Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis

Authors:
Clemens Brunner;Muhammad Naeem;Robert Leeb;Bernhard Graimann;Gert Pfurtscheller
Affiliations:
Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria;Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria;Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria;Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria;Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria
Venue:
Pattern Recognition Letters
Year:
2007

Citing 5
Cited 7

Floating search methods in feature selection

Pattern Recognition Letters
An information-maximization approach to blind separation and blind deconvolution

Neural Computation
A fast fixed-point algorithm for independent component analysis

Neural Computation
Independent component analysis: algorithms and applications

Neural Networks
A blind source separation technique using second-order statistics

IEEE Transactions on Signal Processing

Denoising of magnetoencephalographic data using spatial averaging

Neurocomputing
A time-frequency convolutional neural network for the offline classification of steady-state visual evoked potential responses

Pattern Recognition Letters
Mutual information-based selection of optimal spatial-temporal patterns for single-trial EEG-based BCIs

Pattern Recognition
Classification of primitive shapes using brain-computer interfaces

Computer-Aided Design
Harmonic Mean of Kullback---Leibler Divergences for Optimizing Multi-Class EEG Spatio-Temporal Filters

Neural Processing Letters
Non-homogeneous spatial filter optimization for ElectroEncephaloGram (EEG)-based motor imagery classification

Neurocomputing
A brain-computer interface for high-level remote control of an autonomous, reinforcement-learning-based robotic system for reaching and grasping

Proceedings of the 19th international conference on Intelligent User Interfaces

Quantified Score

Hi-index	0.10

Visualization

Abstract

Three independent components analysis (ICA) algorithms (Infomax, FastICA and SOBI) have been compared with other preprocessing methods in order to find out whether and to which extent spatial filtering of EEG data can improve single trial classification accuracy. As reference methods, common spatial patterns (CSP) (a supervised method, whereas all ICA algorithms are unsupervised), bipolar derivations and the original raw monopolar data were used. In addition to only performing ICA, the number of components was reduced with PCA before calculating a spatial filter for Infomax and FastICA. The multichannel data (22 channels) of eight subjects, consisting of two sessions recorded on different days, was analyzed. The task was to perform motor imagery of the left hand, right hand, foot or tongue, respectively, during predefined time slices (cued paradigm). For a measure of fitness, classification accuracies for both cross-validated results using data from just one session as well as simulated online results (representing the session-to-session transfer) were calculated. In the latter case, the spatial filters and classifiers were computed for one session and applied to the completely unseen second session. For the data analyzed in this study, Infomax outperformed the other two ICA variants by far, both in the cross-validated as well as in the simulated online case. CSP, on the other hand, yielded significantly lower classification accuracies than Infomax for the cross-validated results, whereas there is no statistically significant difference when it comes to simulated online data. Performing PCA before ICA improved the results in the case of FastICA, whereas the classification accuracies dropped significantly for Infomax.