Kabbara Aya, Khalil Mohamad, El-Falou Wassim, Eid Hassan, Hassan Mahmoud
Department of electrical and computer engineering, ULFG1, Tripoli, Lebanon.
Azm center for research in biotechnology and its applications, EDST, Tripoli, Lebanon.
PLoS One. 2016 Jan 11;11(1):e0146282. doi: 10.1371/journal.pone.0146282. eCollection 2016.
The brain is a large-scale complex network often referred to as the "connectome". Cognitive functions and information processing are mainly based on the interactions between distant brain regions. However, most of the 'feature extraction' methods used in the context of Brain Computer Interface (BCI) ignored the possible functional relationships between different signals recorded from distinct brain areas. In this paper, the functional connectivity quantified by the phase locking value (PLV) was introduced to characterize the evoked responses (ERPs) obtained in the case of target and non-targets visual stimuli. We also tested the possibility of using the functional connectivity in the context of 'P300 speller'. The proposed approach was compared to the well-known methods proposed in the state of the art of "P300 Speller", mainly the peak picking, the area, time/frequency based features, the xDAWN spatial filtering and the stepwise linear discriminant analysis (SWLDA). The electroencephalographic (EEG) signals recorded from ten subjects were analyzed offline. The results indicated that phase synchrony offers relevant information for the classification in a P300 speller. High synchronization between the brain regions was clearly observed during target trials, although no significant synchronization was detected for a non-target trial. The results showed also that phase synchrony provides higher performance than some existing methods for letter classification in a P300 speller principally when large number of trials is available. Finally, we tested the possible combination of both approaches (classical features and phase synchrony). Our findings showed an overall improvement of the performance of the P300-speller when using Peak picking, the area and frequency based features. Similar performances were obtained compared to xDAWN and SWLDA when using large number of trials.
大脑是一个大规模复杂网络,常被称为“连接组”。认知功能和信息处理主要基于远距离脑区之间的相互作用。然而,脑机接口(BCI)背景下使用的大多数“特征提取”方法都忽略了从不同脑区记录的不同信号之间可能存在的功能关系。本文引入通过锁相值(PLV)量化的功能连接来表征在目标和非目标视觉刺激情况下获得的诱发反应(ERP)。我们还测试了在“P300 拼写器”背景下使用功能连接的可能性。将所提出的方法与“P300 拼写器”现有技术中提出的知名方法进行了比较,主要包括峰值提取、面积、基于时间/频率的特征、xDAWN 空间滤波和逐步线性判别分析(SWLDA)。对从 10 名受试者记录的脑电图(EEG)信号进行了离线分析。结果表明,相位同步为 P300 拼写器中的分类提供了相关信息。在目标试验期间明显观察到脑区之间的高度同步,而在非目标试验中未检测到明显的同步。结果还表明,相位同步在 P300 拼写器中对字母分类提供的性能高于一些现有方法,主要是在有大量试验可用时。最后,我们测试了两种方法(经典特征和相位同步)的可能组合。我们的研究结果表明,在使用峰值提取、面积和基于频率的特征时,P300 拼写器的性能总体上有所提高。在使用大量试验时,与 xDAWN 和 SWLDA 相比获得了相似的性能。