Effectively Interpreting Electroencephalogram Classification Using the Shapley Sampling Value to Prune a Feature Tree

Identifying the features that contribute to classification using machine learning remains a challenging problem in terms of the interpretability and computational complexity of the endeavor. Especially in electroencephalogram (EEG) medical applications, it is important for medical doctors and patients to understand the reason for the classification. In this paper, we thus propose a method to quantify contributions of interpretable EEG features on classification using the Shapley sampling value (SSV). In addition, a pruning method is proposed to reduce the SSV computation cost. The pruning is conducted on an EEG feature tree, specifically at the sensor (electrode) level, frequency-band level, and amplitude-phase level. If the contribution of a feature at a high level (e.g., sensor level) is very small, the contributions of features at a lower level (e.g., frequency-band level) should also be small. The proposed method is verified using two EEG datasets: classification of sleep states, and screening of alcoholics. The results show that the method reduces the SSV computational complexity while maintaining high SSV accuracy. Our method will thus increase the importance of data-driven approaches in EEG analysis.