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Affective brain-computer interfaces: Choosing a meaningful performance measuring metric
Computers in Biology and Medicine ( IF 7.7 ) Pub Date : 2020-09-24 , DOI: 10.1016/j.compbiomed.2020.104001
Md Rakibul Mowla 1 , Rachael I Cano 2 , Katie J Dhuyvetter 1 , David E Thompson 1
Affiliation  

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 for recognizing affective states, 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 comparison of the results between studies using different datasets 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 for 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 from the literature as well as 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.



中文翻译:

情感的人机界面:选择有意义的绩效衡量指标

情感脑机接口是情感计算研究的一个相对较新的领域。估计情感状态可以改善人机交互,并改善对严重残疾人的护理。为了评估脑电图记录对识别情感状态的有效性,我们使用了在实验室中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,发现大量文章并未考虑DEAP中类不平衡的存在。不考虑阶级失衡会产生误导性的结果。此外,由于不同的数据集将具有不同的类别失衡,因此忽略类别失衡将无法使用不同数据集进行研究之间的结果比较。班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班级偏差至关重要。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,并使用其后验分布来计算可信区间。对于分类,我们使用了文献中的特征以及theta beta-1比率。来自DEAP的结果和我们的数据表明,β频带功率,θ频带功率和θβ-1比值分别是更好的特征集,用于分类价,激发和主导。及其后验分布以计算可信区间。对于分类,我们使用了文献中的特征以及theta beta-1比率。来自DEAP的结果和我们的数据表明,β频带功率,θ频带功率和θβ-1比值分别是更好的特征集,用于分类价,激发和主导。及其后验分布以计算可信区间。对于分类,我们使用了文献中的特征以及theta beta-1比率。来自DEAP的结果和我们的数据表明,β频带功率,θ频带功率和θβ-1比值分别是更好的特征集,用于分类价,激发和主导。

更新日期:2020-09-30
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