Design of an EEG-based Brain-Computer Interface (BCI) from Standard Components running in Real-time under Windows - Entwurf eines EEG-basierten Brain-Computer Interfaces (BCI) mit Standardkomponenten, das unter Windows in Echtzeit arbeitet

An EEG-based brain-computer interface (BCI) is a direct connection between the human brain and the computer. Such a communication system is needed by patients with severe motor impairments (e.g. late stage of Amyotrophic Lateral Sclerosis) and has to operate in real-time. This paper describes the selection of the appropriate components to construct such a BCI and focuses also on the selection of a suitable programming language and operating system. The multichannel system runs under Windows 95, equipped with a real-time Kernel expansion to obtain reasonable real-time operations on a standard PC. Matlab controls the data acquisition and the presentation of the experimental paradigm, while Simulink is used to calculate the recursive least square (RLS) algorithm that describes the current state of the EEG in real-time. First results of the new low-cost BCI show that the accuracy of differentiating imagination of left and right hand movement is around 95%.

[1]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[2]  R A Normann,et al.  The Utah intracortical Electrode Array: a recording structure for potential brain-computer interfaces. , 1997, Electroencephalography and clinical neurophysiology.

[3]  J. Wolpaw,et al.  Multichannel EEG-based brain-computer communication. , 1994, Electroencephalography and clinical neurophysiology.

[4]  Dennis J. McFarland,et al.  Design and operation of an EEG-based brain-computer interface with digital signal processing technology , 1997 .

[5]  G Pfurtscheller,et al.  Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[6]  G. Pfurtscheller,et al.  Graz brain-computer interface II: towards communication between humans and computers based on online classification of three different EEG patterns , 1996, Medical and Biological Engineering and Computing.

[7]  G. Pfurtscheller,et al.  Motor imagery activates primary sensorimotor area in humans , 1997, Neuroscience Letters.

[8]  J J Vidal,et al.  Toward direct brain-computer communication. , 1973, Annual review of biophysics and bioengineering.

[9]  G Pfurtscheller,et al.  Adaptive Autoregressive Modeling used for Single-trial EEG Classification - Verwendung eines Adaptiven Autoregressiven Modells für die Klassifikation von Einzeltrial-EEG-Daten , 1997, Biomedizinische Technik. Biomedical engineering.

[10]  D.B. Popovic,et al.  Sensory nerve recording for closed-loop control to restore motor functions , 1993, IEEE Transactions on Biomedical Engineering.

[11]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[12]  G Pfurtscheller,et al.  Selection of electrode positions for an EEG-based Brain Computer Interface (BCI). Auswahl von Elektrodenpositionen für ein auf EEG-Ableitungen basierendes Brain Computer Interface (BCI) , 1994, Biomedizinische Technik. Biomedical engineering.

[13]  G. Pfurtscheller,et al.  Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery. , 1999, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[14]  Gert Pfurtscheller,et al.  Prompt recognition of brain states by their EEG signals , 1997 .