A performance based feature selection technique for subject independent MI based BCI
- 22 Downloads
Significant research has been conducted in the field of brain computer interface (BCI) algorithm development, however, many of the resulting algorithms are both complex, and specific to a particular user as the most successful methodology can vary between individuals and sessions. The objective of this study was to develop a simple yet effective method of feature selection to improve the accuracy of a subject independent BCI algorithm and streamline the process of BCI algorithm development. Over the past several years, several high precision features have been suggested by researchers to classify different motor imagery tasks. This research applies fourteen of these features as a feature pool that can be used as a reference for future researchers. Additionally, we look for the most efficient feature or feature set with four different classifiers that best differentiates several motor imagery tasks. In this work we have successfully employed a feature fusion method to obtain the best sub-set of features. We have proposed a novel computer aided feature selection method to determine the best set of features for distinguishing between motor imagery tasks in lieu of the manual feature selection that has been performed in past studies. The features selected by this method were then fed into a Linear Discriminant Analysis, K-nearest neighbor, decision tree, or support vector machine classifier for classification to determine the overall performance.
The methods used were a novel performance based additive feature fusion algorithm working in conjunction with machine learning in order to classify the motor imagery signals into particular states. The data used for this study was collected from BCI competition III dataset IVa.
The result of this algorithm was a classification accuracy of 99% for a subject independent algorithm with less computation cost compared to traditional methods, in addition to multiple feature/classifier combinations that outperform current subject independent methods.
The conclusion of this study and its significance is that it developed a viable methodology for simple, efficient feature selection and BCI algorithm development, which leads to an overall increase in algorithm classification accuracy.
KeywordsMachine learning Brain computer interfaces Motor imagery Subject independent BCI Biomedical signal processing Electroencephalography
The continued support of the University of Wisconsin-Milwaukee and the University of Wisconsin-Milwaukee College of Engineering and Applied Science, as well as the continued support of our mentors and loved ones throughout our work.
- 2.Zhou J, et al. Classification of motor imagery EEG using wavelet envelope analysis and LSTM networks. In: 2018 Chinese Control and Decision Conference (CCDC). IEEE, 2018.Google Scholar
- 4.Cantillo-Negrete J, et al. An approach to improve the performance of subject-independent BCIs-based on motor imagery allocating subjects by gender. Biomed Eng. 2014;13(1):158.Google Scholar
- 6.Saha S, Mamun KA, Ahmed K, Mostafa R, et al. Progress in brain computer interfaces: challenges and trends. arXiv:1901.03442v1 [cs.HC}, 2019.Google Scholar
- 22.Siuly S, Li Y, Zhang Y. EEG Signal Analysis and Classification. IEEE Trans Neural Syst Rehabilit Eng. 2016;11:141–4.Google Scholar
- 26.Shan H, et al. EEG-based motor imagery classification accuracy improves with gradually increased channel number. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) IEEE, 2012.Google Scholar
- 27.Su Y, Li Y, Wang S. Filter ensemble regularized common spatial pattern for EEG classification. In: Seventh International Conference on Digital Image Processing (ICDIP 2015). Vol 9631. International Society for Optics and Photonics, 2015.Google Scholar
- 29.Baziyad AG, Djemal R. A study and performance analysis of three paradigms of wavelet coefficients combinations in three-class motor imagery based BCI. In: 2014 5th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), IEEE, 2014.Google Scholar
- 34.Siuly S, Yan L, Yanchun Z. Cross-correlation aided logistic regression model for the identification of motor imagery EEG signals in BCI applications. EEG signal analysis and classification. Cham: Springer; 2016. p. 153–72.Google Scholar
- 37.Meng J, et al. Automated selecting subset of channels based on CSP in motor imagery brain-computer interface system. In: 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO) IEEE, 2009.Google Scholar
- 40.Resalat SN, Valiallah S. A study of various feature extraction methods on a motor imagery based brain computer interface system. Basic Clin Neurosci. 2016;7(1):13.Google Scholar
- 41.Cantillo-Negrete J, et al. An approach to improve the performance of subject-independent BCIs-based on motor imagery allocating subjects by gender. Biomed Eng. 2014;13(1):158.Google Scholar
- 44.Tolić M, Jović F. Classification of wavelet transformed EEG signals with neural network for imagined mental and motor tasks. Int J Fundam Appl Kinesiol. 2013;45(1):130–8.Google Scholar
- 45.Hu J, Xiao D, Mu Z. Application of energy entropy in motor imagery EEG classification. JDCTA. 2009;3(2):83–90.Google Scholar
- 46.Abdul-Latif AA, et al. Power changes of EEG signals associated with muscle fatigue: the root mean square analysis of EEG bands. In: Proceedings of the 2004, Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004. IEEE, 2004.Google Scholar
- 50.Mathworks®, MATLAB™ R2017b, Natick, Massachusetts 2017.Google Scholar
- 51.Myszewski J, Reina T, Bergendahl E, Rahman M, Development of a classification algorithm for bicep flexion from multi-subject EEG data. In: Proceedings of the Biomedical Engineering Society 2018 Meeting, Oct 2018, Atlanta, Georgia [Online]. Available: http://submissions.mirasmart.com/BMESArchive.Accessed 3 Feb 2019.
- 54.Ye J, et al. Feature reduction via generalized uncorrelated linear discriminant analysis. IEEE Trans Knowl Data Eng. 2006;10:1312–22.Google Scholar
- 60.Prashant G. Decision trees in machine learning. 2017. Available online: https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052. Accessed 05 May 2019).
- 61.Hatamikia S, Nasrabadi AM. Subject independent BCI based on LTCCSP method and GA wrapper optimization. In: IEEE 22nd Iranian Conference on Biomedical Engineering (ICBME), 2015.Google Scholar
- 63.Lotte F, Cuntai G, Ang KK. Comparison of designs towards a subject-independent brain-computer interface based on motor imagery. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009.Google Scholar