LIRINS - Facultad de Ingeniería, Universidad Nacional de Entre Rios, Oro Verde, Argentina.
Facultad de Ingenieria, Universidad Nacional de Entre Rios, Oro Verde, Argentina.
Med Biol Eng Comput. 2019 Mar;57(3):589-600. doi: 10.1007/s11517-018-1898-9. Epub 2018 Sep 28.
The P300 component of event-related potentials (ERPs) is widely used in the implementation of brain computer interfaces (BCI). In this context, one of the main issues to solve is the binary classification problem that entails differentiating between electroencephalographic (EEG) signals with and without P300. Given the particularly unfavorable signal-to-noise ratio (SNR) in the single-trial detection scenario, this is a challenging problem in the pattern recognition field. To the best of our knowledge, there are no previous experimental studies comparing feature extraction and selection methods for single trial P300-based BCIs using unified criteria and data. In order to improve the performance and robustness of single-trial classifiers, we analyzed and compared different alternatives for the feature generation and feature selection blocks. We evaluated different orthogonal decompositions based on the wavelet transform for feature extraction, as well as different filter, wrapper, and embedded alternatives for feature selection. Accuracies over 75% were obtained for most of the analyzed strategies with a relatively low computational cost, making them attractive for a practical BCI implementation using inexpensive hardware. Graphical Abstract Experiments performed for P300 detection.
事件相关电位（ERPs）中的 P300 成分广泛应用于脑机接口（BCI）的实现中。在这种情况下，需要解决的主要问题之一是二进制分类问题，即区分有无 P300 的脑电图（EEG）信号。鉴于单试检测场景中特别不利的信噪比（SNR），这是模式识别领域的一个具有挑战性的问题。据我们所知，以前没有使用统一标准和数据的基于单试 P300 的 BCI 的特征提取和选择方法的实验研究。为了提高单试分类器的性能和鲁棒性，我们分析和比较了特征生成和特征选择块的不同替代方案。我们评估了基于小波变换的不同正交分解进行特征提取，以及不同的滤波、包装和嵌入式选择进行特征选择。对于大多数分析策略，都可以获得超过 75%的准确率，并且计算成本相对较低，这使得它们对于使用廉价硬件的实际 BCI 实现具有吸引力。
图摘要 P300 检测实验。
