Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface
Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface
复制标题
人工神经网络通过脑机接口评估情绪状态
DOI：
10.3390/electronics7120384
复制
发表时间：
2018
期刊：
影响因子：
2.9
通讯作者：
Pascual González
中科院分区：
文献类型：
--
作者：
Roberto Sánchez;A. García;Miguel A. Vicente;L. Fernández;María T. López;A. Fernández;Pascual González
人类情绪的估计在现代脑机接口设备的开发中起着重要的作用，比如EPOC+耳机。在本文中，我们提出了一个实验，以评估耳机的应用程序编程接口（API）提供的情绪状态的分类准确性。在这个实验中，从国际情感图片系统（IAPS）数据集中选择的几组图像显示给十六名戴着耳机的参与者。首先，参与者的反应形式的一个自我评估的假人问卷的情绪引发的比较与验证IAPS预定义的效价，唤醒和优势值。在统计学上证明了响应与IAPS值高度相关之后，测试了基于多层感知器架构的几个人工神经网络（ANN）来计算连续性EPOC+ API情绪结果的分类精度。最好的结果是得到一个人工神经网络配置有三个隐藏层，30，8和3个神经元层1，2和3，分别。这种配置提供了85%的分类准确度，这意味着耳机提供的情绪估计可以在基于用户情绪状态的实时应用中以高置信度使用。因此，可以使用由头戴式耳机的API给出的情绪状态，而不对从头皮获取的脑电图信号进行进一步处理，这将增加一定程度的难度。
Estimation of human emotions plays an important role in the development of modern brain-computer interface devices like the Emotiv EPOC+ headset. In this paper, we present an experiment to assess the classification accuracy of the emotional states provided by the headset’s application programming interface (API). In this experiment, several sets of images selected from the International Affective Picture System (IAPS) dataset are shown to sixteen participants wearing the headset. Firstly, the participants’ responses in form of a self-assessment manikin questionnaire to the emotions elicited are compared with the validated IAPS predefined valence, arousal and dominance values. After statistically demonstrating that the responses are highly correlated with the IAPS values, several artificial neural networks (ANNs) based on the multilayer perceptron architecture are tested to calculate the classification accuracy of the Emotiv EPOC+ API emotional outcomes. The best result is obtained for an ANN configuration with three hidden layers, and 30, 8 and 3 neurons for layers 1, 2 and 3, respectively. This configuration offers 85% classification accuracy, which means that the emotional estimation provided by the headset can be used with high confidence in real-time applications that are based on users’ emotional states. Thus the emotional states given by the headset’s API may be used with no further processing of the electroencephalogram signals acquired from the scalp, which would add a level of difficulty.