The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

By Benjamin Blankertz, Michael W. Tangermann, Carmen Vidaurre, Siamac Fazli, Claudia Sannelli, Stefan Haufe, Cecilia L Maeder, L. Ramsey, Irene Sturm, Gabriel Curio and 1 more. Frontiers in Neuroscience, 2010

396 citations recorded by the source corpus.

Brain–computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/d2f2a5bfbdc168784468276539e2b0d5d313d517

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Review of the BCI Competition IV (2012, background): “Data from those recent developments have not yet been included in the BCI competitions, but may pose interesting and novel challenges for future competitions.”
Event-related (De)synchronization (ERD/ERS) during motor imagery tasks: Implications for brain–computer interfaces (2011, background): “As a non-muscular communication and control system (Wolpaw et al., 2002; Morash et al., 2008; Nam et al., 2009; Blankertz et al., 2010), brainecomputer interface (BCI) has shown emerging possibilities for people who have lost all voluntary muscle control, but are cognitively intact, by allowing…”
Brain Computer Interfaces, a Review (2012, background): “Broader applicability of BCIs requires greater ease of use, which in turn means reducing time spent on preparation, training and calibration [7].”
Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces (2015, background): “Among the different reasons currently preventing BCI from being widely used outside laboratories for practical applications, one can cite their lack of robustness and reliability, their cum-bersome setup and their long calibration time [1], [8], [9].”
Robust artifactual independent component classification for BCI practitioners (2014, background): “Such studies include users (inter-)acting in space [7–9] like in collaborative and social paradigms (for a review see [10]), the interaction between users and machines [11] and the nonmedical use of BCI methods [12, 13].”