Consumer Grade Brain Sensing for Emotion Recognition
Payongkit Lakhan, Nannapas Banluesombatkul, Vongsagon Changniam,, Ratwade Dhithijaiyratn, Pitshaporn Leelaarporn, Ekkarat Boonchieng, Supanida, Hompoonsup, Theerawit Wilaiprasitporn

TL;DR
This study demonstrates that consumer-grade EEG devices like OpenBCI can effectively recognize emotional states, matching the performance of research-grade systems, and introduces a novel stimulus selection method for emotion elicitation.
Contribution
It evaluates OpenBCI's performance in emotion recognition and proposes a new method for selecting high/low valence and arousal stimuli using machine learning.
Findings
OpenBCI achieved comparable accuracy to high-end EEG systems.
A new machine learning-based stimulus selection method was effective.
Emotion classification accuracy was consistent with prior research.
Abstract
For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets have been generated with the use of diverse emotion-eliciting stimuli and the resulting brainwave responses conventionally captured with high-end EEG devices. However, the applicability of these devices is to some extent limited by practical constraints and may prove difficult to be deployed in highly mobile context omnipresent in everyday happenings. In this study, we evaluate the potential of OpenBCI to bridge this gap by first comparing its performance to research grade EEG system, employing the same algorithms that were applied on benchmark datasets. Moreover, for the purpose of emotion classification, we propose a novel method to facilitate the selection of audio-visual stimuli of…
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