Institute of Bioengineering, University Miguel Hernandez and CIBER BBN, Elche 03202, Spain.
Department of Electronics and Computer Technology, University of Cartagena, Cartagena 30202, Spain.
Int J Neural Syst. 2023 Jan;33(1):2250057. doi: 10.1142/S0129065722500575. Epub 2022 Dec 10.
The large range of potential applications, not only for patients but also for healthy people, that could be achieved by affective brain-computer interface (aBCI) makes more latent the necessity of finding a commonly accepted protocol for real-time EEG-based emotion recognition. Based on wavelet package for spectral feature extraction, attending to the nature of the EEG signal, we have specified some of the main parameters needed for the implementation of robust positive and negative emotion classification. Twelve seconds has resulted as the most appropriate sliding window size; from that, a set of 20 target frequency-location variables have been proposed as the most relevant features that carry the emotional information. Lastly, QDA and KNN classifiers and population rating criterion for stimuli labeling have been suggested as the most suitable approaches for EEG-based emotion recognition. The proposed model reached a mean accuracy of 98% (s.d. 1.4) and 98.96% (s.d. 1.28) in a subject-dependent (SD) approach for QDA and KNN classifier, respectively. This new model represents a step forward towards real-time classification. Moreover, new insights regarding subject-independent (SI) approximation have been discussed, although the results were not conclusive.
情感脑机接口 (aBCI) 具有广泛的潜在应用，不仅适用于患者，也适用于健康人群，这使得寻找一种普遍接受的基于实时 EEG 的情绪识别协议变得更加必要。基于小波包进行频谱特征提取，并考虑到 EEG 信号的性质，我们已经确定了实现稳健正、负情绪分类所需的一些主要参数。结果表明，12 秒是最合适的滑动窗口大小；从这个窗口中，我们提出了一组 20 个目标频率 - 位置变量作为最相关的特征，携带情绪信息。最后，我们提出了 QDA 和 KNN 分类器以及基于人群评分的刺激标记准则，作为基于 EEG 的情绪识别的最适合方法。该模型在 QDA 和 KNN 分类器的基于个体的（SD）方法中分别达到了 98%（标准差 1.4）和 98.96%（标准差 1.28）的平均准确率。该新模型代表了朝着实时分类的一步进展。此外，尽管结果尚不确定，但我们已经讨论了关于独立于个体的（SI）近似的新见解。
