User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

Brain-Computer Interfaces (BCIs) have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG-) based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA) is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.

[1]  Max Henrion,et al.  Propagating uncertainty in bayesian networks by probabilistic logic sampling , 1986, UAI.

[2]  Mark S. Leeson,et al.  Artificial Intelligence in Medicine Channel Selection and Classification of Electroencephalogram Signals: an Artificial Neural Network and Genetic Algorithm-based Approach , 2022 .

[3]  Gert Pfurtscheller,et al.  Characterization of four-class motor imagery EEG data for the BCI-competition 2005 , 2005, Journal of neural engineering.

[4]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[5]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[6]  Licheng Jiao,et al.  Semisupervised Particle Swarm Optimization for Classification , 2014 .

[7]  Yudong Zhang,et al.  Binary PSO with mutation operator for feature selection using decision tree applied to spam detection , 2014, Knowl. Based Syst..

[8]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[9]  Brendan Z. Allison,et al.  Brain-Computer Interface Research , 2019, SpringerBriefs in Electrical and Computer Engineering.

[10]  G. Pfurtscheller,et al.  The BCI competition III: validating alternative approaches to actual BCI problems , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  Qingfu Zhang,et al.  Multi-objective evolutionary methods for channel selection in Brain-Computer Interfaces: Some preliminary experimental results , 2010, IEEE Congress on Evolutionary Computation.

[12]  Lenan Wu,et al.  UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization , 2013 .

[13]  S. G. Ponnambalam,et al.  Genetic Algorithm and Bayesian Linear Discriminant Analysis Based Channel Selection Method for P300 BCI , 2012, ICRA 2012.

[14]  Olatz Arbelaitz,et al.  An extensive comparative study of cluster validity indices , 2013, Pattern Recognit..

[15]  Peng-Yeng Yin,et al.  Cyber-EDA: Estimation of Distribution Algorithms with Adaptive Memory Programming , 2013 .

[16]  T. Hinterberger,et al.  Automated EEG feature selection for brain computer interfaces , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

[17]  Christa Neuper,et al.  A Comparative Analysis of Multi-Class EEG Classification for Brain Computer Interface , 2005 .

[18]  Minpeng Xu,et al.  Channel Selection Based on Phase Measurement in P300-Based Brain-Computer Interface , 2013, PloS one.

[19]  Gianfranco Piras,et al.  sphet: Spatial Models with Heteroskedastic Innovations in R , 2010 .

[20]  Basilio Sierra,et al.  Fusing multiple image transformations and a thermal sensor with kinect to improve person detection ability , 2013, Eng. Appl. Artif. Intell..

[21]  Concha Bielza,et al.  Mateda-2.0: A MATLAB package for the implementation and analysis of estimation of distribution algorithms , 2010 .

[22]  Toshio Tsuji,et al.  A Quasi-Optimal Channel Selection Method for Bioelectric Signal Classification Using a Partial Kullback–Leibler Information Measure , 2013, IEEE Transactions on Biomedical Engineering.

[23]  R. Santana,et al.  The mixture of trees Factorized Distribution Algorithm , 2001 .

[24]  Cuntai Guan,et al.  Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI , 2011, IEEE Transactions on Biomedical Engineering.

[25]  Bernhard Schölkopf,et al.  Support vector channel selection in BCI , 2004, IEEE Transactions on Biomedical Engineering.

[26]  Pedro Larrañaga,et al.  Feature Subset Selection by Bayesian network-based optimization , 2000, Artif. Intell..

[27]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[28]  Qingguo Wei,et al.  Channel selection by genetic algorithms for classifying single-trial ECoG during motor imagery , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[29]  Amit Konar,et al.  Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata , 2013, Medical & Biological Engineering & Computing.

[30]  Yuhui Ma,et al.  Channel Selection for Optimizing Feature Extraction in an Electrocorticogram-Based Brain-Computer Interface , 2010, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[31]  M. Pelikán,et al.  The Bivariate Marginal Distribution Algorithm , 1999 .

[32]  S. Bonnet,et al.  Channel selection procedure using riemannian distance for BCI applications , 2011, 2011 5th International IEEE/EMBS Conference on Neural Engineering.

[33]  M. Thulasidas,et al.  Robust classification of EEG signal for brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[34]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[35]  Basilio Sierra,et al.  Histogram distance-based Bayesian Network structure learning: A supervised classification specific approach , 2009, Decis. Support Syst..

[36]  Irena Koprinska,et al.  Classification of Brain-Computer Interface Data , 2008, AusDM.

[37]  Kwang-Eun Ko,et al.  Optimal EEG Channel Selection for Motor Imagery BCI System Using BPSO and GA , 2012, RiTA.

[38]  Kurt Hornik,et al.  The support vector machine under test , 2003, Neurocomputing.

[39]  G. Pfurtscheller,et al.  Designing optimal spatial filters for single-trial EEG classification in a movement task , 1999, Clinical Neurophysiology.

[40]  Concha Bielza,et al.  Regularized logistic regression and multiobjective variable selection for classifying MEG data , 2012, Biological Cybernetics.

[41]  S. Baluja,et al.  Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space , 1997 .