Effects of local and global spatial patterns in EEG motor-imagery classification using convolutional neural network

References

• Ramadan RA , Vasilakos AV. Brain computer interface: control signals review. Neurocomputing. 2017;223:26–44.
   Web of Science ®Google Scholar
• Riaz F , Hassan A , Rehman S , et al. EMD-based temporal and spectral features for the classification of EEG signals using supervised learning. IEEE Trans Neural Syst Rehabil Eng. 2016;24(1):28–35.
   PubMed Web of Science ®Google Scholar
• Kevric J , Subasi A . Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system. Biomed Signal Process Control. 2017;31:398–406.
   Web of Science ®Google Scholar
• Gaur P , Pachori RB , Wang H , et al. A multi-class EEG-based BCI classification using multivariate empirical mode decomposition based filtering and Riemannian geometry. Expert Syst Appl. 2018;95:201–211.
   Web of Science ®Google Scholar
• Dutta S , Singh M , Kumar A . Classification of non-motor cognitive task in EEG based brain-computer interface using phase space features in multivariate empirical mode decomposition domain. Biomed Signal Process Control. 2018;39:378–389.
   Web of Science ®Google Scholar
• Ang KK , Chin ZY , Wang C , et al. Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci. 2012;6:39.
   PubMed Web of Science ®Google Scholar
• Ramoser H , Muller-Gerking J , Pfurtscheller G . Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Neural Syst Rehabil Eng. 2000;8(4):441–446.
   Google Scholar
• Zhang Y , Zhou G , Jin J , et al. Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface. J Neurosci Methods. 2015;255:85–91.
   PubMed Web of Science ®Google Scholar
• Wu W , Chen Z , Gao X , et al. Probabilistic common spatial patterns for multichannel EEG analysis. IEEE Trans Pattern Anal Mach Intell. 2015;37(3):639–653.
   PubMed Web of Science ®Google Scholar
• Kumar S , Mamun K , Sharma A . CSP-TSM: optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI. Comput Biol Med. 2017;91:231–242.
   PubMed Web of Science ®Google Scholar
• Lotte F , Bougrain L , Cichocki A , et al. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update (in eng). J Neural Eng. 2018 June;15(3):031005.
   PubMed Web of Science ®Google Scholar
• Kang H , Nam Y , Choi S . Composite common spatial pattern for subject-to-subject transfer. IEEE Signal Process Lett. 2009;16(8):683–686.
   Web of Science ®Google Scholar
• Arvaneh M , Guan C , Ang KK , et al. EEG data space adaptation to reduce intersession nonstationarity in brain-computer interface. Neural Comput. 2014;25(8):2146–2171.
   Web of Science ®Google Scholar
• Lotte F , Guan C . Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms (in eng). IEEE Trans Biomed Eng. 2011 Feb;58(2):355–362.
   PubMed Web of Science ®Google Scholar
• Yger F , Berar M , Lotte F . Riemannian approaches in brain-computer interfaces: a review (in eng). IEEE Trans Neural Syst Rehabil Eng. 2017 Nov;25(10):1753–1762.
   PubMed Web of Science ®Google Scholar
• Zanini P , Congedo M , Jutten C , et al. Transfer learning: a riemannian geometry framework with applications to brain-computer interfaces (in eng). IEEE Trans Biomed Eng. 2018 May;65(5):1107–1116.
   PubMed Web of Science ®Google Scholar
• Congedo M , Barachant A , Bhatia R . Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain-Comput Interfaces. 2017;4(3):155–174.
   Web of Science ®Google Scholar
• Barachant A , Bonnet S , Congedo M , et al. Multi-class brain computer interface classification by Riemannian geometry. IEEE Trans Biomed Eng. 2012;59(4):920–928.
   PubMed Web of Science ®Google Scholar
• Barachant A , Bonnet S , Congedo M , et al. Classification of covariance matrices using a Riemannian-based kernel for BCI applications. Neurocomputing. 2013;112:172–178.
   Web of Science ®Google Scholar
• Sakhavi S , Guan C , Yan S . Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Trans Neural Netw Learn Syst. 2018;29(11):5619–5629.
   PubMed Web of Science ®Google Scholar
• Tan C , Sun F , Zhang W , et al. Multimodal classification with deep convolutional-recurrent neural networks for electroencephalography. In International Conference on Neural Information Processing, Guangzhou, China; Springer; 2017. p. 767–776.
   Google Scholar
• Schirrmeister RT , Springenberg JT , Fiederer LDJ , et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp. 2017;38(11):5391–5420.
   PubMed Web of Science ®Google Scholar
• Bashivan P , Rish I , Yeasin M , et al. Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv preprint arXiv:1511.06448. 2015.
   Google Scholar
• Sakhavi S , Guan C , Yan S . Parallel convolutional-linear neural network for motor imagery classification. In Signal Processing Conference (EUSIPCO), 2015 23rd European, Nice, France; IEEE; 2015. p. 2736–2740.
   Google Scholar
• Lawhern VJ , Solon AJ , Waytowich NR , et al. Eegnet: A compact convolutional network for EEG-based brain-computer interfaces. arXiv preprint arXiv:1611.08024. 2016.
   Google Scholar
• Zhang D , Yao L , Zhang X , et al. EEG-based intention recognition from spatio-temporal representations via cascade and parallel convolutional recurrent neural networks. arXiv preprint arXiv:1708.06578. 2017.
   Google Scholar
• Sakhavi S . Application of deep learning methods in brain-computer interface systems. Singapore: Doctor of Philosophy, Department of Electrical and Computer Engineering, National University of Singapore; 2017.
   Google Scholar
• Zhang X , Yao L , Wang X , et al. Know your mind: adaptive brain signal classification with reinforced attentive convolutional neural networks. arXiv preprint arXiv:1802.03996. 2018.
   Google Scholar
• Nunez PL , Srinivasan R . Electric fields of the brain: the neurophysics of EEG. USA: Oxford University Press; 2006.
   Google Scholar
• Snyder JP . Map projections–A working manual. USA: US Government Printing Office; 1987.
   Google Scholar
• Tangermann M , Müller K-R , Aertsen A , et al. Review of the BCI competition IV,”. Front Neurosci. 2012;6:55.
   PubMed Web of Science ®Google Scholar
• Newson JJ , Thiagarajan TC . EEG frequency bands in psychiatric disorders: a review of resting state studies. Front Hum Neurosci. 2019;12:521.
   PubMed Web of Science ®Google Scholar
• Szegedy C , Liu W , Jia Y , et al. Going deeper with convolutions. In Cvpr, Boston, MA, USA; 2015.
   Google Scholar
• Zhao Y , Yao S , Hu S , et al. On the improvement of classifying EEG recordings using neural networks. In 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA; IEEE; 2017. p. 1709–1711.
   Google Scholar
• Ioffe S , Szegedy C . Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167. 2015.
   Google Scholar
• Srivastava N , Hinton G , Krizhevsky A , et al. Dropout: A simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15(1):1929–1958.
   Google Scholar
• Kingma DP , Ba J . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 2014.
   Google Scholar
• Rosenthal R . Meta-analytic procedures for social science research Sage publications: Beverly Hills, 1984, 148 pp. Educ Res. 1986;15(8):18–20.
   Google Scholar