An affective brain-computer interface (aBCI) is a direct communication pathway between human brain and computer, via which the computer tries to recognize the affective states of its user and respond accordingly. As aBCI introduces personal affective factors into human-computer interaction, it could potentially enrich the user’s experience during the interaction. Successful emotion recognition plays a key role in such a system. The state-of-the-art aBCIs leverage machine learning techniques which consist in acquiring affective electroencephalogram (EEG) signals from the user and calibrating the classifier to the affective patterns of the user. Many studies have reported satisfactory recognition accuracy using this paradigm. However, affective neural patterns are volatile over time even for the same subject. The recognition accuracy cannot be maintained if the usage of aBCI prolongs without recalibration. Existing studies have overlooked the performance evaluation of aBCI during long-term use. In this paper, we propose SAFE—an EEG dataset for stable affective feature selection. The dataset includes multiple recording sessions spanning across several days for each subject. Multiple sessions across different days were recorded so that the long-term recognition performance of aBCI can be evaluated. Based on this dataset, we demonstrate that the recognition accuracy of aBCIs deteriorates when re-calibration is ruled out during long-term usage. Then, we propose a stable feature selection method to choose the most stable affective features, for mitigating the accuracy deterioration to a lesser extent and maximizing the aBCI performance in the long run. We invite other researchers to test the performance of their aBCI algorithms on this dataset, and especially to evaluate the long-term performance of their methods.

情感脑计算机接口（aBCI）是人脑与计算机之间的直接通信路径，计算机试图通过该路径来识别其用户的情感状态并做出相应的响应。由于aBCI将个人情感因素引入人机交互中，因此有可能在交互过程中丰富用户的体验。成功的情感识别在这样的系统中起着关键作用。最新的aBCI利用机器学习技术，该技术包括从用户那里获取情感脑电图（EEG）信号并将分类器校准为用户的情感模式。许多研究报告了使用这种范例的令人满意的识别精度。但是，即使对于同一受试者，情感神经模式也会随时间变化。如果aBCI的使用时间延长而不进行重新校准，则无法保持识别精度。现有研究忽略了长期使用aBCI的性能评估。在本文中，我们提出了SAFE-一种用于稳定情感特征选择的EEG数据集。数据集包括每个主题跨越几天的多个记录会话。记录了不同日期的多次会话，以便可以评估aBCI的长期识别性能。基于此数据集，我们证明了在长期使用过程中排除重新校准后，aBCI的识别精度会下降。然后，我们提出了一种稳定的特征选择方法，以选择最稳定的情感特征，从而从长远角度减轻精度降低的幅度并最大化aBCI性能。