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首页> 外文会议>2016 2nd International Conference of Signal Processing and Intelligent Systems >Enhance evoked potentials detection using RBF neural networks: Application to brain-computer interface
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Enhance evoked potentials detection using RBF neural networks: Application to brain-computer interface

机译：使用RBF神经网络增强诱发电位检测：在脑机接口中的应用

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Brain-Computer Interface (BCI) is a specific type of human-computer interface that stablish the direct communication between human and computers by analyzing brain activities. Oddball paradigms are used in BCI to generate Event-Related Potentials (ERPs), like the P300 response, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300, allows the user to spell characters. The P300 speller is divided into in two classification problems. The first classification is for detect P300 in the electroencephalogram (EEG). The second one for determining the desired symbol by user. A new method for the detection of P300 waves is presented. This model is based on a Radial Basis Function Neural Network (RBFNN). The architecture of the network is adapted to the detection of P300 response in the time domain. Principal Component Analysis (PCA) is used for feature extraction. These models are tested and compared on the Data set III of BCI competition (2004). The proposed approach provides also a new way for analyzing brain activities due to the receptive field of the RBFNN models.
机译：脑机接口（BCI）是一种特定类型的人机接口，它通过分析脑部活动来稳定人机之间的直接通信。 BCI中使用奇数球范例在用户选择的目标上生成事件相关电位（ERP），如P300响应。 P300拼写器基于此原理，通过检测P300，用户可以拼写字符。 P300拼写器分为两个分类问题。第一种分类是用于检测脑电图（EEG）中的P300。第二个用于由用户确定所需的符号。提出了一种检测P300波的新方法。该模型基于径向基函数神经网络（RBFNN）。网络的体系结构适合于在时域中检测P300响应。主成分分析（PCA）用于特征提取。这些模型在BCI竞赛的数据集III（2004年）中进行了测试和比较。由于RBFNN模型的接受场，所提出的方法还提供了一种分析大脑活动的新方法。

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