Top

Neural Computing and Applications

Published in:

26-09-2022 | Original Article

Generating ten BCI commands using four simple motor imageries and classification by divergence-based DNN

Authors: Nuri Korhan, Tamer Olmez, Zümray Dokur

Published in: Neural Computing and Applications | Issue 2/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The brain computer interface (BCI) systems are utilized for transferring information among humans and computers by analyzing electroencephalogram (EEG) recordings. However, in this emerging research field, the number of commands in the BCI is limited in relation to the number of motor imagery (MI) tasks; in the current literature, mostly two or four commands (classes) are studied. As a solution to this problem, it is recommended to use mental tasks as well as MI tasks. Unfortunately, the use of this approach reduces the classification performance of MI EEG signals. The fMRI analyses show that the resources in the brain associated with the motor imagery can be activated independently. It is assumed that the activity of the brain produced by the MI of the combination of body parts corresponds to the superposition of the activities generated during each body part’s simple MI. In this study, in order to create more than four BCI commands, we suggest to generate combined MI EEG signals artificially by using tongue, feet, left and right hands’ motor imageries in pairs. For the first time in the literature, combined MI EEG signal is artificially generated by using the superposition of simple MI EEG signals from two different sources. We observe in the literature that the classification performances are adversely affected as the number of classes is increased, and the classification performances for the MI with more than four classes are poor. The aim of this study is to increase the BCI commands by generating artificially combined MI EEG signals and to achieve high success rates for ten BCI commands by using a small-sized deep neural network (DNN). In this context, by analyzing the ERD signal of combined MI tasks, we investigate how to generate combined MI signals artificially using the superposition of simple MI signals. The proposed method is validated on real data which consist of simple and combined MI EEG signals, and average classification performance of 81.8% is achieved for ten BCI commands generated from the BCI Competition 3 and 4 datasets.

previous article The application of neural network for software vulnerability detection: a review

next article Distance-based arranging oversampling technique for imbalanced data

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Yang H, Sakhavi S, Ang KK, Guan C (2015) On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification. 37th Annu Inter Confere IEEE Eng Med and Biol Soc. https://doi.org/10.1109/EMBC.2015.7318929CrossRef

Sakhavi S, Guan C, Yan S (2015) Parallel convolutional-linear neural network for motor imagery classification. 23rd Europ Sig Process Conf. https://doi.org/10.1109/EUSIPCO.2015.7362882CrossRef

Lu N, Li T, Ren X, Miao H (2017) A deep learning scheme for motor imagery classification based on restricted Boltzmann machines. IEEE Trans Neural Syst Rehabil Eng 25(6):567–576. https://doi.org/10.1109/TNSRE.2016.2601240CrossRef

Sakhavi S, Guan C, Yan S (2018) Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Transact on Neural Netw Learn Syst 29(11):5619–5629. https://doi.org/10.1109/TNNLS.2018.2789927MathSciNetCrossRef

Abbas W, Khan NA (2018) DeepMI: Deep learning for multiclass motor imagery classification. 40th Annu Inter Conferen IEEE Eng Med Biol Soc. https://doi.org/10.1109/EMBC.2018.8512271CrossRef

Wu YT, Huang TH, Lin YC, et al. (2018) Classification of EEG motor imagery using support vector machine and convolutional neural network. International Automatic Control Conference – CACS. https://doi.org/10.1109/CACS.2018.8606765

Dai M, Zheng D, Na R et al (2019) EEG Classification of motor imagery using a novel deep learning framework. Sensors 19(3):551. https://doi.org/10.3390/s19030551CrossRef

Tabar YR, Halici U (2017) A novel deep learning approach for classification of EEG motor imagery signals. J Neural Eng 14(1):016003. https://doi.org/10.1088/1741−2560/14/1/016003CrossRef

Tang X, Zhao J, Fu W (2019) Research on extraction and classification of EEG features for multi-class motor imagery. IEEE 4th Adv Inform Technol, Electron and Automation Control Conf. https://doi.org/10.1109/IAEAC47372.2019.8998049CrossRef

10.

Chaudhary S, Taran S, Bajaj V, Sengur A (2019) Convolutional neural network based approach towards motor imagery tasks EEG signals classification. IEEE Sens J 19(12):4494–4500. https://doi.org/10.1109/JSEN.2019.2899645CrossRef

11.

Zhao X, Zhang H, Zhu G, You F, Kuang S, Sun L (2019) A multi-branch 3D convolutional neural network for EEG-based motor imagery classification. IEEE Trans Neural Syst Rehabil Eng 27(10):2164–2177. https://doi.org/10.1109/TNSRE.2019.2938295CrossRef

12.

