Machine Learning Analysis of EEG Measurements of Stock Trading Performance

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.

Año de publicación:

2021

Keywords:

• Emotion recognition
• EEG
• Brain-Computer Interface
• Machine learning

Fuente:

scopus