+ Site Statistics
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

Ensembles of adaptive spatial filters increase BCI performance: an online evaluation

Ensembles of adaptive spatial filters increase BCI performance: an online evaluation

Journal of Neural Engineering 13(4): 046003

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. 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. 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. 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.

(PDF emailed within 0-6 h: $19.90)

Accession: 057775390

Download citation: RISBibTeXText

PMID: 27187530

DOI: 10.1088/1741-2560/13/4/046003

Related references

Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction. Neuroimage 25(4): 1056-1067, 2005

Adaptive online performance evaluation of video trackers. IEEE Transactions on Image Processing 21(5): 2812-2823, 2012

Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data. IEEE Transactions on Image Processing 2(3): 327-340, 1993

ICA-based artifact correction improves spatial localization of adaptive spatial filters in MEG. Neuroimage 78: 284-294, 2014

Emergence of overlap in ensembles of spatial multiplexes and statistical mechanics of spatial interacting network ensembles. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics 89(1): 012806, 2014

Adaptive scene dependent filters for segmentation and online learning of visual objects. Neurocomputing 70(7-9): 1235-1246, 2007

Adaptive filters in spatial vision. Investigative Ophthalmology & Visual Science 31(4 ABSTR ISSUE): 430, 1990

Reconstruction of correlated brain activity with adaptive spatial filters in MEG. Neuroimage 49(3): 2387-2400, 2010

A model of human pattern perception: association fields for adaptive spatial filters. Spatial Vision 12(3): 363-394, 1999

Online EEG artifact removal for BCI applications by adaptive spatial filtering. Journal of Neural Engineering 15(5): 056009, 2018

On the performance of adaptive pruned Volterra filters. Signal Processing 93(7): 1909-1920, 2013

Design and performance optimization of fiber optic adaptive filters. Applied Optics 30(14): 1826-1838, 1991

Performance analysis of novel steepest descent algorithms for adaptive filters. Signal Processing 51(1): 29-39, 1996

Performance of constant modulus adaptive digital filters for interference cancellation. Signal Processing 26(2): 185-196, 1992

Mean-square performance of the family of adaptive filters with selective partial updates. Signal Processing 88(8): 2008-2018, 2008