Intent Switching and Co-Adaption of Man and Machine in a Closed-Loop Brain Computer Interface
Abstract
Brain computer interfaces (BCI) have been an active area of research for well over a decade. Originally envisioned as communication devices for the neurologically locked-in, several BCI systems have been developed that aim to assist the otherwise neurologically healthy in human-machine interaction. For BCIs of this type, it is important to develop an understanding of man-machine co-adaption. For example, when the C3Vision BCI for image search (Sajda et al, 2010; Pohlmeyer et al. 2011) is operating in a closed loop, neural signatures are being used to train a transductive graph-based model based on neural interest scores. The feedback from the machine can change the prevalence and even characteristics of the images shown to the user that can, in turn, affect the users perceptual/cognitive state. For instance, we have observed adaptation of not only the machine, via new neural interest scores, but adaptation of the neural signatures evoked by the images, including amplitude and timing changes of said neural signatures. Most BCI systems largely ignore co-adaption and instead assume a more-or-less stationary process in terms of the stochastic nature of the man-machine interaction. In this project our major goal is to investigate fundamental scientific questions in co-adaption of man and machine in a closed-loop brain computer interface. The following outlines the main objectives of the research project. Objective 1. Investigate whether switches in user intent can be measured using EEG and exploited in a closed-loop BCI. Preliminary data by our group has shown how a closed-loop BCI can be used to integrate neurophysiological signatures of object recognition and attentional orienting with computer vision to enable a man-machine system for image search. However, in this preliminary work, the user was not adaptive i.e., they did not change their object of interest or intention during the search task.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 31, 2019
- Accession Number
- AD1110958
Entities
People
- Paul Sajda
Organizations
- Columbia University