This post was written by J Wolny and Alisha Cayce.
The lyrics “Don’t touch my hair, when it’s the feelings I wear,” sung by the widely acclaimed musician Solange, express how hair can be an extension of one’s identity for many in the Black community. However important hair is to many Black individuals, cultural sensitivity in working with Black clients and research participants is often absent from basic neuroscience research training. This blog aims to discuss how hair biases and hair discrimination impact Black individuals’ representation in neuroscience research, with a particular focus on one neuroscience method known as Electroencephalography (or EEG, for short).
EEG collection allows researchers to capture electrical activity as it dissipates across the cerebral cortex (i.e., the outer surface) of the brain. The fluctuations in electrical activity captured by EEG are thought to be a direct measure of the activity that occurs as neurons in the brain “fire” in order to communicate with one another. EEGs are valuable in critical clinical situations. For example, EEG has been used to detect and diagnose abnormal brain activity indicating various health concerns, such as epilepsy and strokes. Researchers have also applied EEG techniques to investigate the neurological abnormalities associated with mental disorders, such as schizophrenia, anxiety, and depression (to name a few). For example, researchers have identified EEG biomarkers that may predict who develops disorders such as schizophrenia versus who does not among individuals at heightened risk for the illness (Kerins et al., 2022).
When coming into a lab or clinic to have EEG collected, here is what you can expect: a technician will place small metal discs called ‘electrodes’ on your scalp. These electrodes connect via wires to the EEG device itself, amplifying the signals received from the scalp and converting them into brain waves which are to be inspected later. Some might presume that this method would be easily transferable to people of all hair types. However, this is not the case. To this day, many people with coarse or curly hair are excluded from EEG research. As a result, Black individuals are unlikely to participate in research that employs EEG technology. (Webb et al., 2022).
Experts who have examined the intersection of racial bias and EEG methodology (Choy et al., 2021; Webb et al., 2022) point to several reasons why Black individuals are underrepresented in this research. Among these reasons is the assumption that Black hair leads to poorer quality data, poor understanding of Black hair, and a lack of cultural competence in working with individuals of color. Researchers Webb and colleagues (2022) described this phenomenon as ‘hair-type bias’, in which researchers inadvertently apply standard screening criteria which excludes people with hair types other than straight or wavy hair. Examples of such hair type biases in EEG research include the following:
1. Preemptive Exclusion
Insofar as only 4% of PhD holders in Neuroscience or Psychology are Black, as the Society for Neuroscience and the American Psychological Association have shown, research labs are particularly susceptible to biases that may result in exclusionary practices. For example, many EEG researchers preemptively exclude Black participants based on hair texture and/or style, assuming that phenotypically Black hair will lead to poorer contact between the electrode and the scalp. This scalp-to-electrode connection is referred to as ‘impedance levels’ in the field, which reflect the ratio of signal (i.e., electrical activity captured from the brain) to noise (i.e., ‘bad signals, such as electrical activity captured from external sources). Yet, there is little empirical (scientific) support for the assertion that phenotypically coarse or curly hair leads to higher impedance levels, as little research has directly examined how race-based differences in hair type may impact EEG signal quality.
2. Cultural Considerations
Lack of racial representation within EEG research often leads to poor cultural competency in labs that administer EEG to Black participants. As previously stated, Black participants with curly and coarse hair or protective hairstyles are often deemed incompatible with EEG equipment. Consequently, attempts to adjust EEG administration to be more compatible with Black hair or the development of efficient protocols to work successfully with participants’ hair styling routines have been virtually non-existent. Developing screening questionnaires to determine how Black participants usually wear their hair and how often their hairstyle changes may help researchers better grasp when it is appropriate to schedule EEG sessions. Additional procedures may include offering to schedule participants around their usual hair wash day and potential reimbursement for hairstyling costs following each session.
3. Participant Comfort & Wellbeing
In research settings, EEG administrators often lack an understanding of how hair may be rooted in ancestral symbolism and tied to current beliefs of one’s identity. Such lack of awareness may lead Black participants to have poorer experiences, which may prevent them from returning to research settings for future EEG research. Such incidents may result from microaggressions such as negative comments regarding the difficulty of applying EEG electrodes to a given hair type, in addition to the rough handling of participants’ hair during EEG set-up. Ways to mitigate such experiences include affirmations that the set-up period is going well and frequent check-ins regarding participant comfort. Furthermore, consent to touch participants’ hair should be obtained prior to starting the EEG administration process and throughout data collection, rather than implicitly assumed. Lastly, Black hair products should be made available for participants so that individuals’ can appropriately wash and style their hair following each appointment.
The issues described above make it clear that the field of neuroscience must pay greater attention to inclusivity, particularly when collecting data from individuals with phenotypically Black hair. Striving towards racial equity within EEG practices will require an increased representation of Black EEG technicians within research and healthcare settings, in addition to holistic approaches which target biases, such as those listed in this post. Such methods might further include the use of culturally informed training materials and increased awareness of how biases lead to preemptive practices of exclusion.
Fortunately, researchers at the Grover Lab, housed at Carnegie Mellon University, are leading such progress. Research associate Arnelle Etienne is tackling the diversification of EEG research on two fronts. First, she is working to validate novel EEG electrodes which may be freely placed onto the scalp (aka, ones that are clipped into position, rather than constrained standard placement systems imposed by EEG caps). Second, she aims to refine braiding techniques to promote a higher quality of data collected and contribute to participant comfort when administering EEG. The ingenuity of such work, combined with a commitment to human-centered research, provides promise for a more equitable future in the use of neuroscience techniques. These steps are necessary to ensure the generalizability of neuroscience research to all folks, independent of their physical features.
Choy, T., Baker, E., & Stavropoulos, K. (2021). Systemic Racism in EEG Research: Considerations and Potential Solutions. Affective science, 3(1), 14–20. https://doi.org/10.1007/s42761-021-00050-0
Kerins, S., Nottage, J., Salazar de Pablo, G., Kempton, M. J., Tognin, S., Niemann, D. H., de Haan, L., van Amelsvoort, T., Kwon, J. S., Nelson, B., Mizrahi, R., McGuire, P., Fusar-Poli, P., & PSYSCAN Consortium (2022). Identifying Electroencephalography Biomarkers in Individuals at Clinical High Risk for Psychosis in an International Multi-Site Study. Frontiers in psychiatry, 13, 828376. https://doi.org/10.3389/fpsyt.2022.828376
Webb, E., Etter, J., Kwasa, J. (2022). Addressing racial and phenotypic bias in human neuroscience methods. Nature Neuroscience. https://doi.org/10.1038/s41593-022-01046-0