Brain–computer interfacing (BCI) aims at directly capturing brain activity in order to enable a user to drive an application such as a wheelchair without using peripheral neural or motor systems. Low signal to noise ratio’s, low processing speed, and huge intra- and inter-subject variability currently call for the addition of intelligence to the applications, in order to compensate for errors in the production and/or the decoding of brain signals. However, the combination of minds and machines through BCI’s and intelligent devices (...) (IDs) can affect a user’s sense of agency. Particularly confusing cases can arise when the behavioral control switches implicitly from user to ID. I will suggest that in such situations users may be insecure about the extent to which the resulting behavior, whether successful or unsuccessful, is genuinely their own. Hence, while performing an action, a user of a BCI–ID may be uncertain about being the agent of the act. Several cases will be examined and some implications for (legal) responsibility (e.g. establishing the presence of a ‘guilty mind’) are discussed. (shrink)

Brain-Computer Interfaces interpret neural activity, applying it to the control of external devices. As BCIs approach market viability, ethical implications come under consideration. This paper identifies potential privacy disruptions. BCI literature is reviewed in order to identify a BCI typology likely to support a privacy analysis. The typology describes the active, reactive, passive and hybrid types of BCI and, where possible, includes examples that are further classified as existing, prospective or speculative. A review of privacy theory supports an analysis that (...) juxtaposes privacy theory and BCI technologies. The analysis finds that while all four types of BCI have potential for disrupting privacy, disruptions are more likely to arise from the use of reactive, passive and hybrid BCIs. Limitations and directions for future research close the paper. (shrink)