Human Emotion Recognition Using an EEG Cloud Computing Platform
- 61 Downloads
Human wearable helmet is a useful tool for monitoring the status of miners in the mining industry. However, there is little research regarding human emotion recognition in an extreme environment. To the best of our knowledge, this paper is the first to describe the human anxiety change rule and to propose a cloud computing platform for detecting human emotions using brain-computer interface (BCI) devices. In this paper, an emotional state evoked paradigm is designed to identify the brain area where the emotion feature is most evident. Next, the correct electrode position is determined for the collection of the negative emotion by the electroencephalograph (EEG) based on the international 10–20 system of electrode placement. Next, a fusion algorithm of the anxiety level is proposed to evaluate the person’s mental state using the θ, α, and β rhythms of an EEG. Next, the human smart helmet system is designed to collect the human state, which includes the mental parameters of the anxiety level, the fatigue level, the concentration level, and the environmental parameters in the coal mine. Experiments demonstrate that the position Fp2 is the best electrode position for obtaining the anxiety level parameter. The most visible EEG changes appear within the first 2 s following stimulation. The amplitudes of the θ rhythm increase most significantly in the negative emotional state. The fusion algorithm of the anxiety level accurately measures negative emotional change.
KeywordsEmotion recognition EEG Cloud computing Internet of things
This work was supported by Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746), Grant in Aid for Scientific Research of JSPS (17 K14694), Research Fund of State Key Laboratory of Marine Geology in Tongji University (MGK1608), Research Fund of State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University (1510), Research Fund of The Telecommunications Advancement Foundation, Fundamental Research Developing Association for Shipbuilding and Offshore, Strengthening Research Support Project of Kyushu Institute of Technology, China National Natural Science Foundation under Grant 61702553 and in part by the MOE (Ministry of Education in China) Project of Humanities and Social Sciences under Grant 17YJCZH252.
- 1.Lu H, Li Y, Chen M, Kim H, Serikawa S (2017) Brain intelligence: go beyond artificial intelligence. Mobile Networks and Application 1-10Google Scholar
- 2.Khalili Z, Moradi M (2009) Emotion recognition system using brain and peripheral signals: using correlation dimension to improve the results of EEG. In: Proc. of International Joint Conference on Neural Networks. IEEE Press, Piscataway, pp 1920–1924Google Scholar
- 7.Hu J (2017) Comparison of different features and classifiers for driver fatigue detection based on a single EEG channel. Computational and Mathematical Methods in Medicine 2017:1–9Google Scholar
- 8.Langp J, Bradleym M, Cuthbertb H (1997) International affective picture system (IAPS): technical manual and affective ratings, pp 1-30Google Scholar
- 13.Wang M, Xu C, Lu H (2017) Fault location without wave velocity influence using wavelet and Clark transform”, Artificial Intelligence and Robotics 321-326Google Scholar
- 20.Lu H, Li Y, Li Y, Serikawa S, Kim H (2017) Highly accurate energy-conserving flexible touch sensors. Sensors and Materials 29(6):611–617Google Scholar