BCI: Towards Second Generation Brain-computer Interfaces


Full title

Towards Second Generation Brain-computer Interfaces

Duration of project

12/1/2007 – 11/30/2010

Funding source

Swiss National Science Foundation

Funding number



A brain-computer interface (BCI) is a system that allows a user to communicate with the environment only through cerebral activity, without using muscular output channels. To establish a direct link between the brain and a computer, the cerebral activity is measured and then analyzed with the help of signal processing and machine learning algorithms. Once a certain mental activity has been detected by the computer, a response can be displayed on a screen or a command can be sent to a device – for example a robot or a remote control. The main application area for BCI is assistive technology for handicapped persons.

During the last years, it has been convincingly shown that communication via a BCI is feasible for able-bodied as well as handicapped subjects. However, so far no practical, commercially available systems suited for use in everyday life by handicapped persons are available.

The goal of this project is to develop a second-generation BCI which overcomes some of the problems inherent in current system. To achieve this goal it is planned to develop new machine learning algorithms that allow users to immediately use a BCI without going through a training phase. In contrast to many other BCI systems the developed system will be asynchronous. This means the system will continuously analyze cerebral activity but will react only when it detects that the user is actually trying to send a command. The motivation for performing the proposed research is to extend the functionality of current BCI systems and thus to move towards practical BCI systems for disabled users. Generally speaking many applications that rely on the categorization of brain-activity could possibly profit from the methods developed in this project. Among the potential applications are driver-monitoring (mapping EEG to vigilance levels), interrogative polygraphs (“lie detection”), and clinical applications, for example coma outcome prognosis and depth of anesthesia monitoring.

Research Topics

  • Improving the performance of conventional Brain Computer Interface systems.
  • Detection of scientific interest (curiosity) and retrieval of salient data [here].
  • Affect analysis and emotion recognition using Electroencephalogram and BCI [here].
  • Design and implementation of new BCI paradigms.

Results and resources