Abstract
In this paper, we analyze the participants’ state of mind through the measurement of EEG readings like alpha, theta, gamma, and beta waves. To obtain the EEG readings, we use OpenBCI with its Cyton Bluetooth helmet product. Due to its higher temporal resolution, EEG is an important noninvasive method for studying the transient dynamics of the human brain’s neuronal circuitry. EEG provides useful observational data of variability in different mental states. Thus, since stress affects neural activity, EEG signals are the ideal tool to measure it. Our objective is to understand the relationship between mental states and trading results. We believe that understanding these relationships can potentially translate into improved trading performance and profitability in traders.
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References
Fenton-O’Creevy, M., et al.: Emotion regulation and trader expertise: heart rate variability on the trading floor. J. Neurosci. Psychol. Econ. 5(4), 227–237 (2012)
Hamid, N.H.A., Sulaiman, N., Aris, S.A.M., Murat, Z.H., Taib, M.N.: Evaluation of human stress using the EEG power spectrum. In: 6th International Colloquium on Signal Processing & its Applications, pp. 1–4. IEEE (2010)
Seo, S.H., Lee, J.T.: Stress and EEG in Convergence and Hybrid Information Technologies, pp. 413–426 (2010)
Mohammadpour, M., Hashemi, S.M.R., Houshmand, N.: Classification of EEG-based emotion for BCI applications. In: Artificial Intelligence and Robotics (IRANOPEN), pp. 127–131. IEEE (2017)
Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S., Zunaidi, I., Hazry, D.: Lifting scheme for human emotion recognition using EEG. In: International Symposium on Information Technology, pp. 1–7. IEEE (2008)
Jenke, R., Peer, A., Buss, M.: Feature extraction and selection for emotion recognition from EEG. IEEE Trans. Affective Comput. 5(3), 327–339 (2014)
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Torres, E.P., Torres, E.A., Hernández-Álvarez, M., Yoo, S.G. (2021). Machine Learning Analysis of EEG Measurements of Stock Trading Performance. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-51328-3_9
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DOI: https://doi.org/10.1007/978-3-030-51328-3_9
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