基于注意力机制的多分支LSTM心理负荷评估模型

充分获取脑电信号的有效特征已成为心理负荷评估亟待解决的问题.提出一种多分支LSTM和注意力机制相结合的多分类网络框架.首先,此网络在对脑电信号做切片处理后,采用多分支LSTM网络提取切片中的时间特征;然后,利用注意力机制对所提取的时间特征进行权重参数优化;最后,通过softmax层输出心理负荷评估结果.通过消融实验和对比实验对模型进行验证.结果 表明,此网络无论在二分类任务还是多分类任务中的表现均优于现有先进网络.

A Novel multiclass classification framework with multi-branch LSTM and attention mechanism for mental workload evaluation

It is critical to obtain sufficient and effective EEG features in mental workload evaluation. This paper presented a multiclass classification network which combined multi-branch LSTM with attention mechanism. Firstly, this network segmented an EEG signal into a number of temporal slices, in each of which multi-branch LSTM extracted temporal feature from the EEG slices. Then attention mechanism optimized the features. Finally, softmax function evaluated mental workload level. The experimental results show that the performance of the proposed network is better than that of the existing network in both binary class and multiclass classification task.