Seperability of four-class motor imagery data using independent components analysis

By Muhammad Naeem, Clemens Brunner, Robert Leeb, Bernhard Graimann, Gert Pfurtscheller. Journal of Neural Engineering, 2006

303 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f4a72a696d125bd9bf05dc307afed5ba28084586

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Citations in the corpus

Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces (2015, methodology): “The first data set used is data set 2a from BCI competition IV, provided by the Graz group [70].”
Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms (2011, background): “3) Data set IIa, BCI competition IV: Data set IIa [24], from BCI competition IV1 comprises EEG signals from 9 subjects who performed left hand, right hand, foot and tongue MI. EEG 1http://www.bbci.de/competition/iv/ in ria -0 04 76 82 0, v er si on 4 - 24 S ep 2 01 0 signals were recorded using 22 e”
BCI Competition 2008 { Graz data set B (2008, background): “Eight out of the total of nine data sets were analyzed in [2, 3].”
A review of channel selection algorithms for EEG signal processing (2015, methodology): “The performance of this algorithm was evaluated using two datasets, Dataset IIa [49] from BCI competition IV with 22 channels (four motor imagery tasks; left hand, right hand, feet, or tongue) and Dataset IVa [50] from BCI competition III with 118 channels (two motor imagery tasks; right hand and…”
Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI (2011, methodology): “In order to evaluate the performance of the proposed SCSP algorithm, two datasets from publicly available BCI competitions are used, one with moderate number of initial channels (22 channels) [23], and another with much denser electrodes (118 channels) [24].”
Riemannian Approaches in Brain-Computer Interfaces: A Review (2017, methodology): “On the BCI data from [59], the LogEuclidean distance derived from the learned metric has shown better performances than other distances on Pn when used in a 1-NN classifier.”
BCILAB: a platform for brain–computer interface development (2013, methodology): “Dataset A.3 was published as data set IIa 5 of the BCI competition IV, also from Graz University, comprising data from nine subjects who performed 72 trials each, imagining left hand, right hand, feet, and tongue movements [48]; here we also restrict ourselves to left versus right hand movements.”