Graph-Based Transfer Learning for Managing Brain Signals Variability in NIRS-Based BCIs
One of the major limitations to the use of brain-computer interfaces (BCIs) based on near-infrared spectroscopy (NIRS) in realistic interaction settings is the long calibration time needed before every use in order to train a subject-specific classifier. One way to reduce this calibration time is to use data collected from other users or from previous recording sessions of the same user as a training set. However, brain signals are highly variable and using heterogeneous data to train a single classifier may dramatically deteriorate classification performance. This paper proposes a transfer learning framework in which we model brain signals variability in the feature space using a bipartite graph. The partitioning of this graph into sub-graphs allows creating homogeneous groups of NIRS data sharing similar spatial distributions of explanatory variables which will be used to train multiple prediction models that accurately transfer knowledge between data sets.
KeywordsBrain-computer interface (BCI) near-infrared spectroscopy (NIRS) brain signals variability transfer learning bipartite graph partitioning
Unable to display preview. Download preview PDF.
- 4.Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces. Journal of Neural Engineering 4, R1–R13 (2007)Google Scholar
- 7.Krauledat, M., Tangermann, M., Blankertz, B., Muller, K.R.: Towards Zero Training for Brain-Computer Interfacing. Plos One 3(8), e2967 (2008)Google Scholar
- 8.Lotte, F., Guan, C.: Learning from other subjects helps reducing brain-computer interface calibration time. In: International Conference on Audio Speech and Signal Processing (ICASSP), pp. 614–617 (2010)Google Scholar
- 9.Falzi, S., Grozea, C., Danoczy, M., Popescu, F., Blankertz, B., Muller, K.R.: Subject independent EEG-based BCI decoding. In: Neural Information Processing Systems Conference (NIPS), pp. 513–521 (2009)Google Scholar
- 13.Power, S.D., Kushki, A., Chau, T.: Intersession Consistency of Single-Trial Classification of the Prefrontal Response to Mental Arithmetic and the No-Control State by NIRS. Plos One 7(7), e37791 (2012)Google Scholar
- 15.Abibullaev, B., An, J., Jin, S.H., Lee, S.H., Moon, J.I.: Minimizing Inter-Subject Variability in fNIRS-based Brain-Computer Interfaces via Multiple-Kernel Support Vector Learning. Medical Engineering and Physics, S1350-4533(13)00183-5 (2013)Google Scholar
- 17.Zha, H., He, X., Ding, C., Simon, H., Gu, M.: Bipartite Graph Partitioning and Data Clustering. In: CIKM 2001, Atlanta, Georgia, USA (2001)Google Scholar
- 18.Dhillon, I.S.: Co-clustering documents and words using Bipartite Spectral Graph Partitioning. In: KDD, San Francisco, California, USA (2001)Google Scholar