We report the details of hyperparameters in our experiments.

Neural Information Processing Systems http://nips.cc/

Stimulus-drivenbrain-computer interfaces (BCIs), such astheP300 speller,rely onusing asequence ofsensory stimuli toelicit specific neural responses ascontrol signals, while a user attends to relevant target stimuli that occur within the sequence. In current BCIs, the stimulus presentation schedule is typically generated in a pseudo-random fashion. Given the non-stationarity of brain electrical signals, a better strategy could be to adapt the stimulus presentation schedule in real-time by selecting the optimal stimuli that will maximize the signal-to-noise ratios of the elicited neural responses and provide the most information about the user's intent based on the uncertainties of the data being measured. However, the high-dimensional stimulus space limits the development of algorithms with tractable solutions for optimized stimulus selection to allow for real-time decision-making within the stringent time requirements of BCI processing.

Is there anything afuture user could do to mitigate theseundesirableharms? Although ConfLab's long-term vision is towards developing technology to assist individuals in navigating social interactions, the data could also affect a community in unintended ways: for instance, cause worsened social satisfaction, alackofagency,stereotype newcomers andveterans, or benefit only those members of the community who make use of resulting applications at the expense of the rest. More nefarious uses involve exploiting the data for developing methods that harmfully surveilorprofile people.

Ourbenchmarks showcase some of the open research tasks related to in-the-wild privacy-preserving social data analysis: keypoints detection from overhead camera views, skeleton-based no-audiospeakerdetection,andF-formationdetection.