Deep learning-based classification for brain-computer interfaces

Brain-computer interface (BCI) is an emerging area of research that aims to improve the quality of human-computer applications. It has enormous scope in biomedical applications, neural rehabilitation, biometric authentication, educational programmes, and entertainment applications. A BCI system has four major components: signal acquisition, signal preprocessing, feature extraction, and classification. In this study, we provide a comparison of various traditional classification algorithms to the newer methods of deep learning. We explore two different types of deep learning methods, namely, convolutional neural networks (CNN) and recurrent neural networks (RNN) with long short-term memory (LSTM) architecture. We test the classification accuracies on a recent 5-dass steady-state visual evoked potential (SSVEP) dataset. The results prove the superiority of deep learning methods in comparison with the traditional classification algorithms. Amongst the traditional classifiers, support vector machine (SVM) with Gaussian kernel employing sequential forward selection (SFS) of features provided a better classification accuracy of 66.09%, while CNN provided the highest classification accuracy of 69.03%.

[1]  Romis de Faissol Attux,et al.  Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs , 2015, Biomed. Signal Process. Control..

[2]  John Thomas,et al.  Automated epileptiform spike detection via affinity propagation-based template matching , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[3]  A. Erfanian,et al.  suppression using recurrent neural network for electro-encephalogram based brain-computer interface , 2005 .

[4]  Dean J Krusienski,et al.  A comparison of classification techniques for the P300 Speller , 2006, Journal of neural engineering.

[5]  John G. Proakis,et al.  Digital Signal Processing: Principles, Algorithms, and Applications , 1992 .

[6]  Trevor Hastie,et al.  Multi-class AdaBoost ∗ , 2009 .

[7]  Marina Schmid,et al.  An Introduction To The Event Related Potential Technique , 2016 .

[8]  N. Birbaumer,et al.  A brain–computer interface (BCI) for the locked-in: comparison of different EEG classifications for the thought translation device , 2003, Clinical Neurophysiology.

[9]  Hubert Cecotti,et al.  Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[11]  A. Prasad Vinod,et al.  Online Biometric Authentication Using Subject-Specific Band Power features of EEG , 2017, ICCSP '17.

[12]  Antonio Frisoli,et al.  A novel BCI-SSVEP based approach for control of walking in Virtual Environment using a Convolutional Neural Network , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[13]  Donald F. Morrison,et al.  Multivariate Analysis, Overview , 2005 .

[14]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[15]  Pedro Ponce,et al.  Alternative Classification Techniques for Brain-Computer Interfaces for Smart Sensor Manufacturing Environments , 2015 .

[16]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[17]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[18]  S. Palazzo,et al.  Deep Learning Human Mind for Automated Visual Classification , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  A. Wayne Whitney,et al.  A Direct Method of Nonparametric Measurement Selection , 1971, IEEE Transactions on Computers.

[20]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[21]  Rajesh Singla,et al.  Comparison of SSVEP Signal Classification Techniques Using SVM and ANN Models for BCI Applications , 2014 .

[22]  Alan V. Oppenheim,et al.  Discrete-time Signal Processing. Vol.2 , 2001 .

[23]  Leif E. Peterson.  K-nearest neighbor , 2009, Scholarpedia.

[24]  Yiannis Kompatsiaris,et al.  Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs , 2016, ArXiv.

[25]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[26]  Charles W. Anderson,et al.  Classification of EEG during imagined mental tasks by forecasting with Elman Recurrent Neural Networks , 2011, The 2011 International Joint Conference on Neural Networks.

[27]  Domenico Campolo,et al.  Role of EMG as a complementary tool for assessment of motor impairment , 2016, 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[28]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[29]  Klaus-Robert Müller,et al.  A convolutional neural network for steady state visual evoked potential classification under ambulatory environment , 2017, PloS one.

[30]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[31]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[32]  Ayman Atia,et al.  Brain computer interfacing: Applications and challenges , 2015 .

[33]  Mohamed Ettaouil,et al.  Multilayer Perceptron: Architecture Optimization and Training , 2016, Int. J. Interact. Multim. Artif. Intell..

[34]  Hubert Cecotti,et al.  Convolutional Neural Network with embedded Fourier Transform for EEG classification , 2008, 2008 19th International Conference on Pattern Recognition.

[35]  A. Prasad Vinod,et al.  A study on the impact of neurofeedback in EEG based attention-driven game , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).