Information Technologies Institute, Centre for Research and Technology Hellas, CERTH-ITI, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, Greece.
Sensors (Basel). 2023 Feb 23;23(5):2480. doi: 10.3390/s23052480.
In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the Sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy).
在这项工作中，我们提出了一个新的框架，使用 EEG 信号识别基于神经营销的刺激的大脑的认知和情感过程。我们方法的最关键部分是基于稀疏表示分类方案的提议的分类算法。我们方法的基本假设是，认知或情感过程的 EEG 特征位于线性子空间上。因此，测试脑信号可以表示为训练集中所有类别的脑信号的线性（或加权）组合。通过采用基于图的先验的稀疏贝叶斯框架来确定脑信号的类别成员，该先验对线性组合的权重进行了限定。此外，通过使用线性组合的残差来构建分类规则。使用公开可用的神经营销 EEG 数据集进行的实验证明了我们方法的有用性。对于所采用数据集提供的两个分类任务，即情感状态识别和认知状态识别，与基线和最先进的方法相比，所提出的分类方案设法实现了更高的分类准确性（分类准确性提高了 8%以上）。
