Human Emotion Recognition Using an EEG Cloud Computing Platform

Abstract

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.