Yu-Wei Yang and Youngjun Cho
Department of Computer Science, University College London, London, UK
EEG neurofeedback has shown positive effects on motor rehabilitation in daily settings. However, high heterogeneity between existing works has been identified given a lack of systematic validation of various intervention training strategies and terminologies adopted for different users, EEG devices and feedback methods. Here, we contribute a systematic review and meta-analysis, providing a constructive overview of the EEG neurofeedback interfaces and assessing the effectiveness of intervention strategies. We initially identify 5307 articles and focus on 62 key articles for systematic review and 35 eligible studies for meta-analysis on motor-related outcomes from which we report on significant improvements in motor performance. With our findings on the effectiveness of individual factors, we develop a taxonomy to inform the future research agenda of cutting-edge EEG neurofeedback systems and intervention strategies. We also provide a guideline for practitioners and end-users to choose optimal intervention approaches and insights into challenges and opportunities for future interface design (see our previous related work for insights [1-4]).
 Moge, C., Wang, K. and Cho, Y., 2022. Shared user interfaces of physiological data: Systematic review of social biofeedback systems and contexts in HCI. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-16).
 Wang, K., Julier, S. and Cho, Y., 2022. Attention-Based Applications in Extended Reality to Support Autism: A Systematic Review. IEEE Access.
 Cho, Y., 2021. Rethinking eye-blink: Assessing task difficulty through physiological representation of spontaneous blinking. In Proceedings of the 2021 CHI conference on human factors in computing systems (pp. 1-12).
 Cho, Y., Kim, S. and Joung, M., 2017. Proximity sensor and control method thereof. U.S. Patent 9,703,368.