Ran Shuang, Zhong Wei, Duan Danting, Ye Long, Zhang Qin
Key Laboratory of Media Audio & Video, Ministry of Education, Communication University of China, Beijing, China.
State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China.
Front Hum Neurosci. 2023 Jun 1;17:1132254. doi: 10.3389/fnhum.2023.1132254. eCollection 2023.
EEG signals can non-invasively monitor the brain activities and have been widely used in brain-computer interfaces (BCI). One of the research areas is to recognize emotions objectively through EEG. In fact, the emotion of people changes over time, however, most of the existing affective BCIs process data and recognize emotions offline, and thus cannot be applied to real-time emotion recognition.
In order to solve this problem, we introduce the instance selection strategy into transfer learning and propose a simplified style transfer mapping algorithm. In the proposed method, the informative instances are firstly selected from the source domain data, and then the update strategy of hyperparameters is also simplified for style transfer mapping, making the model training more quickly and accurately for a new subject.
To verify the effectiveness of our algorithm, we carry out the experiments on SEED, SEED-IV and the offline dataset collected by ourselves, and achieve the recognition accuracies up to 86.78%, 82.55% and 77.68% in computing time of 7s, 4s and 10s, respectively. Furthermore, we also develop a real-time emotion recognition system which integrates the modules of EEG signal acquisition, data processing, emotion recognition and result visualization.
Both the results of offline and online experiments show that the proposed algorithm can accurately recognize emotions in a short time, meeting the needs of real-time emotion recognition applications.
脑电图（EEG）信号能够以非侵入性方式监测大脑活动，并且已在脑机接口（BCI）中得到广泛应用。其中一个研究领域是通过脑电图客观地识别情绪。事实上，人的情绪会随时间变化，然而，现有的大多数情感脑机接口都是对数据进行离线处理和情绪识别，因此无法应用于实时情绪识别。
为了解决这个问题，我们将实例选择策略引入迁移学习，并提出一种简化的风格迁移映射算法。在所提出的方法中，首先从源域数据中选择信息丰富的实例，然后还简化了超参数的更新策略以进行风格迁移映射，从而使模型针对新对象的训练更加快速和准确。
为了验证我们算法的有效性，我们在SEED、SEED-IV以及我们自己收集的离线数据集上进行了实验，在7秒、4秒和10秒的计算时间内分别达到了86.78%、82.55%和77.68%的识别准确率。此外，我们还开发了一个实时情绪识别系统，该系统集成了脑电图信号采集、数据处理、情绪识别和结果可视化等模块。
离线和在线实验结果均表明，所提出的算法能够在短时间内准确识别情绪，满足实时情绪识别应用的需求。
