In emotion recognition using EEG, it is not generally agreed upon how much time an EEG signal sequence must have in order to maximize precision and recall rates. To the best of our knowledge, there is not a systematic evaluation of effects on classifier performance related to EEG signal durations. The human factors related to attention decreasing and tiredness increasing have imposed difficulties to create EEG datasets containing a rich variation of signal samples. This paper proposes an experimental evaluation of three different EEG datasets (DEAP, MAHNOB, and STEED) each one mainly characterized by short, intermediate and long signal (or stimulus) durations. Statistical evaluation pointed out that for an EEG dataset to be well-suited for emotion recognition it should have two main characteristics: emotion stimulus data should be publicly available and evaluated by world-wide volunteers, and media stimulus should have duration long enough to affect the subjects. Our statistical analysis revealed that, at least for the considered datasets, signals with duration longer than 60 seconds allow better classification results. This work did not analyse the impact to humans of longer stimulus media.

中文翻译：

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将 EEG 信号持续时间与情绪分类性能相关的经验证据

在使用 EEG 的情绪识别中，一般没有就 EEG 信号序列必须具有多长时间才能最大限度地提高精度和召回率达成一致。据我们所知，没有对与 EEG 信号持续时间相关的分类器性能影响的系统评估。与注意力下降和疲劳增加相关的人为因素给创建包含丰富信号样本变化的 EEG 数据集带来了困难。本文提出了对三个不同 EEG 数据集（DEAP、MAHNOB 和 STEED）的实验评估，每个数据集的主要特征是短、中和长信号（或刺激）持续时间。统计评估指出，对于一个非常适合情感识别的 EEG 数据集，它应该具有两个主要特征：情绪刺激数据应公开可用并由全球志愿者评估，媒体刺激应具有足够长的持续时间以影响受试者。我们的统计分析表明，至少对于所考虑的数据集，持续时间超过 60 秒的信号可以获得更好的分类结果。这项工作没有分析较长刺激媒体对人类的影响。