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Innovations

Classification of motor imagery using a time-localised approach

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Pages 361-374 | Received 15 Nov 2020, Accepted 18 Mar 2021, Published online: 13 Apr 2021

References

  • McFarland DJ, Wolpaw JR. Brain-computer interfaces for communication and control. Commun Acm. 2011;54:60–66.
  • Abdulkader SN, Atia A, Sami M, et al. Brain computer interfacing: applications and challenges. Egypt Inform J. 2015;16:213–230.
  • Nicolas-Alonso LF, Gomez-Gil J. Brain computer interfaces, a review. Sensors. 2012;12:1211–1279.
  • Wierzgała P, Zapała D, Wojcik GM, et al. Most Popular signal processing methods in motor-imagery BCI: a review and meta-analysis. Front Neuroinform. 2018;12:78.
  • Dornhege G. Toward brain-computer interfacing. Cambridge (MA): MIT Press; 2007.
  • Rahman MKM, Joadder MAM. A space-frequency localized approach of spatial filtering for motor imagery classification. Health Inf Sci Syst. 2020;8:15.
  • Bajaj V, Taran S, Sengur A. Emotion classification using flexible analytic wavelet transform for electroencephalogram signals. Health Inf Sci Syst. 2018;6:12.
  • Rahman MKM, Joadder MAM, Ashique TA. Seizure detection system: a comparative study on features and fusions. Proceedings of International Conference on Medical Engineering, Health Informatics and Technology (MediTec); 2016.
  • Sanei S, Chambers JA. EEG signal processing. Kindle Edition. Chichester (UK): Wiley-Interscience; 2013.
  • Ai Q, Liu Q, Meng W, et al. Advanced rehabilitative technology, 1st ed. London: Academic Press; 2018.
  • Scherer R, Vidaurre C. Motor imagery based brain-computer interfaces. In: Diez P, editor. Smart wheelchairs and brain-computer interfaces. London (UK): Academic Press; 2018. p. 171–195.
  • Chaudhari R, Galiyawala HJ. A review on motor imagery signal classification for BCI. Signal Process. 2017;11:16–34.
  • Padfield N, Zabalza J, Zhao H, et al. EEG-based brain-computer interfaces using motor-imagery: techniques and challenges. Sensors. 2019;19:1423.
  • Rahman MKM, Joadder MAM. A review on the components of EEG-based motor imagery classification with quantitative comparison. Appl Theory Comput Technol. 2017;2:1–15.
  • Kumar S, Sharma A, Tsunoda T. Subject-specific-frequency-band for motor imagery EEG signal recognition based on common spatial spectral pattern. In: Nayak A, Sharma A, editors. PRICAI 2019: trends in artificial intelligence. PRICAI 2019. Cham: Springer; 2019. (Lecture notes in computer science; 11671).
  • Shahtaleb S, Mohammadi A. A Bayesian framework to optimize double band spectra-spatial filters for motor imagery classification. Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP); 2018.
  • Schalk G, Mellinger J. A practical guide to brain-computer interfacing with BCI2000. London (UK): Springer; 2010.
  • Jiang X, Bian GB, Tian Z. Removal of artifacts from EEG signals: a review. Sensors. 2019;19:987.
  • Comon P. Independent component analysis, a new concept? Signal Process. 1994;36:287–314.
  • Jolliffe I. Principal component analysis. In: Lovric M, editors. International encyclopedia of statistical science. Berlin: Springer; 2011.
  • Higashi H, Tanaka T. Common spatio-time-frequency patterns for motor imagery-based brain machine interfaces. Comput Intell Neurosci. 2013;2013:537218.
  • Grosse-Wentrup M, Buss M. Multiclass common spatial patterns and information theoretic feature extraction. IEEE Trans Biomed Eng. 2008; 55:1991–2000.
  • Lotte F, Guan C. Spatially regularized common spatial patterns for EEG classification. Proceedings of the 20th International Conference on Pattern Recognition; 2010 Aug 23–26; Istanbul, Turkey.
  • Kim Y, Ryu J, Kim KK, et al. Motor imagery classification using mu and beta rhythms of EEG with strong uncorrelating transform based complex common spatial patterns. Comput Intell Neurosci. 2016;2016:1489692.
  • 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.
  • Dornhege G, Blankertz B, Krauledat M, et al. Combined optimization of spatial and temporal filters for improving brain-computer interfacing. IEEE Trans Biomed Eng. 2006;53:2274–2281.
  • Kirar JS, Agrawal RK. Relevant feature selection from a combination of spectral-temporal and spatial features for classification of motor imagery EEG. J Med Syst. 2018;42:78.
  • Rahman MKM, Bhuiyan MOS, Joadder MAM. Progressive fusion of feature sets for optimal classification of MI signal. Int J Biomed Eng Technol. https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijbet
  • Hamedi M, Salleh SH, Noor AM, et al. Neural network-based three-class motor imagery. Proceedings of the 2014 IEEE region 10 symposium; Kuala Lumpur, Malaysia; 2014. p. 14–16.
  • 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.
  • Rodríguez-Bermúdez G, García-Laencina P. Automatic and adaptive classification of electroencephalographic signals for brain computer interfaces. J Med Syst. 2012;36:51–63.
  • Yang B, Zhang A. Power spectral entropy analysis of EEG signal based-on BCI. Proceedings of the 32nd Chinese Control Conference; Xi’an, China; 2013. p. 26–28.
