Interdisciplinary views of fNIRS: Current advancements, equity challenges, and an agenda for future needs of a diverse fNIRS research community

Emily J Doherty; Cara A Spencer; Jeremy Burnison; Marta Čeko; Jenna Chin; Lucca Eloy; Kerstin Haring; Pilyoung Kim; Daniel Pittman; Shannon Powers; Samuel L Pugh; Demetris Roumis; Jaclyn A Stephens; Tom Yeh; Leanne Hirshfield

doi:10.3389/fnint.2023.1059679

Interdisciplinary views of fNIRS: Current advancements, equity challenges, and an agenda for future needs of a diverse fNIRS research community

Front Integr Neurosci. 2023 Feb 27:17:1059679. doi: 10.3389/fnint.2023.1059679. eCollection 2023.

Authors

Emily J Doherty^{1

2}, Cara A Spencer^{1

2}, Jeremy Burnison³, Marta Čeko², Jenna Chin⁴, Lucca Eloy^{1

2}, Kerstin Haring⁵, Pilyoung Kim⁴, Daniel Pittman⁵, Shannon Powers⁴, Samuel L Pugh^{1

2}, Demetris Roumis³, Jaclyn A Stephens⁶, Tom Yeh¹, Leanne Hirshfield^{1

2}

Affiliations

¹ Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States.
² Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, United States.
³ NIRx Medizintechnik GmbH, Berlin, Germany.
⁴ College of Arts, Humanities, and Social Sciences, Psychology, University of Denver, Denver, CO, United States.
⁵ Department of Computer Science, University of Denver, Denver, CO, United States.
⁶ Department of Occupational Therapy, Colorado State University, Fort Collins, CO, United States.

Abstract

Functional Near-Infrared Spectroscopy (fNIRS) is an innovative and promising neuroimaging modality for studying brain activity in real-world environments. While fNIRS has seen rapid advancements in hardware, software, and research applications since its emergence nearly 30 years ago, limitations still exist regarding all three areas, where existing practices contribute to greater bias within the neuroscience research community. We spotlight fNIRS through the lens of different end-application users, including the unique perspective of a fNIRS manufacturer, and report the challenges of using this technology across several research disciplines and populations. Through the review of different research domains where fNIRS is utilized, we identify and address the presence of bias, specifically due to the restraints of current fNIRS technology, limited diversity among sample populations, and the societal prejudice that infiltrates today's research. Finally, we provide resources for minimizing bias in neuroscience research and an application agenda for the future use of fNIRS that is equitable, diverse, and inclusive.

Keywords: bias; collaboration; diversity; fNIRS; human-computer interaction.

Publication types

Review

Grants and funding

This research was supported by the NSF National AI Institute for Student-AI Teaming (iSAT) under grant DRL 2019805. The opinions expressed are those of the authors and do not represent views of the NSF.