Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions

Brain Computer Interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is Motor Imagery (MI). In BCI applications, the ElectroEncephaloGraphy (EEG) is a very popular measurement for brain dynamics because of its non-invasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. This difficulty lies in the selection of the correct EEG channels, the signal-tonoise ratio of these signals and how to discern the redundant information among them. BCI systems are composed of a wide range of components that perform signal pre-processing, feature extraction and decision making. In this paper, we define a new BCI Framework, named Enhanced Fusion Framework, where we propose three different ideas to improve the existing MI-based BCI frameworks. Firstly, we include an additional pre-processing step of the signal: a differentiation of the EEG signal that makes it time-invariant. Secondly, we add an additional frequency band as feature for the system: the Sensory Motor Rhythm band, and we show its effect on the performance of the system. Finally, we make a profound study of how to make the final decision in the system. We propose the usage of both up to six types of different classifiers and a wide range of aggregation functions (including classical aggregations, Choquet and Sugeno integrals and their extensions and overlap functions) to fuse the information given by the considered classifiers. We have tested this new system on a dataset of 20 volunteers performing motor imagery-based braincomputer interface experiments. On this dataset, the new system achieved a 88.80% of accuracy. We also propose an optimized version of our system that is able to obtain up to 90, 76%. Furthermore, we find that the pair Choquet/Sugeno integrals and overlap functions are the ones providing the best results.

[1]  Amit Konar,et al.  Performance analysis of LDA, QDA and KNN algorithms in left-right limb movement classification from EEG data , 2010, 2010 International Conference on Systems in Medicine and Biology.

[2]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

[3]  J. Kacprzyk,et al.  The Ordered Weighted Averaging Operators: Theory and Applications , 1997 .

[4]  Stefan Haufe,et al.  Single-trial analysis and classification of ERP components — A tutorial , 2011, NeuroImage.

[5]  T. Martin McGinnity,et al.  Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain–Computer Interface Classification of Motor Imagery Induced EEG Patterns , 2017, IEEE Transactions on Fuzzy Systems.

[6]  Humberto Bustince,et al.  Using the Choquet Integral in the Pooling Layer in Deep Learning Networks , 2018, NAFIPS.

[7]  R P Lesser,et al.  Functional significance of the mu rhythm of human cortex: an electrophysiologic study with subdural electrodes. , 1993, Electroencephalography and clinical neurophysiology.

[8]  Ronald R. Yager,et al.  Generalized OWA Aggregation Operators , 2004, Fuzzy Optim. Decis. Mak..

[9]  Xingyu Wang,et al.  Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI , 2019, IEEE Transactions on Cybernetics.

[10]  Chin-Teng Lin,et al.  Multimodal Fuzzy Fusion for Enhancing the Motor-Imagery-Based Brain Computer Interface , 2019, IEEE Computational Intelligence Magazine.

[11]  Andrzej Cichocki,et al.  EmotionMeter: A Multimodal Framework for Recognizing Human Emotions , 2019, IEEE Transactions on Cybernetics.

[12]  Humberto Bustince,et al.  Preaggregation Functions: Construction and an Application , 2016, IEEE Transactions on Fuzzy Systems.

[13]  K.-R. Muller,et al.  Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.

[14]  Pachamuthu Rajalakshmi,et al.  A Novel Classification for EEG Based Four Class Motor Imagery Using Kullback-Leibler Regularized Riemannian Manifold , 2018, 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom).

[15]  Humberto Bustince,et al.  General overlap functions , 2019, Fuzzy Sets Syst..

[16]  Miguel Aguilera,et al.  Signatures of criticality in a maximum entropy model of the C. elegans brain during free behaviour , 2017, ECAL.

[17]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[18]  Francisco Herrera,et al.  Enhancing Multiclass Classification in FARC-HD Fuzzy Classifier: On the Synergy Between $n$-Dimensional Overlap Functions and Decomposition Strategies , 2015, IEEE Transactions on Fuzzy Systems.

[19]  Mohammad Reza Karami Mollaei,et al.  A novel method for classification of BCI multi-class motor imagery task based on Dempster-Shafer theory , 2019, Inf. Sci..

[20]  Tzyy-Ping Jung,et al.  EEG-Based Attention Tracking During Distracted Driving , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[21]  Humberto Bustince,et al.  Analyzing the performance of different fuzzy measures with generalizations of the Choquet integral in classification problems , 2019, 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[22]  Humberto Bustince,et al.  Fuzzy Rule-Based Classification Systems for multi-class problems using binary decomposition strategies: On the influence of n-dimensional overlap functions in the Fuzzy Reasoning Method , 2016, Inf. Sci..

[23]  Matthias Rauterberg,et al.  Handbook of Digital Games and Entertainment Technologies , 2016 .

[24]  Andrzej Cichocki,et al.  Correlation-based channel selection and regularized feature optimization for MI-based BCI , 2019, Neural Networks.

[25]  Chin-Teng Lin,et al.  A Wireless Multifunctional SSVEP-Based Brain–Computer Interface Assistive System , 2019, IEEE Transactions on Cognitive and Developmental Systems.

[26]  G. Pfurtscheller,et al.  Critical Decision-Speed and Information Transfer in the “Graz Brain–Computer Interface” , 2003, Applied psychophysiology and biofeedback.

[27]  Ugur Halici,et al.  A novel deep learning approach for classification of EEG motor imagery signals , 2017, Journal of neural engineering.

