Ensembles of adaptive spatial filters increase BCI performance: an online evaluation
| dc.contributor.author | Sannelli, Claudia | |
| dc.contributor.author | Vidaurre Arbizu, Carmen | |
| dc.contributor.author | Müller, Klaus Robert | |
| dc.contributor.author | Blankertz, Benjamin | |
| dc.contributor.department | Matemáticas | es_ES |
| dc.contributor.department | Matematika | eu |
| dc.date.accessioned | 2020-09-22T07:53:17Z | |
| dc.date.available | 2020-09-22T07:53:17Z | |
| dc.date.issued | 2016 | |
| dc.description.abstract | Objective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain–computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Approach: Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. Main results: The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. Significance: CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI inefficiency to one-fourth in comparison to previous non-adaptive paradigms. | en |
| dc.description.sponsorship | The work of Claudia Sannelli, Carmen Vidaurre and Klaus-Robert Müller was funded by the German Ministry for Education and Research (BMBF) under Grant 01IS14013A-E and Grant 01GQ1115, as well as by the Deutsche Forschungsgesellschaft (DFG) under Grant MU 987/19-1, MU987/14-1 and DFG MU 987/3-2. Additionally, the work of Klaus-Robert Müller was funded by the Brain Korea 21 Plus Program. The work of Benjamin Blankertz was funded by the BMBF contract 01GQ0850. | en |
| dc.format.extent | 28 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Claudia Sannelli et al 2016 J. Neural Eng. 13 046003 | en |
| dc.identifier.doi | 10.1088/1741-2560/13/4/046003 | |
| dc.identifier.issn | 1741-2552 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/38216 | |
| dc.language.iso | eng | en |
| dc.publisher | IOP | en |
| dc.relation.ispartof | Journal of Neural Engineering, 2016, 13(4), 046003 | en |
| dc.relation.publisherversion | https://doi.org/10.1088/1741-2560/13/4/046003 | |
| dc.rights | © 2016 IOP Publishing Ltd. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Spatial filters | en |
| dc.subject | Brain-computer interfacing | en |
| dc.title | Ensembles of adaptive spatial filters increase BCI performance: an online evaluation | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | bfc272aa-95a8-45b2-ada9-5e679009a082 | |
| relation.isAuthorOfPublication.latestForDiscovery | bfc272aa-95a8-45b2-ada9-5e679009a082 |