An improved MI recognition by localising feature extraction in both frequency and time domains

References

• Abdulkader SN, Ayman A, Mostafa-Sami MM. 2015. Brain computer interfacing: applications and challenges. Egypt Inf J. 16(2):213–230. doi:10.1016/j.eij.2015.06.002.
   Google Scholar
• Ai Q, Liu Q, Meng W, Xie SQ. 2018. Advanced rehabilitative technology: neural interfaces and devices. Cambridge, Massachusetts, United States: Academic Press. ISBN:978-0-12-814597-5. https://doi.org/10.1016/C2017-0-01714-7
   Google Scholar
• Alomari MH, Awada EA, Samaha A, Alkamha K. 2014. Wavelet based feature extraction for the analysis of EEG signals associated with imagined fists and feet movements. CIS. 7(2):17–27. doi:10.5539/cis.v7n2p17.
   Google Scholar
• Ang KK, Chin ZY, Guan C, Zhang G 2008. Filter bank common spatial pattern (FBCSP) in brain-computer interface. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), Hong Kong, China. IEEE. p. 2390–2397. doi:10.1109/IJCNN.2008.4634130.
   Google Scholar
• Blankertz B, Dornhege G, Krauledat M, Müller KR, Curio G. 2007. The non-invasive Berlin brain–computer interface: fast acquisition of effective performance in untrained subjects. NeuroImage. 37(2):539–550. doi:10.1016/j.neuroimage.2007.01.051.
   Google Scholar
• Brunner C, Leeb R, Müller-Putz G, Schlögl A, Pfurtscheller G. 2008. BCI Competition 2008–graz data set a. Austria: Institute for Knowledge Discovery (Laboratory of Brain-Computer Interfaces), Graz University of Technology: 16,2008. p. 1–6.
   Google Scholar
• Chaudhari R, Galiyawala HJ. 2017. A review on motor imagery signal classification for BCI. Signal Process. 2017(11):16–34.
   Google Scholar
• Comon P. 1994. Independent component analysis, a new concept? Signal Process. 36(3):287–314. doi:10.1016/0165-1684(94)90029-9.
   Google Scholar
• Grosse-Wentrup M, Buss M. 2008. Multiclass common spatial patterns and information theoretic feature extraction. IEEE Trans Biomed Eng. 55(8):1991–2000. doi:10.1109/TBME.2008.921154.
   Google Scholar
• Hamedi M, Salleh SH, Noor AM, Mohammad-Rezazadeh I 2014. Neural network-based three-class motor imagery classification using time-domain features for BCI applications. In 2014 IEEE region 10 symposium, Kuala Lumpur, Malaysia. IEEE. p. 204–207.
   Google Scholar
• Hsu WY, Sun YN. 2009. EEG-based motor imagery analysis using weighted wavelet transform features. J Neurosci Methods. 176(2):310–318. doi:10.1016/j.jneumeth.2008.09.014.
   Google Scholar
• Hu J, Xiao D, Mu Z. 2009. Application of energy entropy in motor imagery EEG classification. International Journal of Digital Content Technology and Its Applications. 3(2):83–90. doi:10.4156/jdcta.vol3.issue2.hu.
   Google Scholar
• Jiang J, Wang C, Wu J, Qin W, Xu M, Yin E. 2020. Temporal combination pattern optimization based on feature selection method for motor imagery BCIs. Front Hum Neurosci. 14:231. doi:10.3389/fnhum.2020.00231.
   Google Scholar
• Jin J, Miao Y, Daly I, Zuo C, Hu D, Cichocki A. 2019. Correlation-based channel selection and regularized feature optimization for MI-based BCI. Neural Networks. 118:262–270. doi:10.1016/j.neunet.2019.07.008.
   Google Scholar
• Jin J, Xiao R, Daly I, Miao Y, Wang X, Cichocki A. 2021. Internal feature selection method of CSP based on L1-norm and Dempster– Shafer theory. IEEE Trans Neural Netw Learn Syst. 32(11): 4814–482. doi:10.1109/TNNLS.2020.3015505.
   Google Scholar
• Jolliffe I. 2011. Principal component analysis. In: Lovric M, editor. International encyclopedia of statistical science. Berlin: Springer. p. 1094–1096.
   Google Scholar
• Kirar JS, Agrawal RK. 2018. Relevant feature selection from a combination of spectral-temporal and spatial features for classification of motor imagery EEG. J Med Syst. 42(5):78. doi:10.1007/s10916-018-0931-8.
   Google Scholar
• Kumar JS, Bhuvaneswari P. 2012. Analysis of Electroencephalography (EEG) signals and its categorization–a study. Procedia Engineering. 38:2525–2536. doi:10.1016/j.proeng.2012.06.298.
   Google Scholar
• Kumar S, Sharma A, Tsunoda T 2019. Subject-specific-frequency-band for motor imagery EEG signal recognition based on common spatial spectral pattern. In Pacific Rim International Conference on Artificial Intelligence (pp. 712–722). Springer, Cham.
