EEG Data Space Adaptation to Reduce Intersession Nonstationarity in Brain-Computer Interface
EEG Data Space Adaptation to Reduce Intersession Nonstationarity in Brain-Computer Interface
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DOI：
10.1162/neco_a_00474
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发表时间：
2013-08-01
影响因子：
2.9
通讯作者：
Quek, Chai
中科院分区：
文献类型：
--
作者：
Arvaneh, Mahnaz;Guan, Cuntai;Quek, Chai
基于EEG的脑机接口(BCI)的一个主要挑战是EEG数据的会话间非平稳性，这往往会导致BCI性能的恶化。为了解决这一问题，本文提出了一种新的数据空间自适应技术--EEG数据空间自适应(EEG-DSA)，用于对来自目标空间(评估会话)的EEG数据进行线性变换，从而最小化对源空间(训练会话)的分布差异。使用Kullback-Leibler(KL)发散准则，我们提出了两种版本的EEG-DSA算法：当评估过程中有标签数据时，监督版本；当标签数据不可用时，非监督版本。所提出的EEG-DSA算法的性能在公开可用的BCI竞赛IV数据集IIa和16名受试者在不同日期执行运动想象任务的数据集上进行了评估。实验结果表明，在有监督和无监督两种情况下，本文提出的EEG-DSA算法在分类精度上都明显优于无自适应的结果。实验结果还表明，对于脑机接口性能较差的受试者，在没有应用自适应的情况下，在监督和非监督版本中提出的EEG-DSA算法的性能都显著优于无监督偏差自适应算法(PMean)。
A major challenge in EEG-based brain-computer interfaces (BCIs) is the intersession nonstationarity in the EEG data that often leads to deteriorated BCI performances. To address this issue, this letter proposes a novel data space adaptation technique, EEG data space adaptation (EEG-DSA), to linearly transform the EEG data from the target space (evaluation session), such that the distribution difference to the source space (training session) is minimized. Using the Kullback-Leibler (KL) divergence criterion, we propose two versions of the EEG-DSA algorithm: the supervised version, when labeled data are available in the evaluation session, and the unsupervised version, when labeled data are not available. The performance of the proposed EEG-DSA algorithm is evaluated on the publicly available BCI Competition IV data set IIa and a data set recorded from 16 subjects performing motor imagery tasks on different days. The results show that the proposed EEG-DSA algorithm in both the supervised and unsupervised versions significantly outperforms the results without adaptation in terms of classification accuracy. The results also show that for subjects with poor BCI performances when no adaptation is applied, the proposed EEG-DSA algorithm in both the supervised and unsupervised versions significantly outperforms the unsupervised bias adaptation algorithm (PMean).