Context-Aware Cybernetics: Building BCIs that turn Intentions into Appropriate Actions

This half-day tutorial at IEEE CYBCONF 2013 is organised by the following speakers, in association with the IEEE SMC Technical Committee on Shared Control:


Dr. Tom Carlson

Dr. Ricardo Chavarriaga

Prof. José del R. Millán

 

Controlling neuroprostheses and other assistive robotic devices directly from brain signals has gained increasing attention over the past few years. Indeed such devices can potentially be employed to substitute motor capabilities (e.g. brain-controlled prosthetics for amputees or patients with spinal cord injuries); to help in the restoration of such functions (e.g. as a tool for stroke rehabilitation) as well as non-clinical applications like telepresence and entertainment. This half-day tutorial gives an introduction to the field of brain-computer interfaces (BCI) and presents several context-aware design principles to successfully employ them in robot control.

Overview

A non-invasive brain-computer interface (BCI) is a system that translates user's intent, coded by spatiotemporal neural activity (usually recorded through EEG), into a control signal without using activity of any muscles or peripheral nerves. Current EEG-based BCIs are limited by a low information transfer rate and the low signal to noise ratio of the brain-generated signals. Nevertheless, it has been shown that online asynchronous analysis of spontaneous EEG signals, in combination with statistical machine learning techniques and smart interaction design, is sufficient for allowing humans to do so. Based on the principles of mutual learning, shared control and adaptation, users convey high level mental commands that the devices interpret given the context and execute in the most appropriate way to achieve the goal. Thus allowing the efficient control of mobile robots (e.g. automated wheelchairs), or neuroprostheses.

Moreover, brain-robot interaction can be enriched by detection of user's cognitive states. These states may provide additional contextual information about interaction errors as perceived by the user, as well as fatigue or perception of relevant feedback information. In particular, EEG correlates of error awareness can be used for correcting BCI misclassifications of the user's intent, as well as be used as a teaching signal to improve the performance of an adaptive device through human supervision. Experiments using simulated and real interaction with mobile robots show the feasibility of detecting such signals in real time; thus providing an alternative, natural way of interaction to current BCI systems, while reducing the user demands in terms of cognitive attention and effort.

This tutorial will introduce the basic principles for context-aware cybernetics using brain-computer interfaces and is composed of the following components:

  • Introduction to brain-computer interfaces
  • Feature selection and classification techniques
  • Shared control and adaptation
  • Cognitive signals for human-robot interaction

Intended audience

The intended audience is both novices and experts in cybernetics. This tutorial is appropriate for students and researchers in the engineering and computer science disciplines. No prior experience in robotics, control systems, signal processing or neuroscience is required.

Resources

Here are a few representative articles (full text pre-prints available):

Please follow this link to the workshop website for updated information

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Original

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Additional

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