Abstract
In the past few decades, lots of studies were proposed on investigations of brain circuits for physical health, mental health, educational learning, controlling system and so on. However, very few studies concentrate their attention on contextual brain recognition. Actually, the brain neural system is the main bridge between mental senses and physical behaviors, and the mental senses such as emotions and preferences are heavily impacted by the context, which can be represented by the brain signals. Hence, how to recognize the contextual brain signal for mental senses is the aimed issue of this paper. To deal with this issue, in this paper, we examine a number of machine learning methods on a real dataset, including traditional classifiers and LSTM (Long Short-Term Memory Neural Network). Also, a free Convolutional Neural Network called BrainCNN is proposed to recognize the contextual brain signal using a mobile brain-signal detection device. Through the brain-signal recognition, the context can be identified and thereby facilitates the prediction of mental senses in the future. The experimental results show the proposed BrainCNN is more promising than the other methods in recognizing the context brain signal. Further, this work provides a basic idea for future interests in investigating the contextual brain.
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Acknowledgement
This research was supported by Ministry of Science and Technology, Taiwan, R.O.C. under grant no. MOST 111–2221-E-390–014 and NSTC 111–2410-H-230 -003 -MY2.
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Su, JH., Chen, WJ., Zhang, MC., Liao, YW. (2023). Artificial Intelligences on Automated Context-Brain Recognition with Mobile Detection Devices. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2023. Lecture Notes in Computer Science(), vol 13995. Springer, Singapore. https://doi.org/10.1007/978-981-99-5834-4_31
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DOI: https://doi.org/10.1007/978-981-99-5834-4_31
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