| 基于EOG的扫视角度识别与分类研究 |
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| 引用本文： | 王波,吴小培.基于EOG的扫视角度识别与分类研究[J].电子测量技术,2010,33(8):39-42,49. |
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| 作者姓名： | 王波 吴小培 |
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| 作者单位： | 安徽大学计算智能与信号处理教育部重点实验室,合肥,230039 |
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| 基金项目： | 国家自然科学基金，博士点基金 |
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| 摘 要： | 论文对基于眼电信号（EOG）的扫视角度识别算法进行研究。设计了扫视角度定位和EOG采集实验。提出了联合线性预测系数和波形参数的EOG特征描述新方法;并使用BP神经网络与支持向量机对特征向量进行识别和分类。实验结果表明,本文所提出的方法能较好地识别出不同扫视角度的眼动模式。
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| 关 键 词： | 眼电 扫视角度 线性预测系数 BP神经网络 支持向量机 |
Reaserch on saccadic angle recognition and classification based on EOG |
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| Wang Bo,Wu Xiaopei.Reaserch on saccadic angle recognition and classification based on EOG[J].Electronic Measurement Technology,2010,33(8):39-42,49. |
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| Authors: | Wang Bo Wu Xiaopei |
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| Affiliation: | Wang Bo Wu Xiaopei(The Key Laboratory of Intelligent Computing＆Signal Processing of,Anhui University,Hefei 230039) |
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| Abstract: | This paper researches the saccadic angle recognition algorithm based on electrooculogram（EOG）.The experiment of EOG signal collection at different horizontal saccadic angle is carried out.The linear prediction coefficients（LPC）and waveform features are combined to be the feature vector of EOG signal;then the BP neural network and support vector machine are used to identify and classify the feature vectors.The result of experiment shows that the method proposed can recognize the eye movement with different saccadic angles effectively. |
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| Keywords: | electrooculogram saccadic angle linear predictive coding BP neural network support vector machine |
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