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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >A new approach for EEG feature extraction in P300-based lie detection.
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A new approach for EEG feature extraction in P300-based lie detection.

机译：基于P300的测谎中脑电特征提取的新方法。

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摘要

P300-based Guilty Knowledge Test (GKT) has been suggested as an alternative approach for conventional polygraphy. The purpose of this study was to extend a previously introduced pattern recognition method for the ERP assessment in this application. This extension was done by the further extending the feature set and also the employing a method for the selection of optimal features. For the evaluation of the method, several subjects went through the designed GKT paradigm and their respective brain signals were recorded. Next, a P300 detection approach based on some features and a statistical classifier was implemented. The optimal feature set was selected using a genetic algorithm from a primary feature set including some morphological, frequency and wavelet features and was used for the classification of the data. The rates of correct detection in guilty and innocent subjects were 86%, which was better than other previously used methods.
机译：已建议将基于P300的犯罪知识测验（GKT）作为常规测谎的替代方法。这项研究的目的是扩展先前引入的模式识别方法用于此应用程序中的ERP评估。通过进一步扩展功能集并采用一种选择最佳功能的方法来完成此扩展。为了评估该方法，一些受试者经历了设计的GKT范例，并记录了他们各自的大脑信号。接下来，实现了基于某些功能和统计分类器的P300检测方法。使用遗传算法从包括一些形态，频率和小波特征的主要特征集中选择最佳特征集，并将其用于数据分类。有罪和无辜受试者的正确检出率是86％，比以前使用的其他方法要好。

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