unknown environments by embedding human-like risk aversion into robotic planning, offering a robust solution for applications requiring minimal prior environmental knowledge. Results highlight the synergy between neural feedback and computational models, advancing inclusive human-robot collaboration in safety-critical scenarios. EEG signal classification; inverse reinforcement learning; global path planning 2020  
graph construction complexity from quadratic \(O(n^{2})\) to \(O(Kn)\)\((K\ll n)\), achieving an order-of-magnitude reduction in redundant edges while significantly improving memory efficiency. Particularly noteworthy is the framework's frequency-domain sparse graph construction, which demonstrates superior classification accuracy and stability compared with traditional time-domain approaches, especially under aggressive pruning conditions. Subsequent validation in epileptic EEG recognition tasks has confirmed the framework's robustness and practical applicability in critical biomedical applications. The Bonn dataset comprises five subsets (A-E), with subset E containing seizure data. Signals are segmented into non-overlapping 256-point windows to form four binary classification tasks (A v.s. E, B v.s. E, C v.s. E, D v.s. E), each containing 3200 samples (1600 epileptic vs. non-epileptic) [9]. Experiments demonstrate that SSGCNet with WNFG in the frequency domain outperforms traditional time-domain methods, while ADMM pruning reduces model parameters by 10-fold without performance degradation. These results highlight ADMM's superiority in balancing lightweight design and precision, paving the way for portable epilepsy monitoring devices [8]. Motor imagery (MI)-based EEG classification, which decodes neural activity patterns generated during imagined movements (e.g., limb motion), has emerged as a critical research direction in human-machine interaction. The PhysioNet MI dataset [10], for example, includes 64-channel EEG signals (sampled at 160 Hz) from 109 subjects performing 14 tasks (e.g., eye opening/closing, fist clenching/relaxing), with over 1500 samples [11]. Traditional classifiers such as support vector machines (SVM) [12] and linear discriminant analysis (LDA) [13] achieve high accuracy but rely on manually designed features, limiting their generalizability. In contrast, deep learning models (e.g., convolutional neural networks (CNN) [3; 14]) automatically extract frequency-domain features but suffer from excessive training time and computational costs [15; 16]. To address these challenges, lightweight improvement strategies have been proposed. Dropout techniques mitigate overfitting by randomly deactivating neurons [17; 18], while magnitude-based pruning removes redundant weights for model compression. However, existing methods often lack theoretical optimization of network sparsity, leading to a trade-off between pruning rates and classification accuracy. Recent studies have demonstrated that hybrid frameworks combining ADMM with masked retraining have effectively solved non-convex sparse optimization problems, showing advantages in convergence and computational efficiency for epileptic EEG classification tasks [7; 19].