Al-Nafjan Abeer, Alharthi Khulud, Kurdi Heba
Computer Science Department, Imam Muhammad ibn Saud Islamic University, Riyadh 11432, Saudi Arabia.
Computer Science Department, King Saud University, Riyadh 11543, Saudi Arabia.
Brain Sci. 2020 Oct 26;10(11):781. doi: 10.3390/brainsci10110781.
Brain-computer interface (BCI) technology provides a direct interface between the brain and an external device. BCIs have facilitated the monitoring of conscious brain electrical activity via electroencephalogram (EEG) signals and the detection of human emotion. Recently, great progress has been made in the development of novel paradigms for EEG-based emotion detection. These studies have also attempted to apply BCI research findings in varied contexts. Interestingly, advances in BCI technologies have increased the interest of scientists because such technologies' practical applications in human-machine relationships seem promising. This emphasizes the need for a building process for an EEG-based emotion detection system that is lightweight, in terms of a smaller EEG dataset size and no involvement of feature extraction methods. In this study, we investigated the feasibility of using a spiking neural network to build an emotion detection system from a smaller version of the DEAP dataset with no involvement of feature extraction methods while maintaining decent accuracy. The results showed that by using a NeuCube-based spiking neural network, we could detect the valence emotion level using only 60 EEG samples with 84.62% accuracy, which is a comparable accuracy to that of previous studies.
脑机接口（BCI）技术提供了大脑与外部设备之间的直接接口。BCI有助于通过脑电图（EEG）信号监测有意识的脑电活动以及检测人类情绪。最近，基于EEG的情绪检测新范式的发展取得了巨大进展。这些研究还试图将BCI研究结果应用于各种情境。有趣的是，BCI技术的进步增加了科学家们的兴趣，因为此类技术在人机关系中的实际应用似乎很有前景。这强调了构建一个基于EEG的情绪检测系统的必要性，该系统在EEG数据集规模较小且不涉及特征提取方法方面是轻量级的。在本研究中，我们研究了使用脉冲神经网络从较小版本的DEAP数据集中构建情绪检测系统的可行性，在不涉及特征提取方法的情况下保持良好的准确性。结果表明，通过使用基于NeuCube的脉冲神经网络，我们仅使用60个EEG样本就能检测效价情绪水平，准确率达84.62%，这与之前研究的准确率相当。
