An Idle-State Detection Algorithm for SSVEP-Based Brain-Computer Interfaces Using a Maximum Evoked Response Spatial Filter
An Idle-State Detection Algorithm for SSVEP-Based Brain-Computer Interfaces Using a Maximum Evoked Response Spatial Filter
复制标题
使用最大诱发响应空间滤波器的基于 SSVEP 的脑机接口空闲状态检测算法
DOI：
10.1142/s0129065715500306
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发表时间：
2015-08
影响因子：
8
通讯作者：
Shiliang Li
中科院分区：
文献类型：
--
作者：
Dan Zhang;Bisheng Huang;Wei Wu;Shiliang Li
虽然空闲状态的准确识别对于脑机接口（BCI）在现实世界中的应用是必不可少的，但由于空闲状态的可变性，它仍然是一项具有挑战性的任务。提出了一种基于稳态视觉诱发电位（SSVEP）的脑机接口空闲状态检测算法。该算法的目的是解决闲置状态检测问题，通过构建一个更好的模型的控制状态。对于特征提取，最大诱发响应（MER）空间滤波器被开发用于提取神经生理学上合理的SSVEP响应，通过找到多通道脑电图（EEG）信号的组合，最大化诱发响应，同时抑制不相关的背景EEG。提取的SSVEP响应的频率都出席和无人值守的刺激，然后被用来形成特征向量和一系列的二进制分类器，用于识别每个控制状态和空闲状态的构建。在一个三目标SSVEP脑机接口实验与各种空闲状态条件下的9个科目的EEG数据被用来评估所提出的算法。与最流行的基于典型相关分析的算法和传统的基于功率谱的算法相比，所提出的算法通过实现88.0 ± 11.1%的离线控制状态分类准确度和范围从7.4 ± 5.6%到14.2 ± 10.1%的空闲状态误报率（FPRs），取决于特定的空闲状态条件。此外，在线模拟报告的BCI性能接近实际用途：24个控制命令中的22.0 ± 2.9个被正确识别，并且FPR在睁眼的空闲状态条件下达到低至约0.5事件/分钟，在闭眼的空闲状态条件下达到0.05事件/分钟。这些结果表明，所提出的算法实现实际的SSVEP BCI系统的潜力。
Although accurate recognition of the idle state is essential for the application of brain-computer interfaces (BCIs) in real-world situations, it remains a challenging task due to the variability of the idle state. In this study, a novel algorithm was proposed for the idle state detection in a steady-state visual evoked potential (SSVEP)-based BCI. The proposed algorithm aims to solve the idle state detection problem by constructing a better model of the control states. For feature extraction, a maximum evoked response (MER) spatial filter was developed to extract neurophysiologically plausible SSVEP responses, by finding the combination of multi-channel electroencephalogram (EEG) signals that maximized the evoked responses while suppressing the unrelated background EEGs. The extracted SSVEP responses at the frequencies of both the attended and the unattended stimuli were then used to form feature vectors and a series of binary classifiers for recognition of each control state and the idle state were constructed. EEG data from nine subjects in a three-target SSVEP BCI experiment with a variety of idle state conditions were used to evaluate the proposed algorithm. Compared to the most popular canonical correlation analysis-based algorithm and the conventional power spectrum-based algorithm, the proposed algorithm outperformed them by achieving an offline control state classification accuracy of 88.0 ± 11.1% and idle state false positive rates (FPRs) ranging from 7.4 ± 5.6% to 14.2 ± 10.1%, depending on the specific idle state conditions. Moreover, the online simulation reported BCI performance close to practical use: 22.0 ± 2.9 out of the 24 control commands were correctly recognized and the FPRs achieved as low as approximately 0.5 event/min in the idle state conditions with eye open and 0.05 event/min in the idle state condition with eye closed. These results demonstrate the potential of the proposed algorithm for implementing practical SSVEP BCI systems.
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影响因子：
2.5
作者：
Ortiz-Rosario A;Berrios-Torres I;Adeli H;Buford JA
通讯作者：
Buford JA
DOI：
--
发表时间：
2009-08
期刊：
2009 17th European Signal Processing Conference
影响因子：
--
作者：
G. G. Molina-G.;D. Ibáñez;V. Mihajlović;Dmitri Chestakov
通讯作者：
G. G. Molina-G.;D. Ibáñez;V. Mihajlović;Dmitri Chestakov
DOI：
10.1109/86.847808
发表时间：
2000-06-01
期刊：
IEEE TRANSACTIONS ON REHABILITATION ENGINEERING
影响因子：
--
作者：
Donchin, E;Spencer, KM;Wijesinghe, R
通讯作者：
Wijesinghe, R
DOI：
10.1109/icbbe.2008.832
发表时间：
2008-05
期刊：
2008 2nd International Conference on Bioinformatics and Biomedical Engineering
影响因子：
--
作者：
Ran Ren;Guangyu Bin;Xiaorong Gao
通讯作者：
Ran Ren;Guangyu Bin;Xiaorong Gao
影响因子：
5.1
作者：
Diez PF;Mut VA;Avila Perona EM;Laciar Leber E
通讯作者：
Laciar Leber E