Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI) approaches. Despite the success of existing EEG approaches in brain state recognition studies, it is still challenging to differentiate brain states via explainable and generalizable deep learning approaches. In other words, how to explore meaningful and distinguishing features and how to overcome the huge variability and overfitting problem still need to be further studied. To alleviate these challenges, in this work, a multiple random fragment search-based multilayer recurrent neural network (MRFS-MRNN) is proposed to improve the differentiating performance and explore meaningful patterns. Specifically, an explainable MRNN module is proposed to capture the temporal dependences preserved in EEG time series. Besides, a MRFS module is designed to cut multiple random fragments from the entire EEG signal time course to improve the effectiveness of brain state differentiating ability. MRFS-MRNN is concatenatedto effectively overcome the huge variabilities and overfitting problems. Experiment results demonstrate that the proposed MRFS-MRNN model not only has excellent differentiating performance, but also has good explanation and generalization ability. The classification accuracies reach as high as 95.18% for binary classification and 89.19% for four-category classification on the individual level. Similarly, 95.53% and 85.84% classification accuracies are obtained for the binary and four-category classification on the group level. What’s more, 94.28% and 85.43% classification accuracies of binary and four-category classifications are achieved for predicting brand new subjects. The experiment results showed that the proposed method outperformed other state-of-the-art (SOTA) models on the same underlying data and improved the explanation and generalization ability.

中文翻译：

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一种可解释且可推广的循环神经网络方法，用于区分脑电图数据集上的人脑状态


脑电图（EEG）是最广泛使用的脑机接口（BCI）方法之一。尽管现有的脑电图方法在大脑状态识别研究中取得了成功，但通过可解释和可推广的深度学习方法来区分大脑状态仍然具有挑战性。换句话说，如何探索有意义的、有区别的特征以及如何克服巨大的变异性和过拟合问题仍然需要进一步研究。为了缓解这些挑战，在这项工作中，提出了一种基于多随机片段搜索的多层递归神经网络（MRFS-MRNN）来提高区分性能并探索有意义的模式。具体来说，提出了一个可解释的 MRNN 模块来捕获脑电图时间序列中保留的时间依赖性。此外，还设计了MRFS模块，从整个脑电信号时程中切割出多个随机片段，以提高大脑状态区分能力的有效性。 MRFS-MRNN的级联有效克服了巨大的变异性和过拟合问题。实验结果表明，所提出的MRFS-MRNN模型不仅具有优异的区分性能，而且具有良好的解释和泛化能力。在个体层面上，二分类准确率高达95.18%，四分类分类准确率高达89.19%。同样，群体级别的二分类和四分类分类准确率分别为 95.53% 和 85.84%。更重要的是，对于预测全新的主题，二分类和四分类的分类准确率达到了 94.28% 和 85.43%。 实验结果表明，该方法在相同底层数据上优于其他最先进（SOTA）模型，并提高了解释和泛化能力。