Performance of Dual-Augmented Lagrangian Method and Common Spatial Patterns applied in classification of Motor-Imagery BCI

Motor-imagery based brain-computer interfaces (MI-BCI) have the potential to become ground-breaking technologies for neurorehabilitation, the reestablishment of non-muscular communication and commands for patients suffering from neuronal disorders and disabilities, but also outside of clinical practice, for video game control and other entertainment purposes. However, due to the noisy nature of the used EEG signal, reliable BCI systems require specialized procedures for features optimization and extraction. This paper compares the two approaches, the Common Spatial Patterns with Linear Discriminant Analysis classifier (CSP-LDA), widely used in BCI for extracting features in Motor Imagery (MI) tasks, and the Dual-Augmented Lagrangian (DAL) framework with three different regularization methods: group sparsity with row groups (DAL-GLR), dual-spectrum (DAL-DS) and l1-norm regularization (DAL-L1). The test has been performed on 7 healthy subjects performing 5 BCI-MI sessions each. The preliminary results show that DAL-GLR method outperforms standard CSP-LDA, presenting 6.9% lower misclassification error (p-value = 0.008) and demonstrate the advantage of DAL framework for MI-BCI.

[1]  Kazuyuki Aihara,et al.  Logistic Regression for Single Trial EEG Classification , 2006, NIPS.

[2]  Motoaki Kawanabe,et al.  Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces , 2011, IEEE Transactions on Biomedical Engineering.

[3]  Lucas C. Parra,et al.  Bilinear Discriminant Component Analysis , 2007, J. Mach. Learn. Res..

[4]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces to restore motor function and probe neural circuits , 2003, Nature Reviews Neuroscience.

[5]  Yiannis Kompatsiaris,et al.  A Comparison Study on EEG Signal Processing Techniques Using Motor Imagery EEG Data , 2017, 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS).

[6]  C. G. Lim,et al.  A Brain-Computer Interface Based Attention Training Program for Treating Attention Deficit Hyperactivity Disorder , 2012, PloS one.

[7]  Klaus-Robert Müller,et al.  A regularized discriminative framework for EEG analysis with application to brain–computer interface , 2010, NeuroImage.

[8]  Masashi Sugiyama,et al.  Dual-Augmented Lagrangian Method for Efficient Sparse Reconstruction , 2009, IEEE Signal Processing Letters.

[9]  Agostino Accardo,et al.  Combined and Singular Effects of Action Observation and Motor Imagery Paradigms on Resting-State Sensorimotor Rhythms , 2019, IFMBE Proceedings.

[10]  R. Goebel,et al.  Real-Time Functional Magnetic Resonance Imaging Neurofeedback for Treatment of Parkinson's Disease , 2011, The Journal of Neuroscience.

[11]  Scott Makeig,et al.  BCILAB: a platform for brain–computer interface development , 2013, Journal of neural engineering.

[12]  Cuntai Guan,et al.  A clinical evaluation of non-invasive motor imagery-based brain-computer interface in stroke , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  Cuntai Guan,et al.  Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.

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

[15]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[16]  Agostino Accardo,et al.  Slow Cortical Potential BCI Classification Using Sparse Variational Bayesian Logistic Regression with Automatic Relevance Determination , 2019, IFMBE Proceedings.