Electroencephalography (EEG) is a brain imaging technology that provides temporal information about neural responses of “sensory, cognitive, and motor events” by detecting event-related potentials (ERPs) (Choy et al., 2022). ERPs are voltages produced by the firing of neurons in the brain in response to stimuli (Sur and Sinha, 2009). EEG requires participants to wear an electrode cap, with a saline solution facilitating the flow of electrical signals from scalp to electrode. While EEG easily establishes scalp contact for those with short, straight hair, it fails to produce a high signal-to-noise (S/N) ratio when used with curly or braided hair types; a high S/N ratio indicates that the target signal of the ERPs fired in response to a particular induced stimulus is much greater than the voltages produced by non-brain body parts (Perez et al., 2019). Consequently, Black individuals have frequently been excluded from studies employing EEG, with researchers blaming methodological barriers. In fact, Choy et al. (2022) found that only 5 out of 81 EEG research papers used Black participants and none of the papers explicitly stated if they used that data after further filtering. This phenotypic bias is inherently racist and reinforces stigmas about Black hair while also reinforcing the exclusion of Black people from clinical trials. Given that the diagnosis of diseases such as COVID-19 and the evaluation of maternal health are established based on these studies, EEG’s racial bias presents a real threat to Black people (Webb et al., 2022). The scientific community should embrace recent innovations in EEG that are adapted for non-white hair types and take measures to increase Black representation in neuroscience studies in order to address EEG’s shortcomings.

EEG’s limitations negatively affect the reliability of data and racial equity. The very inconvenience of data collection using traditional EEG discourages Black participation in studies. Etienne et al. (2022) note that researchers have applied more conductive gel when trying to accommodate EEG for Black hair, resulting in a messier experience. Furthermore, researchers may require Black participants to untie their hair, an annoyance due entirely to EEG’s design flaws (Louis et al., 2022). Manns-James and Neal-Barnett even found that roughly half of the Black women who chose not to participate cited issues with altering their current hairstyle for research  (2019).

The exclusion of Black participants also impacts health outcomes for Black people. Functional near-infrared spectroscopy (fNIRS) employs EEG to detect changes in blood oxygenation levels during cognitive tasks (Sakai et al., 2022). The method detects how hemoglobin absorbs infrared light by shining a light on the scalp to make skin, bone, and brain tissue more transparent. However, fNIRS faces the same challenge of high noise levels, since the higher density of chromophores, like melanin in Black individuals, may reduce the visibility of brain tissue. Besides encouraging scientists to exclude Black data, fNIRS’s noise also reduces the reliability or accuracy of fitness watches and the monitoring of blood oxygenation levels, which is key to COVID-19 treatment (Webb et al., 2022).

Racial bias in EEG also reinforces stereotypes about Black people based on white-oriented methodologies. Penner et al. (2023) explain that monitoring ERPs involved in a mother’s response to their infant’s facial expressions can provide insight into the neural mechanisms tying mental illness to parental behavior. Yet, the scarcity of research on Black mothers due to EEG’s pitfalls perpetuates unsubstantiated beliefs that Black mothers are naturally harsher, worse parents. In fact, socioeconomic conditions contribute significantly to psychological distress and mental illnesses that can influence parenting style (Parker, 2021). Research connecting neural responses, parenting styles, and socioeconomic and environmental factors could dispel such myths by including Black participants. Similarly, fear conditioning studies that use EEG electrodes to detect emotional arousal in response to stimuli rely on sweat levels to influence conductivity. These studies reveal how the brain directs human responses to perceived threats, giving insight into the mechanisms of anxiety and the neurological effects of environmental exposures. Black data is frequently excluded because Black participants exhibit low conductivity similar to unusable “non-learner” data of individuals who simply do not respond to a given stimulus (Webb et al., 2022). This exclusion insensitively overlooks the fact that lower conductivity in Black participants may be due to structural racism embedded in methodological practices.

Given that EEG’s biases threaten Black health and strengthen false stereotypes, what can be done to mitigate these harms? Technological modifications to EEG can be employed to enable high S/N ratios for Black hair. Etienne et al. (2020) created Sevo electrodes with clips to hold hair strands apart and tied participants’ hair in a cornrow pattern that matches the electrode clips (Choy et al., 2022). Casson (2019) similarly used fingered electrodes to separate hair as well as temporary EEG sensor tattoos to improve detection. Gold-cup EEG electrodes and lengthened fNIRS probes can further reduce noise (Parker and Ricard, 2022). These improvements demonstrate the plausibility of adapting EEG to work on individuals of all hair types.

Changes to the conditions of participation and increased inclusion of Black individuals in research can complement technical innovations by increasing Black participation in studies. Researchers should educate themselves on different types of hair to continue to improve EEG and other electrode-based technologies. Greater sensitivity to cultural or religious views of hair can encourage Black and other non-white people to participate in these studies, thereby reducing the data gap and enabling treatment and diagnosis of diseases like COVID-19 to be more accurate in people of color. These goals can be achieved through greater collaboration and consultation with Black hair stylists, engineers, and researchers. For instance, the University of Central Florida’s BRaIN Lab has drafted a set of hair and inclusivity guidelines to follow when conducting EEG research (Richardson, 2021). Training the staff who work with participants to better adhere to such guidelines can further foster a positive environment for participants. Increased recruitment of Black individuals to neuroscience and engineering fields can more directly shift research and society toward greater awareness of methodological and technological biases.

Lastly, alterations to the process of reviewing studies at the approval and submission stages can improve the transparency of the data used and systematically reject non-inclusive practices. Institutional Review Boards (IRBs) can hold training sessions to raise internal awareness of the problems associated with excluding Black hairstyles from the final data set (Webb et al., 2022). Greater diversity within the Boards themselves can facilitate this shift in values. Journals can similarly publish age and gender metadata to bring to light the limitations of exclusionary studies. By regulating which studies can be pursued and making demographic data clear and open to the public, IRBs and journals can help prioritize diversity in data and thus make EEG research, and thus neuroscience, more reliable for everyone.

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About the Author
Evan Hsiang is a rising sophomore at Harvard College, studying Chemical and Physical Biology.

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