  • Liu A, Chen K, Liu Q, et al. Feature selection for motor imagery EEG classification based on firefly algorithm and learning automata. Sensors. 2017;17:2576.
  • Oikonomou VP, Georgiadis K, Liaros G, et al. A Comparison Study on EEG Signal Processing Techniques Using Motor Imagery EEG Data. Proceedings of the IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS); Thessaloniki, Greece; 2017. p. 22–24.
  • Hsu WY, Sun YN. EEG-based motor imagery analysis using weighted wavelet transform features. J Neurosci Methods. 2009;176:310–318.
  • Li M, Luo X, Yang J, et al. Applying a locally linear embedding algorithm for feature extraction and visualization of MI-EEG. J Sens. 2016;2016:1–9.
  • Alomari MH, Awada EA, Samaha A, et al. Wavelet-based feature extraction for the analysis of EEG signals associated with imagined fists and feet movements. CIS. 2014;7:17–27.
  • Hu J, Xiao D, Mu Z. Application of energy entropy in motor imagery EEG classification. Application of Energy Entropy in Motor Imagery EEG Classification. 2009;3:83–90.
  • Jiao Y, Zhou T, Yao L, et al. Multi-view multi-scale optimization of feature representation for EEG classification improvement. IEEE Trans Neural Syst Rehabil Eng. 2020;28:2589–2597.
  • Zhang Y, Zhou T, Wu W, et al. Improving EEG decoding via clustering-based multitask feature learning. IEEE Trans Neural Netw Learn Syst; 2021.
  • Kumar JS, Bhuvaneswari P. Analysis of electroencephalography (EEG) signals and its categorization – a study. Procedia Eng. 2012;38:2525–2536.
  • Lotte F, Congedo M, Lécuyer A, et al. A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng. 2007;4:R1–R13.
  • Shin Y, Lee S, Lee J, et al. Sparse representation-based classification scheme for motor imagery-based brain-computer interface systems. J Neural Eng. 2012;9:056002.
  • Jiao Y, Zhang Y, Chen X, et al. Sparse group representation model for motor imagery EEG classification. IEEE J Biomed Health Inform. 2019;23:631–641.
  • Zhang X, Yao L, Wang X, et al. A survey on deep learning based brain computer interface: recent advances and new frontiers. J Neural Eng. 2021;18:031002.
  • Lee HK, Choi YS. A convolution neural networks scheme for classification of motor imagery EEG based on wavelet time-frequency image. Proceedings of the International Conference on Information Networking; 2018 Jan 10–12; Chiang Mai, Thailand. p. 906–909.
  • Tang Z, Li C, Sun S. Single-trial EEG classification of motor imagery using deep convolutional neural networks. Optik Int J Light Electron Optics. 2017;130:11–18.
  • Geethanjali P, Mohan YK, Sen J. Time domain feature extraction and classification of EEG data for Brain Computer Interface. Proceedings of 9th International Conference on Fuzzy Systems and Knowledge Discovery; 2012; Sichuan. p. 1136–1139.
  • Jaime AP. The functional significance of mu rhythms: translating “seeing” and “hearing” into “doing. Brain Res Rev. 2005;50:57–68.
  • Uyulan C, Erguzel TT. Analysis of time – frequency EEG feature extraction methods for mental task classification. IJCIS. 2017;10:1280–1288.
  • Belwafi K, Djemal R, Ghaffari F, et al. An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery. Proceedings of the IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB); 2014 Dec 9–12; Orlando, FL. p. 121–126.
  • Yuksel A, Olmez T. A neural network-based optimal spatial filter design method for motor imagery classification. PLoS One. 2015;10:e0125039.
  • Teplan M. Fundamental of EEG measurement. Measur Sci Rev. 2002;2:1–11.
  • Duda RO, Hart PE, Stork DG. Pattern recognition. 2nd ed. New York (NY): Wiley Inter-Science; 2000.
  • Fukunaga K. Introduction to statistical pattern recognition. 2nd ed. Academic Press Professional, Inc., San Francisco (CA); 1990.
  • Burges CJC. A tutorial on support vector machines for pattern recognition. Data Mining Knowl Discov. 1998;2:121–167.
  • Bennett KP, Campbell C. Support vector machines: hype or hallelujah? SIGKDD Explor Newsl. 2000;2:1–13.
  • Bishop CM. Neural networks for pattern recognition. New York (NY): Oxford University Press; 1995.
  • Haselsteiner E, Pfurtscheller G. Using time-dependent neural networks for EEG classification. IEEE Trans Rehabil Eng. 2000;8:457–463.
  • Mill´An JR, Mourino J. Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project. IEEE Trans Rehabil Eng. 2003;11:159–169.
  • Zhang H, Guan C, Ang KK, et al. BCI competition IV – data set I: learning discriminative patterns for self-paced EEG-based motor imagery detection. Front Neurosci. 2012;6:7.
  • Pfurtscheller G. Graphical display and statistical evaluation of event-related desynchronization (ERD). Electroencephalogr Clin Neurophysiol. 1977;43:757–760.
  • Joadder MAM, Rahman MKM. Classification of motor imagery signal using wavelet decomposition: a study for optimum parameter settings. Proceedings of International Conference on Medical Engineering, Health Informatics and Technology (MediTec); 2016; Dhaka. p. 1–6.

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