Emotion recognition using EEG has been widely studied to address the challenges associated with affective computing. Using manual feature extraction method on EEG signals result in sub-optimal performance by the learning models. With the advancements in deep learning as a tool for automated feature engineering, in this work a hybrid of manual and automatic feature extraction method has been proposed. The asymmetry in the different brain regions are captured in a 2-D vector, termed as AsMap from the differential entropy (DE) features of EEG signals. These AsMaps are then used to extract features automatically using Convolutional Neural Network (CNN) model. The proposed feature extraction method has been compared with DE and other DE-based feature extraction methods such as RASM, DASM and DCAU. Experiments are conducted using DEAP and SEED dataset on different classification problems based on number of classes. Results obtained indicate that the proposed method of feature extraction results in higher classification accuracy outperforming the DE based feature extraction methods. Highest classification accuracy of 97.10% is achieved on 3-class classification problem using SEED dataset. Further, the impact of window size on classification accuracy has also been assessed in this work.
翻译:使用 EEG 进行情感识别已经广泛研究,以解决与情感计算有关的挑战。在 EEG 信号上使用人工特征提取方法,使学习模型的性能达到次优。随着作为自动特征工程工具的深层次学习的进步,在这项工作中提出了人工和自动特征提取方法的混合。不同脑区域的不对称性被记录在一个二维矢量中,称为AsMap,它来自EEEG 信号的差别性激素(DE)特性。这些AsMaps随后被用来使用Concial神经网络(CNN)模型自动提取特征。拟议的特征提取方法已经与DE和其他基于DE的特征提取方法(如RASM、DASM和DCAU)进行了比较。还利用DEAP和SECD数据组对不同分类问题进行了实验。获得的结果表明,在更高分类精度的分类精度比基于 EEG 特征提取方法的性能。在3级分类问题上,使用SECD 数据集实现了97.10%的最高分类精确度。此外,在这项工作中还评估了窗口大小对精确度的影响。