Sparse Bayesian Classification of EEG for Brain-Computer Interface
Sparse Bayesian Classification of EEG for Brain-Computer Interface
复制标题
脑机接口脑电图的稀疏贝叶斯分类
DOI：
10.1109/tnnls.2015.2476656
复制
发表时间：
2016-11-01
影响因子：
10.4
通讯作者：
Cichocki, Andrzej
中科院分区：
文献类型：
--
作者：
Zhang, Yu;Zhou, Guoxu;Cichocki, Andrzej
正则化是脑机接口（BCI）中防止过拟合的最常用方法之一。正则化的有效性通常高度依赖于正则化参数的选择，这些参数通常由交叉验证（CV）确定。然而，CV对BCI施加了两个主要限制：1）需要来自用户的大量训练数据，以及2）校准分类器需要相对较长的时间。这些限制实质上恶化了系统的实用性，并且可能导致用户不愿意使用BCI。在本文中，我们介绍了一种稀疏贝叶斯方法，利用拉普拉斯先验，即SB拉普拉斯，脑电分类。在贝叶斯证据框架下，以分层方式利用拉普拉斯先验知识学习稀疏判别向量。所有需要的模型参数都是从训练数据中自动估计的，而不需要CV。SBLaplace算法和其他几个竞争的方法之间进行了广泛的比较基于两个EEG数据集。实验结果表明，SBLaplace算法取得了更好的整体性能比竞争算法的EEG分类。
Regularization has been one of the most popular approaches to prevent overfitting in electroencephalogram (EEG) classification of brain-computer interfaces (BCIs). The effectiveness of regularization is often highly dependent on the selection of regularization parameters that are typically determined by cross-validation (CV). However, the CV imposes two main limitations on BCIs: 1) a large amount of training data is required from the user and 2) it takes a relatively long time to calibrate the classifier. These limitations substantially deteriorate the system's practicability and may cause a user to be reluctant to use BCIs. In this paper, we introduce a sparse Bayesian method by exploiting Laplace priors, namely, SBLaplace, for EEG classification. A sparse discriminant vector is learned with a Laplace prior in a hierarchical fashion under a Bayesian evidence framework. All required model parameters are automatically estimated from training data without the need of CV. Extensive comparisons are carried out between the SBLaplace algorithm and several other competing methods based on two EEG data sets. The experimental results demonstrate that the SBLaplace algorithm achieves better overall performance than the competing algorithms for EEG classification.