[28]  Ad Aertsen,et al.  Review of the BCI Competition IV , 2012, Front. Neurosci..

[29]  Andrea Petracca,et al.  A Classification Algorithm for Electroencephalography Signals by Self-Induced Emotional Stimuli , 2016, IEEE Transactions on Cybernetics.

[30]  M. Teplan.  FUNDAMENTALS OF EEG MEASUREMENT , 2002 .

[31]  Martin Luessi,et al.  MEG and EEG data analysis with MNE-Python , 2013, Front. Neuroinform..

[32]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[33]  Febo Cincotti,et al.  Human Movement-Related Potentials vs Desynchronization of EEG Alpha Rhythm: A High-Resolution EEG Study , 1999, NeuroImage.

[34]  M. Akin,et al.  Comparison of Wavelet Transform and FFT Methods in the Analysis of EEG Signals , 2002, Journal of Medical Systems.

[35]  Shang-Lin Wu,et al.  Fuzzy Integral With Particle Swarm Optimization for a Motor-Imagery-Based Brain–Computer Interface , 2017, IEEE Transactions on Fuzzy Systems.

[36]  Robert P. W. Duin,et al.  Is independence good for combining classifiers? , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[37]  Andreas Galka,et al.  Topics in Nonlinear Time Series Analysis, with Implications for Eeg Analysis , 2000 .

[38]  Xingyu Wang,et al.  Towards correlation-based time window selection method for motor imagery BCIs , 2018, Neural Networks.

[39]  Jirí Bíla,et al.  Fast fourier transform for feature extraction and neural network for classification of electrocardiogram signals , 2015, 2015 Fourth International Conference on Future Generation Communication Technology (FGCT).

[40]  Clemens Brunner,et al.  Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis , 2007, Pattern Recognit. Lett..

[41]  Chin-Teng Lin,et al.  Generalized CF1F2-integrals: From Choquet-like aggregation to ordered directionally monotone functions , 2020, Fuzzy Sets Syst..

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

[43]  Humberto Bustince,et al.  The state-of-art of the generalizations of the Choquet integral: From aggregation and pre-aggregation to ordered directionally monotone functions , 2020, Inf. Fusion.

[44]  Joshua W Shaevitz,et al.  Whole-brain calcium imaging with cellular resolution in freely behaving Caenorhabditis elegans , 2015, Proceedings of the National Academy of Sciences.

[45]  Tzyy-Ping Jung,et al.  Towards Serious Games for Improved BCI , 2016 .

[46]  Laxmidhar Behera,et al.  Multiclass Fuzzy Time-Delay Common Spatio-Spectral Patterns With Fuzzy Information Theoretic Optimization for EEG-Based Regression Problems in Brain–Computer Interface (BCI) , 2019, IEEE Transactions on Fuzzy Systems.

[47]  José M. Merigó,et al.  The Uncertain Generalized OWA Operator and its Application to Financial Decision Making , 2011, Int. J. Inf. Technol. Decis. Mak..

[48]  Michel Grabisch,et al.  Application of the Choquet integral in multicriteria decision making , 2000 .

[49]  Reinhold Scherer,et al.  Study of discriminant analysis applied to motor imagery bipolar data , 2006, Medical & Biological Engineering & Computing.

[50]  Humberto Bustince,et al.  CF-integrals: A new family of pre-aggregation functions with application to fuzzy rule-based classification systems , 2018, Inf. Sci..

[51]  Paul D. Gader,et al.  Generalized Choquet fuzzy integral fusion , 2002, Inf. Fusion.

[52]  M. Murugappan,et al.  Wireless EEG signals based Neuromarketing system using Fast Fourier Transform (FFT) , 2014, 2014 IEEE 10th International Colloquium on Signal Processing and its Applications.

[53]  Michio Sugeno,et al.  Fuzzy t -conorm integral with respect to fuzzy measures: generalization of Sugeno integral and choquet integral , 1991 .

[54]  Howida A. Shedeed,et al.  A CSP\AM-BA-SVM Approach for Motor Imagery BCI System , 2018, IEEE Access.

[55]  Zhaohong Deng,et al.  Generalized Hidden-Mapping Transductive Transfer Learning for Recognition of Epileptic Electroencephalogram Signals , 2019, IEEE Transactions on Cybernetics.

[56]  菅野 道夫,et al.  Theory of fuzzy integrals and its applications , 1975 .

[57]  L. D. Miguel,et al.  An algorithm for group decision making using n-dimensional fuzzy sets, admissible orders and OWA operators , 2017, Information Fusion.

[58]  Humberto Bustince,et al.  A Practical Guide to Averaging Functions , 2015, Studies in Fuzziness and Soft Computing.

[59]  Behzad Mozaffari Tazehkand,et al.  A New Self-Regulated Neuro-Fuzzy Framework for Classification of EEG Signals in Motor Imagery BCI , 2018, IEEE Transactions on Fuzzy Systems.

[60]  M. Gupta,et al.  Theory of T -norms and fuzzy inference methods , 1991 .

[61]  G Pfurtscheller,et al.  Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[62]  Wei Wu,et al.  Spatiotemporal-Filtering-Based Channel Selection for Single-Trial EEG Classification , 2020, IEEE Transactions on Cybernetics.

[63]  Iain Murray.  Introduction To Gaussian Processes , 2008 .

[64]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .