基于STN-LFP信号的多核学习人类行为任务分类方法
A Multiple Kernel Learning Approach for Human Behavioral Task Classification using STN-LFP Signal
摘要：
脑深部电刺激（DBS）作为缓解帕金森病（PD）障碍的有效方法越来越受到关注。现有的DBS系统是开环的，使得系统参数不能基于患者行为自动调整。人类行为的分类是设计下一代闭环DBS系统的重要一步。本文提出了一种基于丘脑底核局部场电位（LFP）信号的行为任务分类方法。在我们的方法中，我们使用从左和右STNs记录的原始LFP信号的时频表示（频谱图）作为特征向量。然后，这些功能结合在一起，通过支持向量机（SVM）与多核学习（MKL）制定。基于MKL的分类方法被用来分类不同的任务：按钮按下，嘴部运动，语音和手臂运动。我们的实验表明，LP-范数MKL显着优于单核SVM为基础的分类器在分类行为任务的五个主题，即使使用信号采集低采样率为10 Hz。这导致较低的计算成本。
英文摘要：
Deep Brain Stimulation (DBS) has gained increasing attention as an effective method to mitigate Parkinsons disease (PD) disorders. Existing DBS systems are open-loop such that the system parameters are not adjusted automatically based on patients behavior. Classification of human behavior is an important step in the design of the next generation of DBS systems that are closed-loop. This paper presents a classification approach to recognize such behavioral tasks using the subthalamic nucleus (STN) Local Field Potential (LFP) signals. In our approach, we use the time-frequency representation (spectrogram) of the raw LFP signals recorded from left and right STNs as the feature vectors. Then these features are combined together via Support Vector Machines (SVM) with Multiple Kernel Learning (MKL) formulation. The MKL-based classification method is utilized to classify different tasks: button press, mouth movement, speech, and arm movement. Our experiments show that the lp-norm MKL significantly outperforms single kernel SVM-based classifiers in classifying behavioral tasks of five subjects even using signals acquired with a low sampling rate of 10 Hz. This leads to a lower computational cost.
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