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首页> 外文期刊>Journal of Neuroscience Methods >Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy
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Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy

机译：逐次超过机会等级：脑信号分类和解码准确性统计评估中理论机会等级的警告

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Machine learning techniques are increasingly used in neuroscience to classify brain signals. Decoding performance is reflected by how much the classification results depart from the rate achieved by purely random classification. In a 2-class or 4-class classification problem, the chance levels are thus 50% or 25% respectively. However, such thresholds hold for an infinite number of data samples but not for small data sets. While this limitation is widely recognized in the machine learning field, it is unfortunately sometimes still overlooked or ignored in the emerging field of brain signal classification. Incidentally, this field is often faced with the difficulty of low sample size. In this study we demonstrate how applying signal classification to Gaussian random signals can yield decoding accuracies of up to 70% or higher in two-class decoding with small sample sets. Most importantly, we provide a thorough quantification of the severity and the parameters affecting this limitation using simulations in which we manipulate sample size, class number, cross-validation parameters (k-fold, leave-one-out and repetition number) and classifier type (Linear-Discriminant Analysis, Naive Bayesian and Support Vector Machine). In addition to raising a red flag of caution, we illustrate the use of analytical and empirical solutions (binomial formula and permutation tests) that tackle the problem by providing statistical significance levels (p-values) for the decoding accuracy, taking sample size into account. Finally, we illustrate the relevance of our simulations and statistical tests on real brain data by assessing noise-level classifications in Magnetoencephalography (MEG) and intracranial EEG (iEEG) baseline recordings. (C) 2015 Elsevier B.V. All rights reserved.
机译：机器学习技术在神经科学中越来越多地用于对脑信号进行分类。解码性能通过分类结果与纯随机分类所达到的速率相差多少来反映。因此，在2类或4类分类问题中，机会级别分别为50％或25％。但是，此类阈值适用于无限数量的数据样本，但不适用于较小的数据集。尽管此限制在机器学习领域得到了广泛认可，但不幸的是，在新兴的脑信号分类领域中，有时仍被忽略或忽略。顺便说一句，该领域经常面临样本量低的困难。在本研究中，我们演示了将信号分类应用于高斯随机信号如何在小样本集的两类解码中产生高达70％或更高的解码精度。最重要的是，我们使用模拟方法（包括操纵样本大小，类别编号，交叉验证参数（k倍，留一法和重复数）和分类器类型）来提供对严重性和影响此限制的参数的全面量化。 （线性判别分析，朴素贝叶斯和支持向量机）。除了提出警告的红旗外，我们还将说明分析和经验解决方案（二项式公式和置换检验）的使用，该解决方案通过提供统计有效级别（p值）来提高解码精度，同时考虑样本量，从而解决了该问题。最后，我们通过评估脑磁图（MEG）和颅内EEG（iEEG）基线记录中的噪声级别分类，说明了我们的仿真和统计测试对真实大脑数据的相关性。 （C）2015 Elsevier B.V.保留所有权利。

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