Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9103
Main Title: Ensembles of adaptive spatial filters increase BCI performance: an online evaluation
Author(s): Sannelli, Claudia
Vidaurre, Carmen
Müller, Klaus-Robert
Blankertz, Benjamin
Type: Article
Language Code: en
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.
URI: https://depositonce.tu-berlin.de/handle/11303/10115
http://dx.doi.org/10.14279/depositonce-9103
Issue Date: 17-May-2016
Date Available: 14-Oct-2019
DDC Class: 610 Medizin und Gesundheit
004 Datenverarbeitung; Informatik
Subject(s): brain computer interfaces
motor imagery
BCI inefficiency
performance increase
spatial filters
online evaluation
Sponsor/Funder: BMBF, 01IS14013, Verbundprojekt: BBDC - Berliner Kompetenzzentrum für Big Data
BMBF, 01GQ1115, Adaptive Gehirn-Computer-Schnittstellen (BCI) in nichtstationären Umgebungen
BMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktion - Teilprojekte A1, A3, A4, B4, W3, Zentrum
License: https://creativecommons.org/licenses/by-nc-nd/3.0/
Journal Title: Journal of neural engineering
Publisher: Institute of Physics Publishing (IOP)
Publisher Place: Bristol
Volume: 13
Article Number: 046003
Publisher DOI: 10.1088/1741-2560/13/4/046003
EISSN: 1741-2552
ISSN: 1741-2560
Appears in Collections:FG Neurotechnologie » Publications

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