Comparison of logistic regression, support vector machines, and deep learning classifiers for predicting memory encoding success using human intracranial EEG recordings.
Comparison of logistic regression, support vector machines, and deep learning classifiers for predicting memory encoding success using human intracranial EEG recordings.
复制标题
DOI：
10.1088/1741-2552/aae131
复制
发表时间：
2018-12
影响因子：
4
通讯作者：
中科院分区：
文献类型：
--
作者：
我们试图测试三种二进制分类策略(Logistic回归、支持向量机和深度学习)的性能，以预测成功的情景记忆编码问题，使用从人类立体脑电信号对象获得的直接大脑记录。我们还试图测试应用t分布随机邻居嵌入(TSNE)进行无监督降维的效果，以及测试将输入特征减少到一组与记忆相关的核心脑区的效果。这项工作建立在已发表的努力的基础上，开发了一种闭环式刺激设备来提高记忆性能。我们使用了一个独特的数据集，该数据集由30名立体EEG患者组成，他们的电极被植入一个由五个共同大脑区域(以及其他区域)组成的核心集合，他们在记录大脑活动时执行自由回忆情节记忆任务。使用三种不同的机器学习策略，我们训练分类器来预测成功和不成功的记忆编码，并比较不同通道在主题水平和总体上的分类器性能差异(由AUC衡量)。我们报告了特征约简对分类器的影响，包括减少了输入脑区的数量、频带以及tSNE的影响。深度学习分类器的性能优于支持向量机和Logistic回归。对核心脑区的先验选择也提高了LR和支持向量机模型的分类器性能，特别是在与tSNE结合时。我们首次报道了在使用人脑电生理数据预测成功记忆编码的问题上，传统的二进制分类方法和深度学习方法之间的直接比较。我们的发现将为设计影响记忆处理的脑机接口设备提供参考。
We sought to test the performance of three strategies for binary classification (logistic regression, support vector machines, and deep learning) for the problem of predicting successful episodic memory encoding using direct brain recordings obtained from human stereo EEG subjects. We also sought to test the impact of applying t-distributed stochastic neighbor embedding (tSNE) for unsupervised dimensionality reduction, as well as testing the effect of reducing input features to a core set of memory relevant brain areas. This work builds upon published efforts to develop a closed-loop stimulation device to improve memory performance. We used a unique data set consisting of 30 stereo EEG patients with electrodes implanted into a core set of five common brain regions (along with other areas) who performed the free recall episodic memory task as brain activity was recorded. Using three different machine learning strategies, we trained classifiers to predict successful versus unsuccessful memory encoding and compared the difference in classifier performance (as measured by the AUC) at the subject level and in aggregate across modalities. We report the impact of feature reduction on the classifiers, including reducing the number of input brain regions, frequency bands, and the impact of tSNE. Deep learning classifiers outperformed both support vector machines (SVM) and logistic regression (LR). A priori selection of core brain regions also improved classifier performance for LR and SVM models, especially when combined with tSNE. We report for the first time a direct comparison among traditional and deep learning methods of binary classification to the problem of predicting successful memory encoding using human brain electrophysiological data. Our findings will inform the design of brain machine interface devices to affect memory processing.
登录
查看更多内容
DOI：
10.1016/j.cub.2017.03.028
发表时间：
2017-05-08
期刊：
Current biology : CB
影响因子：
--
作者：
Ezzyat Y;Kragel JE;Burke JF;Levy DF;Lyalenko A;Wanda P;O'Sullivan L;Hurley KB;Busygin S;Pedisich I;Sperling MR;Worrell GA;Kucewicz MT;Davis KA;Lucas TH;Inman CS;Lega BC;Jobst BC;Sheth SA;Zaghloul K;Jutras MJ;Stein JM;Das SR;Gorniak R;Rizzuto DS;Kahana MJ
通讯作者：
Kahana MJ
影响因子：
16.6
作者：
Ezzyat Y;Wanda PA;Levy DF;Kadel A;Aka A;Pedisich I;Sperling MR;Sharan AD;Lega BC;Burks A;Gross RE;Inman CS;Jobst BC;Gorenstein MA;Davis KA;Worrell GA;Kucewicz MT;Stein JM;Gorniak R;Das SR;Rizzuto DS;Kahana MJ
通讯作者：
Kahana MJ
影响因子：
5.3
作者：
Burke, John F.;Sharan, Ashwini D.;Kahana, Michael J.
通讯作者：
Kahana, Michael J.
影响因子：
3.5
作者：
Lega, Bradley C.;Jacobs, Joshua;Kahana, Michael
通讯作者：
Kahana, Michael
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y