A proposal for applying pdi-Boosting to brain-computer interfaces
A proposal for applying pdi-Boosting to brain-computer interfaces
复制标题
将pdi-Boosting应用于脑机接口的提案
DOI：
10.1109/fuzz-ieee.2012.6251152
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
R. Kozma
中科院分区：
文献类型：
--
作者：
I. Hayashi;Shinji Tsuruse;J. Suzuki;R. Kozma
脑机接口(BCI)和脑机接口(BMI)技术最近进入了研究的聚光灯下。在许多这样的系统中，外部计算机和机器由使用近红外光谱(NIR)或脑电图仪(EEG)设备测量的大脑活动信号控制。在本文中，我们提出了一种新的基于概率数据内插的脑机接口Boosting算法。在我们的模型中，内插数据是使用概率分布函数在分类错误周围生成的，而不是传统的AdaBoost，它增加了对应于错误分类示例的权重。通过对插补数据的分析，验证了识别边界对外部机床的有效控制。我们通过一个实验来验证我们的增强方法，在这个实验中，我们从执行基本算术任务的受试者那里获得了NIRS数据，并对结果进行了讨论。
Brain-computer interface (BCI) and brain-machine interface (BMI) technologies have recently entered the research limelight. In many such systems, external computers and machines are controlled by brain activity signals measured using near-infrared spectroscopy (NIRS) or electroencephalograph (EEG) devices. In this paper, we propose a novel boosting algorithm for BCI using a probabilistic data interpolation scheme. In our model, interpolated data is generated around classification errors using a probability distribution function, as opposed to conventional AdaBoost which increases weights corresponding to the misclassified examples. By using the interpolated data, the discriminated boundary is shown to control the external machine effectively. We verify our boosting method with an experiment in which NIRS data is obtained from subjects performing a basic arithmetic task, and discuss the results.
影响因子：
5.7
作者：
Sitaram, Ranganatha;Zhang, Haihong;Birbaumer, Niels
通讯作者：
Birbaumer, Niels