Consumer Grade Brain Sensing for Emotion Recognition

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 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.

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

“…Lakhan et al ( 2019 ) used consumer headsets in affective computing. With a study of 200 healthy subjects, they claimed predictive accuracies approximating those on costlier EEG systems.…”

“…The complete omission of any results was explicitly mentioned in their future work sections, however. Lakhan et al (2019) used consumer headsets in affective computing. With a study of 200 healthy subjects, they claimed predictive accuracies approximating those on costlier EEG systems.…”

Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review

“…Digital devices present a wide range of opportunities for healthcare professionals, ranging from individual health to the common population. Digital devices, especially mobile or wearable devices, are increasingly capable of capturing various sources of real-time behavioral, physiological, and psychosocial data in a precise and confidential manner [13]- [15]. Examples of these technologies include smartphones [16]- [18] and smartwatches [19], [20].…”

Emotion Sensing From Head Motion Capture

“…Several works have also used body gesture to recognize emotion [9], [10]. Usage of brain activity sensing through electroencephalography (EEG) for emotion recognition is gaining growing attention in recent years [11], [12]. Head motion, however, has received relatively less attention, although its importance has been noted in several research [13]- [16].…”