The Inclusive EEG Handbook

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Inclusive EEG and Others

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Addressing racial and phenotypic bias in human neuroscience methods - Nature NeuroscienceHuman neuroscience methods (for example, electroencephalography, functional near-infrared spectroscopy and electrodermal response) are biased to exclude data from dark skin and coarse hair—traits common in Black people—and possibly people with racial trauma. We outline strategies to prevent a biased ‘unusable data crisis’.

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Systemic Racism in EEG Research: Considerations and Potential Solutions - Affective ScienceThe current theoretical paper discusses the unintended systemic racism and racial biases that impact neuroscience, specifically in research utilizing electroencephalography (EEG). As a popular technique in affective science research, EEG requires adherence between the electrode and scalp to measure brain activity. To obtain high-quality data, various factors such as hair length, hair type, body movement, and/or extraneous noise from the environment are taken into consideration. As EEG researchers attempt to gather good-quality data, the recruitment and retention of Black American participants is challenging due to hairstyles commonly worn by Black American participants (e.g., cornrows, braids) and hair type. Taken together, the systemic lack of data from Black American participants renders research findings less generalizable and causes disparities in theoretical knowledge applicable to this population. To address this disparity, innovative solutions invented by bioengineers are discussed.

Racial disparities in EEG research and their implications for our understanding of the maternal brain - Cognitive, Affective, & Behavioral NeuroscienceRacial disparities in maternal health are alarming and persistent. Use of electroencephalography (EEG) and event-related potentials (ERPs) to understand the maternal brain can improve our knowledge of maternal health by providing insight into mechanisms underlying maternal well-being, including implications for child development. However, systematic racial bias exists in EEG methodology—particularly for Black individuals—and in psychological and health research broadly. This paper discusses these biases in the context of EEG/ERP research on the maternal brain. First, we assess the racial/ethnic diversity of existing ERP studies of maternal neural responding to infant/child emotional expressions, using papers from a recent meta-analysis, finding that the majority of mothers represented in this research are of White/European ancestry and that the racially and ethnically diverse samples that are present are limited in terms of geography. Therefore, our current knowledge base in this area may be biased and not generalizable across racially diverse mothers. We outline factors underlying this problem, beginning with the racial bias in EEG equipment that systematically excludes individuals of African descent, and also considering factors specific to research with mothers. Finally, we highlight recent innovations to EEG hardware to better accommodate diverse hairstyles and textures, and other important steps to increase racial and ethnic representativeness in EEG/ERP research with mothers. We urge EEG/ERP researchers who study the maternal brain—including our own research group—to take action to increase racial diversity so that this research area can confidently inform understanding of maternal health and contribute to minimizing maternal health disparities.

Demographic reporting and phenotypic exclusion in fNIRSFunctional near-infrared spectroscopy (fNIRS) promises to be a leading non-invasive neuroimaging method due to its portability and low cost. However, concerns are rising over its inclusivity of all skin tones and hair types (Parker and Ricard, 2022, Webb et al., 2022). Functional NIRS relies on direct contact of light-emitting optodes to the scalp, which can be blocked more by longer, darker, and especially curlier hair. Additionally, NIR light can be attenuated by melanin, which is accounted for in neither fNIRS hardware nor analysis methods. Recent work has shown that overlooking these considerations in other modalities like EEG leads to the disproportionate exclusion of individuals with these phenotypes—especially Black people—in both clinical and research literature (Choy, 2020; Bradford et al., 2022; Louis et al., 2023). In this article, we sought to determine if (Jöbsis, 1977) biomedical optics developers and researchers report fNIRS performance variability between skin tones and hair textures, (2a) fNIRS neuroscience practitioners report phenotypic and demographic details in their articles, and thus, (2b) is a similar pattern of participant exclusion found in EEG also present in the fNIRS literature. We present a literature review of top Biomedical Optics and Human Neuroscience journals, showing that demographic and phenotypic reporting is unpopular in both fNIRS development and neuroscience applications. We conclude with a list of recommendations to the fNIRS commu...

Structural racism in neuroimaging: perspectives and solutionsMacroaggressions and microaggressions in academic settings are not the only contributors to systemic racism in science. The technology we use to do our research is inadvertently racist and biased. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), for example, require adherence between the optode or electrode and scalp to measure brain activity. Contact with the scalp to optimise high-quality signals is disproportionately harder to achieve in Black participants, who tend to have coarse hair textures and protective hairstyles.

Toward an Anti-Racist Approach to Biomedical and Neuroscience ResearchRacism is a threat to public health. Race is a sociopolitical construct that has been used for generations to create disparities in educational access, housing conditions, exposure to environmental contaminants, and access to health care. Collectively, these disparities have a negative impact on the health of non-white Americans. The National Institutes of Health (NIH) funds biomedical research, including basic neuroscience research, aimed at understanding the mechanisms and consequences of health and disease in Americans. NIH has recently acknowledged its own structural racism, the disadvantage this perpetuates in the biomedical research enterprise, and has announced its commitment to eliminating these disparities. Here, we discuss different rates of disease in U.S. citizens from different racial backgrounds. We next describe ways in which the biomedical research enterprise (1) has contributed to health disparities and (2) can contribute to the solving this problem. Based on our own scientific expertise, we use neuroscience in general and mental health/addiction disorders more specifically as examples of a broader issue. The NIH, including its neuroscience-focused Institutes, and NIH-funded scientists, including neuroscientists, should prioritize research topics that reflect the health conditions that affect all Americans, not just white Americans.

Hair me out: Highlighting systematic exclusion in psychophysiological methods and recommendations to increase inclusionWithin the neuroscience field, there have been efforts to address the ways systemic racism has permeated and negatively affected our research practice and body of knowledge (Abiodun, 2019; Choy et al., 2021; Carter et al., 2022; Webb et al., 2022). Neuroscience methods that require access to the hair and scalp systematically exclude groups of people, particularly Black communities, over and beyond the embedded exclusionary factors in the broader human research landscape (Roberts et al., 2020; Fulvio et al., 2021; Taylor et al., 2021; Goldfarb and Brown, 2022). Indeed, recent papers have highlighted the shortcomings of current neuroscience methods (Choy et al., 2021; Parker and Ricard, 2022; Webb et al., 2022). Recently, Bradford et al. (2022) discussed underrepresentation in psychophysiological research samples and offered insightful recommendations for researchers to improve inclusion. We amplify and extend these valuable efforts, with a particular focus on methods that require access to participants’ hair, such as electroencephalography (EEG) and hair sample collection. We briefly review factors that have led to the systematic exclusion of underrepresented groups in psychophysiological research and synthesize practical recommendations for researchers to increase inclusion moving forward. To understand systematic exclusion in neuroscience methods, it is essential to name the legacy of anti-Black racism and its impact on research practices. Many early empirical pursuits of...

<em>Psychophysiology</em> | SPR Journal | Wiley Online LibraryDifferences in hair volume that frequently vary by race may introduce systematic bias in ERP amplitudes. Our results indicate that accounting for these differences by quantifying gel volume used duri...

Whose Signals Are Being Amplified? Toward a More Equitable Clinical Psychophysiology - Daniel E. Bradford, Angelica DeFalco, Emily R. Perkins, Iván Carbajal, Jasmine Kwasa, Fallon R. Goodman, Felicia Jackson, Lietsel N. S. Richardson, Nina Woodley, Lindsay Neuberger, Jennifer A. Sandoval, Helen J. Huang, Keanan J. Joyner, 2022Research using psychophysiological methods holds great promise for refining clinical assessment, identifying risk factors, and informing treatment. Unfortunatel...

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