An interesting case of the well-known Dataset Shift Problem is the classification of Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The non-stationarity of EEG signals can lead to poor generalisation performance in BCI classification systems used in different sessions, also from the same subject. In this paper, we start from the hypothesis that the Dataset Shift problem can be alleviated by exploiting suitable eXplainable Artificial Intelligence (XAI) methods to locate and transform the relevant characteristics of the input for the goal of classification. In particular, we focus on an experimental analysis of explanations produced by several XAI methods on an ML system trained on a typical EEG dataset for emotion recognition. Results show that many relevant components found by XAI methods are shared across the sessions and can be used to build a system able to generalise better. However, relevant components of the input signal also appear to be highly dependent on the input itself.

翻译：数据集漂移问题的一个典型案例是在脑机接口（BCI）背景下对脑电图（EEG）信号进行分类。脑电图信号的非平稳性可能导致不同会话中（即使来自同一受试者）的BCI分类系统泛化性能较差。本文基于以下假设：通过利用适当的可解释人工智能（XAI）方法定位并转换输入中与分类目标相关的特征，可以缓解数据集漂移问题。具体而言，我们聚焦于对典型情绪识别EEG数据集上训练的机器学习系统，进行多种XAI方法所生成解释的实验分析。结果表明：XAI方法发现的许多相关成分在不同会话间存在共享性，可用于构建泛化能力更强的系统；然而，输入信号中的相关成分也表现出对输入本身的高度依赖性。