Near infrared spectroscopy for brain-computer interface development (Englisch)

Wie erhalte ich diesen Titel?

A Brain-computer Interface (BCI) can be developed using the optical response of Near Infrared Spectroscopy (NIRS) which measures metabolic brain activation. NIRS can localize brain regions with a spatial resolution in mm and temporal resolution in hundreds of ms. NIRS has the advantages of noninvasiveness, portability and affordability. The authors have developed a multi-channel NIRS-BCI that distinguishes the brain activation during imagination of left hand and right hand movement. This mechanism is being incorporated in a word speller, in an on-going research project, to help severely disabled persons to communicate. Experiments with a volunteer have shown that the HMM (Hidden Markov Model) classifier performers better (average accuracy of 91.29%) than the SVM (Support Vector Machine) classifier (average accuracy of 75.62%). This might be due to considerable variations in the temporal domain in the performance of the tasks, and such variations may be better dealt with by dynamic machines like HMM. NIRS avoids the noise prominent in the EEG, and is less cumbersome to use, as there is no need for applying conducting gel. The most important advantage of the NIRS is its ability to localize brain activity noninvasively. This provides us with an excellent opportunity to use a variety of motor and cognitive tasks, and to detect signals from specific regions of the cortex for the development of powerful and user-friendly BCIs.

Inhaltsverzeichnis Konferenzband

Die Inhaltsverzeichnisse werden automatisch erzeugt und basieren auf den im Index des TIB-Portals verfügbaren Einzelnachweisen der enthaltenen Beiträge. Die Anzeige der Inhaltsverzeichnisse kann daher unvollständig oder lückenhaft sein.

