Rapid prototyping of an EEG-based brain-computer interface (BCI)

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 9; no. 1; pp. 49 - 58
• Main Authors: Guger, C., Schlogl, A., Neuper, C., Walterspacher, D., Strein, T., Pfurtscheller, G.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2001; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adult; Algorithms; Brain - physiopathology; Brain computer interfaces; Channels; Classification; Classification algorithms; Communication Aids for Disabled; Computer interfaces; Computer Systems; Cortical Synchronization - instrumentation; Digital signal processing; Discriminant Analysis; Electroencephalography; Electroencephalography - instrumentation; Equipment Design - instrumentation; Human computer interaction; Humans; Interfaces (computer); Internet; Least-Squares Analysis; Male; Mathematical models; Matlab; Neuromuscular Diseases - physiopathology; On-line systems; Parameter estimation; Performance analysis; Prototypes; Rapid prototyping; Real time; Real time systems; Regression Analysis; Reproducibility of Results; Software prototyping; Studies; System testing; Time Factors; User-Computer Interface
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/7333.918276

The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional DSP board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive model and linear discriminant analysis.