Near infrared spectroscopy is the promising and noninvasive technique that can be used to detect the brain functional activation by monitoring the concentration alternations in the haemodynamic concentration. The acquired NIRS signals are commonly contaminated by physiological interference caused by breathing and cardiac contraction. Though the adaptive filtering method with least mean squares algorithm or recursive least squares algorithm based on multidistance probe configuration could improve the quality of evoked brain activity response, both methods can only remove the physiological interference occurred in superficial layers of the head tissue. To overcome the shortcoming, we combined the recursive least squares adaptive filtering method with the least squares support vector machine to suppress physiological interference both in the superficial layers and deeper layers of the head tissue. The quantified results based on performance measures suggest that the estimation performances of the proposed method for the evoked haemodynamic changes are better than the traditional recursive least squares method.

中文翻译：

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基于递归最小二乘自适应滤波和最小二乘支持向量机的近红外光谱脑活动测量的生理干扰减少。

近红外光谱是一种有前途且无创的技术，可通过监测血流动力学浓度的浓度变化来检测脑功能激活。所获取的NIRS信号通常被呼吸和心脏收缩引起的生理干扰所污染。尽管基于多距离探针配置的具有最小均方算法或递归最小二乘算法的自适应滤波方法可以提高诱发的大脑活动反应的质量，但这两种方法都只能消除发生在头部组织表层的生理干扰。为了克服缺点，我们将递归最小二乘自适应滤波方法与最小二乘支持向量机相结合，以抑制头部组织的浅层和深层的生理干扰。基于性能指标的量化结果表明，该方法对诱发的血流动力学变化的估计性能优于传统的递归最小二乘方法。