IBPoet

Rosenthal, James; Benabdallah, Gabrielle

doi:10.1145/3123024.3123176

Cited by 2 publications

References 2 publications

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Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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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.

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Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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Fabulating Biodata Futures for Living and Knowing Together

Tsaknaki

Sanches

Jenkins

et al. 2022

Designing Interactive Systems Conference

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A growing number of design researchers explore engagement with and through biodata. To help make sense of this growing space, we synthesize three emergent themes: (1) expanding notions of biodata and bodies, (2) attending to a greater diversity of human bodies and experiences with biodata, and (3) biodata collaborations between human and non-human bodies. We illustrate these themes with selected design examples. From this synthesis, we develop three interconnected fabulations reimagining alternative engagements with biodata: Weaving Alongside, Diffracting Selves, and Collective Affect. Our discussion unpacks conceptual work of the fabulations, offering invitations for design research to explore alternative ways of living and knowing together with biodata.

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IBPoet

Cited by 2 publications

References 2 publications

Consumer Grade Brain Sensing for Emotion Recognition

Consumer Grade Brain Sensing for Emotion Recognition

Fabulating Biodata Futures for Living and Knowing Together

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