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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. [ABSTRACT FROM AUTHOR]

Miner’s wearable robot is an important mobile terminal of the monitoring network for the coal mine production safety. However, it is difficult to find the study on the miner’s emotion change using brain-computer interface (BCI) for miner’s wearable robot, especially for the smart helmet. This paper explores the anxiety change rule and the detection method using BCI of miner’s smart helmet. There are three contributions in this paper. Firstly, an emotional state evoked paradigm is designed to find the brain area where the emotion feature is most obvious. Then, the accurate electrode position is determined for the electroencephalograph (EEG) collection of the negative emotion on the basis of the international 10-20 systems. Secondly, a fusion algorithm of the anxiety level is proposed to evaluate the miner’s mental state by using the θ, α, and β rhythms of EEG. Thirdly, the miner’s smart helmet system is built to collect the human state which includes the mental parameters of the anxiety level, the fatigue level, the concentration level, and the environmental parameter in coal mine. Experiments demonstrate that the position Fp2 is the best electrode position for obtaining the anxiety level parameter. The most obvious EEG changes appear within the first 2 s after the stimulator works. The amplitudes of the θ rhythm increase most obviously in the negative emotion. In addition, the fusion algorithm of the anxiety level parameter has a good following function to the negative emotion change. This method can reflect quantitatively the anxiety level of a human. So it is avoided for the miner to operate improperly, and then the operation safety can be improved. [ABSTRACT FROM AUTHOR]