Brain-Computer Interfaces, Open-Source, and Democratizing the Future of Augmented Consciousness

Abstract

Accessibility, adaptability, and transparency of Brain-Computer Interface (BCI) tools and the data they collect will likely impact how we collectively navigate a new digital age. This discussion reviews some of the diverse and transdisciplinary applications of BCI technology and draws speculative inferences about the ways in which BCI tools, combined with machine learning (ML) algorithms may shape the future. BCIs come with substantial ethical and risk considerations, and it is argued that open source principles may help us navigate complex dilemmas by encouraging experimentation and making developments public as we build safeguards into this new paradigm. Bringing open-source principles of adaptability and transparency to BCI tools can help democratize the technology, permitting more voices to contribute to the conversation of what a BCI-driven future should look like. Open-source BCI tools and access to raw data, in contrast to black-box algorithms and limited access to summary data, are critical facets enabling artists, DIYers, researchers and other domain experts to participate in the conversation about how to study and augment human consciousness. Looking forward to a future in which augmented and virtual reality become integral parts of daily life, BCIs will likely play an increasingly important role in creating closed-loop feedback for generative content. Brain-computer interfaces are uniquely situated to provide artificial intelligence (AI) algorithms the necessary data for determining the decoding and timing of content delivery. The extent to which these algorithms are open-source may be critical to examine them for integrity, implicit bias, and conflicts of interest.

Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces

“…Currently, the usability and user satisfaction of existing BCI systems are low, and their medical efficacy (such as the efficacy of motor imagery BCIs in active rehabilitation) requires rigorous evidence-based research and objective evaluation ( Baniqued et al, 2021 ; Kim et al, 2021 ; Mridha et al, 2021 ). The irreplaceability of BCIs, characterized by the difficulty of existing technologies to substitute or surpass them, along with their accessibility and user acceptability, still requires significant enhancements ( Morone et al, 2015 ; Bernal G. et al, 2021 ; Williams et al, 2022 ).…”

Twice Stepped in Still Waters: Sonification and Interdisciplinarity as Artistic Research

“…For the purpose of Twice Stepped In Still Waters, Muse 2 was chosen over other consumer EEG devices [66] due to connectivity and flexibility of data extraction. The Muse 2 was selected over open-source hardware [67] for reasons of cost and build expertise, however future developments indicate utilisation of this approach. The procedure used in the present work establishes a direct connection between the Muse 2 and my computer via Bluetooth.…”

Active Brain-Computer Interfacing for Healthy Users

“…Art —Another BCI application for healthy users is the use of BCI by enthusiast artists in performances and creating pieces of art, i.e., “brain art” (Nijholt, 2019 ) or “BCI art” (Prpa and Pasquier, 2019 ). These projects are very diverse (Brain Art, 2019 ; Bernal et al, 2021 ), but, unfortunately, rarely documented in the scientific literature (Prpa and Pasquier, 2019 ; Friedman, 2020 ). Of 61 BCI art projects surveyed by Prpa and Pasquier ( 2019 ), mostly described in non-science sources such as YouTube videos, 18 used active or reactive control (Table 3.4 in Prpa and Pasquier, 2019 ).…”

“…2. Experience —In HCI, not only productivity and efficiency are valuable, but also, increasingly, various aspects of interaction experience, such as “affect, comfort, family, community, or playfulness,” where BCI technologies have certain advantages (Bernal et al, 2021 ; Nijholt et al, 2022 ). In some cases, BCI-based interaction brings highly paradoxical experience: for example, the long-known feature of control based on alpha rhythm is “the more you try, the less likely is to succeed” (Lucier and Simon, 1980 , cited by Prpa and Pasquier, 2019 , p. 102).…”