Abstract
The first steps in the attempts to unravel the perception of natural speech and to continuously follow the listener’s brain activity, are to find and characterize the perception-related phenomena and the relevant features in measured signals. In this paper, the problem was tackled by searching for consistencies in single-trial magnetoencephalography (MEG) responses to repeated 49-s audiobook passage. The canonical correlation analysis (CCA) based modeling was applied to find the maximally correlating signal projections across the single-trial responses. Using the trained model and separate test trials, projected MEG time series showed consistent fluctuations in frequencies typically below 10 Hz, with cross-trial correlations up to 0.25 (median). These statistically significant correlations between test trial projections suggest that the proposed method can extract perception-related time series from long-lasting MEG responses to natural speech.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Guimaraes, M.P., Wong, D.K., Uy, E.T., Grosenick, L., Suppes, P.: Single-trial classification of MEG recordings. IEEE Trans. Biomed. Eng. 54, 436–443 (2007)
Luo, H., Poeppel, D.: Phase patterns of neuronal responses reliably discriminate speech in human auditory cortex. Neuron 54, 1001–1010 (2007)
Suppes, P., Han, B., Lu, Z.-L.: Brain-wave recognition of words. Proc. Natl. Acad. Sci. USA 94, 14965–14969 (1997)
Suppes, P., Han, B., Lu, Z.-L.: Brain-wave recognition of sentences. Proc. Natl. Acad. Sci. USA 95, 15861–15866 (1998)
Suppes, P., Han, B., Epelboim, J., Lu, Z.-L.: Invariance between subjects of brain wave representations of language. Proc. Natl. Acad. Sci. 96, 12953–12958 (1999)
Koskinen, M., Viinikanoja, J., Kurimo, M., Klami, A., Kaski, S., Hari, R.: Identifying fragments of natural speech from the listener’s MEG signals. Hum. Brain Mapp., doi: 10.1002/hbm.22004 (in press)
Taulu, S., Simola, J.: Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Phys. Med. Biol. 51, 1759–1768 (2006)
Hotelling, H.: Relations between two sets of variates. Biometrika 28, 321–377 (1936)
Hari, R., Levänen, S., Raij, T.: Timing of human cortical activation sequences during cognition: role of MEG. Trends Cogn. Sci. 4, 455–462 (2000)
Taulu, S., Hari, R.: Removal of magnetoencephalographic artifacts with temporal signal-space separation: demonstration with single-trial auditory-evoked responses. Hum. Brain. Mapp. 30, 1524–1534 (2009)
Lahti, L., Myllykangas, S., Knuutila, S., Kaski, S.: Dependency detection with similarity constraints. In: IEEE International Workshop on Machine Learning for Signal Processing XIX, Piscataway, NJ, USA, pp. 89–94. IEEE (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Koskinen, M. (2012). Finding Consistencies in MEG Responses to Repeated Natural Speech. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-34713-9_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34712-2
Online ISBN: 978-3-642-34713-9
eBook Packages: Computer ScienceComputer Science (R0)