A brain-controlled switch for asynchronous control applications

Asynchronous control applications are an important class of application that has not received much attention from the brain-computer interface (BCI) community. This work provides a design for an asynchronous BCI switch and performs the first extensive evaluation of an asynchronous device in attentive, spontaneous electroencephalographic (EEG). The switch design [named the low-frequency asynchronous switch design (LF-ASD)] is based on a new feature set related to imaginary movements in the 1-4 Hz frequency range. This new feature set was identified from a unique analysis of EEG using a bi-scale wavelet. Offline evaluations of a prototype switch demonstrated hit (true positive) rates in the range of 38%-81% with corresponding false positive rates in the range of 0.3%-11.6%. The performance of the LF-ASD was contrasted with two other ASDs: one based on mu-power features and another based on the outlier processing method (OPM) algorithm. The minimum mean error rates for the LF-ASD were shown to be significantly lower than either of these other two switch designs.

[1]  A. Dale Magoun,et al.  Decision, estimation and classification , 1989 .

[2]  G. Birch,et al.  Single-trial processing of event-related potentials using outlier information , 1993, IEEE Transactions on Biomedical Engineering.

[3]  E. Donchin,et al.  EEG-based communication: prospects and problems. , 1996, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[4]  H G Vaughan,et al.  Topography of the human motor potential. , 1968, Electroencephalography and clinical neurophysiology.

[5]  D. Pandya,et al.  Supplementary motor area structure and function: Review and hypotheses , 1985 .

[6]  D J McFarland,et al.  An EEG-based brain-computer interface for cursor control. , 1991, Electroencephalography and clinical neurophysiology.

[7]  C.W. Anderson,et al.  Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks , 1998, IEEE Transactions on Biomedical Engineering.

[8]  W. J. Williams,et al.  Analysis of event related potentials: time-frequency energy distribution. , 1987, Biomedical sciences instrumentation.

[9]  Z. Keirn,et al.  A new mode of communication between man and his surroundings , 1990, IEEE Transactions on Biomedical Engineering.

[10]  S. Bozinovski,et al.  Using EEG alpha rhythm to control a mobile robot , 1988, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[12]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[13]  William D. Penny,et al.  Imagined Hand Movements Identified From The EEG Mu-Rhythm , 1998 .

[14]  C. Brunia Movement and stimulus preceding negativity , 1988, Biological Psychology.

[15]  Gary E. Birch,et al.  Improved single-trial signal extraction of low SNR events , 1994, IEEE Trans. Signal Process..

[16]  G. Pfurtscheller,et al.  EEG-based discrimination between imagination of right and left hand movement. , 1997, Electroencephalography and clinical neurophysiology.

[17]  Gary E. Birch,et al.  Identification of finger flexions from continuous EEG as a brain computer interface , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[18]  Gert Pfurtscheller,et al.  Brain-computer interface: a new communication device for handicapped persons , 1993 .

[19]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .