论文标题
单调性限制了通过有限的脑电图数据进行情绪分类的注意力模块
A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data
论文作者
论文摘要
在这项工作中，使用有限或相对较少数量的脑电图（EEG）信号提出了一个参数有效的注意模块。该模块被称为单调性限制的注意模块（MCAM），因为它在将特征的革兰氏矩阵转换为注意矩阵以进行更好的特征细化时，可以将先验纳入单调性上。我们的实验表明，MCAM的有效性可与最新的注意模块相媲美，从而在提高骨干网络在预测中的性能时，同时需要更少的参数。还对受过训练的模型对不同攻击的预测进行了几项伴随的灵敏度分析。这些攻击包括各种频域过滤水平和与多个标签相关的样品之间逐渐变形。我们的结果可以帮助更好地了解预测中不同模块的行为，并可以在数据有限且存在噪音的应用程序中提供指导。
In this work, a parameter-efficient attention module is presented for emotion classification using a limited, or relatively small, number of electroencephalogram (EEG) signals. This module is called the Monotonicity Constrained Attention Module (MCAM) due to its capability of incorporating priors on the monotonicity when converting features' Gram matrices into attention matrices for better feature refinement. Our experiments have shown that MCAM's effectiveness is comparable to state-of-the-art attention modules in boosting the backbone network's performance in prediction while requiring less parameters. Several accompanying sensitivity analyses on trained models' prediction concerning different attacks are also performed. These attacks include various frequency domain filtering levels and gradually morphing between samples associated with multiple labels. Our results can help better understand different modules' behaviour in prediction and can provide guidance in applications where data is limited and are with noises.
