Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France; Département d'Etudes Cognitives, Ecole Normale Supérieure, France; UCL Ear Institute, United Kingdom.
Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France; Département d'Etudes Cognitives, Ecole Normale Supérieure, France.
Neuroimage. 2018 May 15;172:206-216. doi: 10.1016/j.neuroimage.2018.01.033. Epub 2018 Jan 31.
The relation between a stimulus and the evoked brain response can shed light on perceptual processes within the brain. Signals derived from this relation can also be harnessed to control external devices for Brain Computer Interface (BCI) applications. While the classic event-related potential (ERP) is appropriate for isolated stimuli, more sophisticated "decoding" strategies are needed to address continuous stimuli such as speech, music or environmental sounds. Here we describe an approach based on Canonical Correlation Analysis (CCA) that finds the optimal transform to apply to both the stimulus and the response to reveal correlations between the two. Compared to prior methods based on forward or backward models for stimulus-response mapping, CCA finds significantly higher correlation scores, thus providing increased sensitivity to relatively small effects, and supports classifier schemes that yield higher classification scores. CCA strips the brain response of variance unrelated to the stimulus, and the stimulus representation of variance that does not affect the response, and thus improves observations of the relation between stimulus and response.
刺激与诱发脑反应之间的关系可以揭示大脑内部的感知过程。从这种关系中提取的信号也可以被用来控制脑机接口（BCI）应用中的外部设备。虽然经典的事件相关电位（ERP）适用于孤立的刺激，但需要更复杂的“解码”策略来处理连续的刺激，如语音、音乐或环境声音。在这里，我们描述了一种基于典型相关分析（CCA）的方法，该方法找到将刺激和反应应用于揭示两者之间相关性的最佳变换。与基于刺激-反应映射的前向或后向模型的先前方法相比，CCA 发现了显著更高的相关分数，从而提高了对相对较小影响的敏感性，并支持分类器方案，从而产生更高的分类分数。CCA 去除了与刺激无关的脑反应方差和不影响反应的刺激表示方差，从而改善了对刺激与反应之间关系的观察。
