Information Technology Reference
In-Depth Information
Blankertz B, Kawanabe M, Tomioka R, Hohlefeld F, Nikulin V, M
ü
ller KR (2008a) Invariant
common spatial patterns: alleviating nonstationarities in brain-computer
interfacing.
In:
Advances in neural information processing systems, vol 20. MIT Press, Cambridge
Blankertz B, Tomioka R, Lemm S, Kawanabe M, M
ü
ller KR (2008b) Optimizing spatial
filters for
robust EEG single-trial analysis. IEEE Signal Proc Mag 25(1):41
-
56
Blankertz B, Lemm S, Treder M, Haufe S, M
ü
ller KR (2010) Single-trial analysis and
classification of ERP components
—
a tutorial. Neuroimage 51(4):1303
-
1309
Boostani R, Moradi MH (2004) A new approach in the BCI research based on fractal dimension as
feature and adaboost as classifier. J Neural Eng 1(4):212
217
-
Brodu N, Lotte F, L
cuyer A (2011) Comparative study of band-power extraction techniques for
motor imagery classification. In: IEEE symposium on computational intelligence, cognitive
algorithms, mind, and brain (CCMB) 2011, IEEE, pp 1
é
6
-
Brodu N, Lotte F, L
cuyer A (2012) Exploring two novel features for EEG-based brain-computer
interfaces: Multifractal cumulants and predictive complexity. Neurocomputing 79(1):87
é
94
-
Browne MW (2000) Cross-validation methods. J Math Psychol 44(1):108
132
Brunner C, Naeem M, Leeb R, Graimann B, Pfurtscheller G (2007) Spatial
filtering and selection
of optimized components in four class motor imagery EEG data using independent components
analysis. Pattern Recogn Lett 28(8):957
-
964. doi:
10.1016/j.patrec.2007.01.002
.
http://www.
Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Knowledge
Discovery and Data Mining 2:121
-
167
Caramia N, Ramat S, Lotte F (2014) Optimizing spatial
filter pairs for EEG classi
cation based on
phase synchronization. In: International conference on audio, speech and signal processing
(ICASSP
'
2014)
Congedo M, Lotte F, L
é
cuyer A (2006) Classi
cation of movement intention by spatially
filtered
electromagnetic inverse solutions. Phys Med Biol 51(8):1971
-
1989
Devlaminck D (2011) Optimization of brain-computer interfaces. PhD thesis, University of Ghent
Dornhege G, Blankertz B, Curio G, M
ü
ller K (2004) Boosting bit rates in non-invasive EEG
single-trial classi
cations by feature combination and multi-class paradigms. IEEE Trans
Biomed Eng 51(6):993
-
1002
Dornhege G, Blankertz B, Krauledat M, Losch F, Curio G, M
ü
ller KR (2006) Combined
optimization of spatial and temporal filters for improving brain-computer interfacing. IEEE
Trans Biomed Eng 53(11):2274
-
2281
Duda RO, Hart PE, Stork DG (2001) Pattern recognition, 2nd edn. Wiley, New York
Farquhar J, Hill N, Lal T, Sch
-
lkopf B (2006) Regularised CSP for sensor selection in BCI. In:
Proceedings of the 3rd international BCI workshop
Fatourechi M, Bashashati A, Ward R, Birch G (2007) EMG and EOG artifacts in brain computer
interface systems: a survey. Clin Neurophysiol 118(3):480
ö
494
-
Fazel-Rezai R, Allison B, Guger C, Sellers E, Kleih S, K
bler A (2012) P300 brain computer
interface: current challenges and emerging trends. Frontiers Neuroeng 5(14). doi:
10.3389/
Friedman JHK (1997) On bias, variance, 0/1-loss, and the curse-of-dimensionality. Data Min
Knowl Disc 1(1):55
ü
77
Friedrich E, Scherer R, Neuper C (2012) The effect of distinct mental strategies on classi
cation
performance for brain-computer interfaces. Int J Psychophysiol. doi:
10.1016/j.ijpsycho.2012.
Fukunaga K (1990) Statistical pattern recognition, 2nd edn. Academic Press Inc., San Diego
Garrett D, Peterson DA, Anderson CW, Thaut MH (2003) Comparison of linear, nonlinear, and
feature selection methods for EEG signal classi
cation. IEEE Trans Neural Syst Rehabil Eng
11:141
-
144
Gouy-Pailler C, Achard S, Rivet B, Jutten C, Maby E, Souloumiac A, Congedo M (2007)
Topographical dynamics of brain connections for the design of asynchronous brain-computer
-