8
Theoretical and experimental basis for the development of direct noninvasive BCI
Grave de Peralta Menendez, R. / Millan, J. del R. / Morier, P. / Gonzalez Andino, S.L. | 2006
12
The use of fuzzy inference systems for classification in EEG-based brain-computer interfaces
Lotte, F. | 2006
14
Regularised CSP for sensor selection in BCI
Farquhar, J. / Hill, N.J. / Lal, T.N. / Schölkopf, B. | 2006
16
High frequency bands and estimated local field potentials to improve single-trial classification of electroencephalographic signals
Ferrez, P.W. / Galan Moles, F. / Buttfield, A. / Gonzalez Andino, S.L. / Grave de Peralta Menendez, R. / Millan, J. del R. | 2006
22
An iterative algorithm for spatio-temporal filter optimization
Tomioka, R. / Dornhege, G. / Aihara, K. / Müller, K.R. | 2006
24
Accurate hand trajectory prediction by real and synthetic EEG
Grave de Peralta Menendez, R. / Morier, P. / Millan, J. del R. / Gonzalez Andino, S.L. | 2006
26
EEG single-trial classification of four classes of imaginary wrist movements based on Gabor coefficients
Vuckovic, A. / Sepulveda, F. | 2006
28
Feature dimensionality reduction by manifold learning in brain-computer interface design
Gan, J.Q. | 2006
34
Brain computer interfacing in space-time-frequency domain
Nazarpour, K. / Shoker, L. / Sanei, S. | 2006
36
Engineering the brain signals - preprocessing
Singh, H. / Hines, E. / Stocks, N. / Syan, C. | 2006
40
Real-time feedback solution applied to the motor imagery based BCI protocol
Parini, S. / Maggi, L. / Piccini, L. / Andreoni, G. | 2006
42
A fuzzy logic classifier design for enhancing BCI performance
Herman, P. / Prasad, G. / McGinnity, T.M. | 2006
46
Neuroelectrical source imaging of mu rhythm control for BCI applications
Mattiocco, M. / Mattia, D. / Babiloni, F. / Bufalari, S. / Marciani, M.G. / Cincotti, F. | 2006
54
A Bayesian approach for adaptive BCI classification
Kawanabe, M. / Krauledat, M. / Blankertz, B. | 2006
56
Online classifier adaptation in high frequency EEG
Buttfield, A. / Ferrez, P.W. / Millan, J. del R. | 2006
60
Brain state differences between calibration and application session influence BCI classification accuracy
Krauledat, M. / Losch, F. / Curio, G. | 2006
64
"Brain switch" BCI system based on EEG during foot movement imagery
Kanoh, S. / Scherer, R. / Yoshinobu, T. / Hoshimiya, N. / Pfurtscheller, G. | 2006
66
Haptic feedback compared with visual feedback for BCI
Kauhanen, L. / Palomäki, T. / Jylänki, P. / Aloise, F. / Nuttin, M. / Millan, J. del R. | 2006
68
Brain-computer interface based on non-motor imagery
Cabrera, A.F. / Lund, M.E. / Christensen, D.M. / Nielsen, T.N. / Skov-Madsen, G. / Nielsen, K.D. | 2006
72
Effects of multimodal user interface in BCI performance
Ron-Angevin, R. / Diaz-Estrella, A. | 2006
76
Is the locus of control of reinforcement a predictor of brain-computer interface performance?
Burde, W. / Blankertz, B. | 2006
78
The relevance of feedback type on BCI classification results
Wriessnegger, S. / Scherer, R. / Maier, C. / Mörth, K. / Pfurtscheller, G. / Neuper, C. | 2006
80
Single-trial EEG classification of executed and imagined hand movements in hemiparetic stroke patients
Mohapp, A. / Scherer, R. / Keinrath, C. / Grieshofer, P. / Pfurtscheller, G. / Neuper, C. | 2006
86
Robust classification of electrocorticographic signals for BCI
Shenoy, P. / Miller, K.J. / Evans, N. / Ojemann, J. / Rao, R.P.N. | 2006
92
Analogue P3O0-based BCI pointing device
Citi, L. / Poli, R. / Cinel, C. | 2006
98
Brain-computer interfacing using selective attention and frequency-tagged stimuli
Desain, P. / Hupse, A.M.G. / Kallenberg, M.G.J. / de Kruif, B.J. / Schaefer, R.S. | 2006
100
Heart rate-controlled EEG-based BCI: the Graz hybrid BCI
Pfurtscheller, G. / Scherer, R. / Müller-Putz, G.R. | 2006
102
First steps towards the NIRS-based Graz-BCI
Leeb, R. / Bauernfeind, G. / Wriessnegger, S. / Scharfetter, H. / Pfurtscheller, G. | 2006
104
Near infrared spectroscopy for brain-computer interface development
Sitaram, R. / Haihong, Z. / Uludag, K. / Cuntai, G. / Hoshi, Y. / Birbaumer, N. | 2006
106
Functional magnetic resonance imaging based BCI for neurorehabilitation
Sitaram, R. / Caria, A. / Veit, R. / Uludag, K. / Gaber, T. / Kübler, A. / Birbaumer, N. | 2006
108
The Berlin brain-computer interface presents the novel mental typewriter Hex-o-Spell
Blankertz, B. / Dornhege, G. / Krauledat, M. / Schröder, M. / Williamson, J. / Murray-Smith, R. / Müller, K.R. | 2006
110
Asynchronous (self-paced) brain-computer communication: exploring the "freeSpace" virtual environment
Scherer, R. / Lee, F. / Bischof, H. / Pfurtscheller, G. | 2006
112
Non-invasive brain-computer interface for mental control of a simulated wheelchair
Lew, E. / Nuttin, M. / Ferrez, P.W. / Degeest, A. / Buttfield, A. / Vanacker, G. / Millan, J. del R. | 2006
114
Neural internet for ALS patients
Karim, A.A. / Bensch, M. / Mellinger, J. / Hinterberger, T. / Schröder, M. / Bogdan, M. / Neumann, N. / Kübler, A. / Rosenstiel, W. / Birbaumer, N. | 2006
116
Robot operation based on pattern recognition of EEG signals
Inoue, K. / Kumamaru, K. / Pfurtscheller, G. | 2006
122
BCI-info.org - An international internet-platform for the BCI community
Graimann, B. / Pfurtscheller, G. | 2006
126
Usage of Simulink for brain-computer interface experiments
Guger, C. / Laundl, F. / Krausz, G. / Niedermayer, I. / Edlinger, G. | 2006