   Google Scholar
• Li M, Luo X, Yang J, Sun Y. 2016. Applying a locally linear embedding algorithm for feature extraction and visualization of MI-EEG. Journal of Sensors. 2016:1–9. doi:10.1155/2016/7481946.
   Google Scholar
• Liu A, Chen K, Liu Q, Ai Q, Xie Y, Chen A. 2017. Feature selection for motor imagery EEG classification based on firefly algorithm and learning automata. Sensors. 17(11):2576. doi:10.3390/s17112576.
   Google Scholar
• Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, Yger F. 2018. A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J Neural Eng. 15(3):031005. doi:10.1088/1741-2552/aab2f2.
   Google Scholar
• Lotte F, Congedo M, Lécuyer A, Arnaldi B, Arnaldi B. 2007. A review of classification algorithms for EEG-based brain–computer interfaces. J Neural Eng. 4(2):R1. doi:10.1088/1741-2560/4/2/R01.
   Google Scholar
• McFarland DJ, Wolpaw JR. 2011. Brain-computer interfaces for communication and control. Commun ACM. 54(5):60–66. doi:10.1145/1941487.1941506.
   Google Scholar
• Nicolas-Alonso LF, Gomez-Gil J. 2012. Brain computer interfaces, a review. Sensors. 12(2):1211–1279. doi:10.3390/s120201211.
   Google Scholar
• Oikonomou VP, Georgiadis K, Liaros G, Nikolopoulos S, Kompatsiaris I 2017. A comparison study on EEG signal processing techniques using motor imagery EEG data. In 2017 IEEE 30th international symposium on computer-based medical systems (CBMS), Thermi-Thessaloniki, Greece. IEEE. p. 781–786.
   Google Scholar
• Padfield N, Zabalza J, Zhao H, Masero V, Ren J. 2019. EEG-based brain-computer interfaces using motor-imagery: techniques and challenges. Sensors. 19(6):1423. doi:10.3390/s19061423.
   Google Scholar
• Pei Y, Luo Z, Yan Y, Yan H, Jiang J, Li W, Xie L, Yin E. 2021. Data augmentation: using channel-level recombination to improve classification performance for motor imagery EEG. Front Hum Neurosci. 15:645952. doi:10.3389/fnhum.2021.645952.
   Google Scholar
• Pfurtscheller G. 1977. Graphical display and statistical evaluation of event-related desynchronization (ERD). Electroencephalogr Clin Neurophysiol. 43(5):757–760. doi:10.1016/0013-4694(77)90092-X.
   Google Scholar
• Pineda JA. 2005. The functional significance of mu rhythms: translating “seeing” and “hearing” into “doing”. Brain Res Rev. 50(1):57–68. doi:10.1016/j.brainresrev.2005.04.005.
   Google Scholar
• Rahman MKM, Haque T. 2021. Classification of motor imagery using a time-localised approach. Journal of Medical Engineering & Technology. 45(5):361–374. doi:10.1080/03091902.2021.1906966.
   Google Scholar
• Rahman MKM, Joadder MAM. 2017. A review on the components of EEG-based motor imagery classification with quantitative comparison. Appl Theory Comput Technol. 2(2):1–15. doi:10.22496/atct20170122133.
   Google Scholar
• Rahman MKM, Joadder MAM. 2020. A space-frequency localized approach of spatial filtering for motor imagery classification. Health Information Science and Systems. 8(1):1–8. doi:10.1007/s13755-020-00106-8.
   Google Scholar
• Sanei S, Chambers JA. 2013. EEG signal processing. The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England: John Wiley & Sons.
   Google Scholar
• Scherer R, Vidaurre C. 2018. Motor imagery based brain–computer interfaces. In Pablo D, editors. Smart wheelchairs and brain-computer interfaces. Cambridge, Massachusetts, United States: Academic Press. p. 171–195.
   Google Scholar
• Shahtalebi S, Mohammadi A 2018. A Bayesian framework to optimize double band spectra spatial filters for motor imagery classification. In 2018 ieee international conference on acoustics, speech and signal processing (icassp), Calgary, AB, Canada. IEEE. p. 871–875.
   Google Scholar
• Teplan M. 2002. Fundamentals of EEG measurement. Measurement Science Review. 2(2):1–11.
   Google Scholar
• Wierzgała P, Zapała D, Wojcik GM, Masiak J. 2018. Most popular signal processing methods in motor-imagery BCI: a review and meta-analysis. Front Neuroinform. 12:78. doi:10.3389/fninf.2018.00078.
   Google Scholar
• Zhang H, Guan C, Ang KK, Wang C, Chin ZY. 2012. BCI competition IV–data set I: learning discriminative patterns for self-paced EEG-based motor imagery detection. Front Neurosci. 6:7. doi:10.3389/fnins.2012.00007.
   Google Scholar
• Zhang X, Yao L, Wang X, Monaghan J, Mcalpine D, Zhang Y. 2019. A survey on deep learning based brain computer interface: recent advances and new frontiers. Journal of neural engineering. 18(3): 031002. doi:10.1088/1741-2552/abc902.
   Google Scholar