论文标题
KAM-用脑电图数据进行情感分类的内核注意模块
KAM -- a Kernel Attention Module for Emotion Classification with EEG Data
论文作者
论文摘要
在这项工作中，提出了一个内核注意模块，用于通过神经网络进行基于脑电图的情绪分类的任务。所提出的模块通过执行内核技巧来利用自我发挥的机制，与标准注意模块相比，要求训练参数和计算要少得多。该设计还提供了一个标量，用于定量检查深度优化期间分配的注意力量，因此有助于更好地解释训练有素的模型。使用EEGNET作为骨干模型，与其他SOTA注意模块相比，在种子数据集上进行了广泛的实验，以评估模块内部主体内分类任务的性能。仅需要一个额外的参数，插入的模块被证明可以在15个受试者中提高基本模型的平均预测精度至1 \％以上。该方法的一个关键组成部分是解决方案的解释性，该解决方案使用几种不同的技术来解决，并作为依赖性分析的一部分包含在整个过程中。
In this work, a kernel attention module is presented for the task of EEG-based emotion classification with neural networks. The proposed module utilizes a self-attention mechanism by performing a kernel trick, demanding significantly fewer trainable parameters and computations than standard attention modules. The design also provides a scalar for quantitatively examining the amount of attention assigned during deep feature refinement, hence help better interpret a trained model. Using EEGNet as the backbone model, extensive experiments are conducted on the SEED dataset to assess the module's performance on within-subject classification tasks compared to other SOTA attention modules. Requiring only one extra parameter, the inserted module is shown to boost the base model's mean prediction accuracy up to more than 1\% across 15 subjects. A key component of the method is the interpretability of solutions, which is addressed using several different techniques, and is included throughout as part of the dependency analysis.
