Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks.
Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks.
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结合稳健的水平提取和无监督自适应分类以实现高精度 fNIRS-BCI：心算任务和歌唱任务之间单次试验区分的证据
DOI：
10.3389/fnins.2022.938518
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发表时间：
2022
影响因子：
4.3
通讯作者：
Gao, Feng
中科院分区：
文献类型：
--
作者：
Zhang, Yao;Liu, Dongyuan;Zhang, Pengrui;Li, Tieni;Li, Zhiyong;Gao, Feng
关键词：
功能性近红外光谱（fNIRS）是一种安全、无创的光学成像技术，越来越多地用于脑机接口（BCI）识别心理任务。与直接测量神经激活的脑电图（EEG）不同，fNIRS信号反映了神经血管耦合诱导的血液动力学反应，该反应在时间上可能是缓慢的，并且在模式上是变化的。所建立的分类器主要采用基于特征的监督模型，如支持向量机（SVM）和线性判别分析（LDA），并未能及时表征水平敏感的血流动力学模式的EEG的扩展。一个专用的分类器是需要的有意活动识别fNIRS-BCI，包括自适应获取的响应相关的功能和准确的歧视隐含的想法。为此，我们在这里提出了一个专门设计的联合自适应分类方法，结合了卡尔曼滤波（KF）的鲁棒水平提取和自适应高斯混合模型（a-GMM）的增强模式识别。仿真研究和范例实验表明，本文提出的KF/a-GMM分类方法能够有效地跟踪任务诱发脑激活模式的随机变化，与传统的心算和心唱任务分类方法相比，提高了心算和心唱任务的单次分类准确率。采用基于带通滤波（BPF）的特征提取器（均值、斜率和方差等）的组合的那些以及经典的识别器（GMM、SVM和LDA）。所提出的方法为开发实时fNIRS-BCI技术铺平了一条很有前途的道路。
Functional near-infrared spectroscopy (fNIRS) is a safe and non-invasive optical imaging technique that is being increasingly used in brain-computer interfaces (BCIs) to recognize mental tasks. Unlike electroencephalography (EEG) which directly measures neural activation, fNIRS signals reflect neurovascular-coupling inducing hemodynamic response that can be slow in time and varying in the pattern. The established classifiers extend the EEG-ones by mostly employing the feature based supervised models such as the support vector machine (SVM) and linear discriminant analysis (LDA), and fail to timely characterize the level-sensitive hemodynamic pattern. A dedicated classifier is desired for intentional activity recognition of fNIRS-BCI, including the adaptive acquisition of response relevant features and accurate discrimination of implied ideas. To this end, we herein propose a specifically-designed joint adaptive classification method that combines a Kalman filtering (KF) for robust level extraction and an adaptive Gaussian mixture model (a-GMM) for enhanced pattern recognition. The simulative investigations and paradigm experiments have shown that the proposed KF/a-GMM classification method can effectively track the random variations of task-evoked brain activation patterns, and improve the accuracy of single-trial classification task of mental arithmetic vs. mental singing, as compared to the conventional methods, e.g., those that employ combinations of the band-pass filtering (BPF) based feature extractors (mean, slope, and variance, etc.) and the classical recognizers (GMM, SVM, and LDA). The proposed approach paves a promising way for developing the real-time fNIRS-BCI technique.
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影响因子：
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