情感脑机接口：选择性能测量指标的教程
Affective Brain-Computer Interfaces: A Tutorial to Choose Performance Measuring Metric
摘要：
情感脑机接口是情感计算中一个相对较新的研究领域。情感状态的估计可以改善人机交互，以及改善严重残疾人的护理。为了评估EEG记录在识别情感状态方面的有效性，我们在这里使用了我们实验室收集的数据以及公开可用的DEAP数据库。我们还回顾了使用DEAP数据库的文章，发现大量文章没有考虑DEAP中存在的类不平衡。不考虑阶级不平衡会产生误导性的结果。此外，忽略类别不平衡使得研究之间的比较结果不可能，因为不同的数据集将具有不同的类别不平衡。阶级不平衡也会改变机会水平，因此在确定结果是否高于机会时，考虑阶级偏见至关重要。为了正确考虑类不平衡的影响，我们建议使用平衡精度作为性能指标，其后验分布计算可信区间。对于分类，我们使用了文献中提到的特征和另外的theta beta-1比率。从DEAP和我们的数据的结果表明，β波段功率，θ波段功率，和θ β-1比率是更好的特征集分类效价，唤醒，和优势，分别。
英文摘要：
Affective brain-computer interfaces are a relatively new area of research in affective computing. Estimation of affective states can improve human-computer interaction as well as improve the care of people with severe disabilities. To assess the effectiveness of EEG recordings in recognizing affective state, here we used data collected in our lab as well as the publicly available DEAP database. We also reviewed the articles that used the DEAP database and found that a significant number of articles did not consider the presence of the class imbalance in the DEAP. Failing to consider class imbalance creates misleading results. Further, ignoring class imbalance makes the comparing results between studies impossible, since different datasets will have different class imbalances. Class imbalance also shifts the chance level, hence it is vital to consider class bias while determining if the results are above chance. To properly account the effect of class imbalance, we suggest the use of balanced accuracy as a performance metric and its posterior distribution for computing credible intervals. For classification, we used features mentioned in the literature and additionally theta beta-1 ratio. Results from DEAP and our data suggest that the beta band power, theta band power, and theta beta-1 ratio are better feature sets for classifying valence, arousal, and dominance, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
