Alvarez-Meza Andres M, Orozco-Gutierrez Alvaro, Castellanos-Dominguez German
Automatics Research G., Universidad Tecnologica de Pereira, Pereira, Colombia.
Signal Processing and Recognition G., Universidad Nacional de Colombia, Manizales, Colombia.
Front Neurosci. 2017 Oct 6;11:550. doi: 10.3389/fnins.2017.00550. eCollection 2017.
We introduce (EKRA) that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i) feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii) enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand.
我们引入了（EKRA），其旨在支持使用脑电图记录自动识别大脑活动模式。EKRA是一种数据驱动策略，它结合了两个核函数以利用可用的联合信息，将神经反应与给定的刺激条件相关联。关于此，调整一个 以学习能最佳区分输入特征集的线性投影，自动优化所需的自由参数。我们的方法在两种情况下进行：（i）通过从提取的神经特征计算相关向量来进行特征选择，以促进对给定大脑活动任务的生理学解释，以及（ii）增强特征选择以对相关特征执行额外变换，旨在提高整体识别准确性。相应地，我们提供了一种替代的特征相关性分析策略，其在有利于数据可解释性的同时允许提高系统性能。为了验证目的，在两项著名的大脑活动任务中对EKRA进行了测试：运动想象辨别和癫痫发作检测。获得的结果表明，EKRA方法估计了从提供的监督信息中提取的相关表示空间，突出了显著的输入特征。因此，我们的提议在大脑活动辨别准确性方面优于现有方法，同时有利于对手头任务进行增强的生理学解释。
