Closed-loop EEG-based neurofeedback and brain-computer interface interventions for mental health: a review

Abstract

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been widely explored for detecting and monitoring mental health-related states, with many existing studies focusing on identification and classification. Such approaches are primarily observational and provide limited support for intervention. Closed-loop EEG-based BCIs address this limitation by incorporating real-time feedback to observe neural activity. A key paradigm within this context is neurofeedback, in which users learn to modulate their own brain activity, with the aim of supporting improvements in mental states and in psychological functioning. In this work, we conducted a review of closed-loop EEG-based BCI interventions for mental health published since 2021, guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) reporting principles. A structured search was conducted across three databases (Scopus, Web of Science, and PubMed), yielding 1,101 records, of which 25 studies met the inclusion criteria. Advancements and observations were summarized across four categories: application, paradigm design and feedback mechanisms, signal processing and machine learning methods, and performance metrics and outcomes. In addition, this review discusses considerations related to signal processing and machine learning (ML), user interface design, and regulatory mechanisms across BCI interventions. Finally, potential directions for future research are outlined, including multimodal BCIs, domain adaptation, the integration of generative AI for BCI-based therapeutic interventions, and home-based deployment. Overall, current findings support the technical feasibility and emerging therapeutic potential of closed-loop EEG-based neurofeedback and BCI interventions in mental health applications, but the evidence base remains preliminary, as many studies were small pilot or feasibility studies.