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Computers 2016, 5(2), 5; doi:10.3390/computers5020005

An Efficient Decoder for the Recognition of Event-Related Potentials in High-Density MEG Recordings

1
Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Brenneckestr. 6, Magdeburg 39118, Germany
2
Department of Neurology, Otto-von-Guericke University, Leipziger Str. 44, Magdeburg 39120, Germany
3
Faculty of Computer Science, Otto-von-Guericke University, Universitätsplatz 2, Magdeburg 39106, Germany
4
German Center for Neurodegenerative Diseases (DZNE), Leipziger Str. 44, Magdeburg 39120, Germany
5
Center for Behavioral Brain Sciences (CBBS), Brenneckestr. 6, Magdeburg 39118, Germany
This paper is an extended version of our paper published in Reichert, C.; Dürschmid, S.; Kruse, R.; Hinrichs, H. Efficient recognition of event-related potentials in high-density MEG recordings. In Proceedings of the 7th Computer Science and Electronic Engineering Conference (CEEC) 2015, Colchester, UK, 24–25 September 2015; pp. 81–86.
*
Author to whom correspondence should be addressed.
Academic Editor: Laith Al-Jobouri
Received: 15 February 2016 / Revised: 5 April 2016 / Accepted: 9 April 2016 / Published: 12 April 2016
View Full-Text   |   Download PDF [606 KB, uploaded 12 April 2016]   |  

Abstract

Brain–computer interfacing (BCI) is a promising technique for regaining communication and control in severely paralyzed people. Many BCI implementations are based on the recognition of task-specific event-related potentials (ERP) such as P300 responses. However, because of the high signal-to-noise ratio in noninvasive brain recordings, reliable detection of single trial ERPs is challenging. Furthermore, the relevant signal is often heterogeneously distributed over several channels. In this paper, we introduce a new approach for recognizing a sequence of attended events from multi-channel brain recordings. The framework utilizes spatial filtering to reduce both noise and signal space considerably. We introduce different models that can be used to construct the spatial filter and evaluate the approach using magnetoencephalography (MEG) data involving P300 responses, recorded during a BCI experiment. Compared to the accuracy achieved in the BCI experiment performed without spatial filtering, the recognition rate increased significantly to up to 95.3% on average (SD: 5.3%). In combination with the data-driven spatial filter construction we introduce here, our framework represents a powerful method to reliably recognize a sequence of brain potentials from high-density electrophysiological data, which could greatly improve the control of BCIs. View Full-Text
Keywords: brain–computer interface; BCI; magnetoencephalography (MEG); ERP; CCA; spatial filter; P300 brain–computer interface; BCI; magnetoencephalography (MEG); ERP; CCA; spatial filter; P300
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Reichert, C.; Dürschmid, S.; Kruse, R.; Hinrichs, H. An Efficient Decoder for the Recognition of Event-Related Potentials in High-Density MEG Recordings. Computers 2016, 5, 5.

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