1 research outputs found
Consumer Grade Brain Sensing for Emotion Recognition
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 high/low valence and arousal. Our setup
entailed recruiting 200 healthy volunteers of varying years of age to identify
the top 60 affective video clips from a total of 120 candidates through
standardized self assessment, genre tags, and unsupervised machine learning.
Additional 43 participants were enrolled to watch the pre-selected clips during
which emotional EEG brainwaves and peripheral physiological signals were
collected. These recordings were analyzed and extracted features fed into a
classification model to predict whether the elicited signals were associated
with a high or low level of valence and arousal. As it turned out, our
prediction accuracies were decidedly comparable to those of previous studies
that utilized more costly EEG amplifiers for data acquisition