Zhang R, Zong Q, Zhao X (2019) A new convolutional neural network for motor imagery classification. Proceedings of the 38th Chinese Control Conference. https://doi.org/10.23919/ChiCC.2019.8865152

13.

Yüksel A (2017) Classification methods for motor imagery based brain computer interfaces. PhD Dissertations. Istanbul Technical University. Institute of Science and Technology.

14.

Yüksel A, Olmez T (2015) A neural network based optimal spatial filter design method for motor imagery classification. PLOS-ONE 10(5):e0125039. https://doi.org/10.1371/journal.pone.0125039CrossRef

15.

Deng X, Zhang B, Yu n, Liu K and Sun K, (2021) Advanced TSGL-EEGNet for Motor Imagery EEG-Based Brain-Computer Interfaces. IEEE Access 9:25118–25130. https://doi.org/10.1109/ACCESS.2021.3056088CrossRef

16.

Olivas-Padilla BE, Chacon-Murguia MI (2019) Classification of multiple motor imagery using deep convolutional neural networks and spatial filters. Appl Soft Comput 75:461–472. https://doi.org/10.1016/j.asoc.2018.11.031CrossRef

17.

Liu M, Zhou M, Zhang T, Xiong N (2020) Semi-supervised learning quantization algorithm with deep features for motor imagery EEG recognition in smart healthcare application. App Soft Comp 89:106071CrossRef

18.

Echtioui A, Zouch W, Ghorbel M, Mhiri C, Hamam H (2021) Fusion Convolutional Neural Network for Multi-Class Motor Imagery of EEG Signals Classification. International Wire Communicat Mobile Comput (IWCMC). https://doi.org/10.1109/IWCMC51323.2021.9498885CrossRef

19.

Mahamune R, Laskar SH (2021) Classification of the four-class motor imagery signals using continuous wavelet transform filter bank-based two dimensional images. Int J Imaging Syst Technol 31:2237–2248. https://doi.org/10.1002/ima.22593CrossRef

20.

Echtioui A, Zouch W (2021) Multi-class Motor Imagery EEG Classification using Convolution Neural Network. In Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART). https://doi.org/10.5220/0010425905910595CrossRef

21.

Zhao X, Liu D, Ma L, Liu Q, Chen K, Xie S, Ai Q (2022) Deep CNN model based on serial-parallel structure optimization for four-class motor imagery EEG classification. Biomed Signal Process Control 72:103338. https://doi.org/10.1016/j.bspc.2021.103338CrossRef

22.

Xu S, Zhu L, Kong W, Peng Y, Hu H, Cao J (2022) A novel classification method for EEG-based motor imagery with narrow band spatial filters and deep convolutional neural network. Cogn Neurodyn 16:379–389. https://doi.org/10.1007/s11571-021-09721-xCrossRef

23.

Wang L, Wu XP (2008) Classification of four-class motor imagery EEG data using spatial filtering. 2nd International Conference on Bioinformatics and Biomedical Engineering. https://doi.org/10.1109/ICBBE.2008.868

24.

Aljalal M, Djemal R (2017) A comparative study of wavelet and CSP features classified using LDA, SVM and ANN in EEG based motor imagery. 9thIEEE-GCC Conference and Exhibition. https://doi.org/10.1109/IEEEGCC.2017.8448212CrossRef

25.

Mirnaziri M, Rahimi M, Alavikakhaki S, Ebrahimpour R (2013) Using combination of µ, β and γ bands in classification of EEG signals. Basic Clin Neurosci 4(1):76–87

26.

Silva VF, Barbosa RM, Vieira PM, Lima CS (2017) Ensemble learning based classification for BCI applications. IEEE 5th Portuguese Meeting on Bioengineering. https://doi.org/10.1109/ENBENG.2017.7889483CrossRef

27.

Alansari M, Kamel M, Hakim B, Kadah Y (2018) Study of wavelet-based performance enhancement for motor imagery brain-computer interface. 6th Inter Conference Brain-Comput Interface (BCI). https://doi.org/10.1109/IWW-BCI.2018.8311520CrossRef

28.

Behri M, Subasi A, Qaisar SM (2018) Comparison of machine learning methods for two class motor imagery tasks using EEG in brain–computer interface. Adv Sci and Eng Technol Inter Conf (ASET). https://doi.org/10.1109/ICASET.2018.8376886CrossRef

29.

Zhang Y, Liu J (2018) EEG recognition of motor imagery based on SVM ensemble. 5th Int Conf on Systems and Informatics. https://doi.org/10.1109/ICSAI.2018.8599464CrossRef

30.

Li B, Yang B, Guan C, Hu C (2019) Three-class motor imagery classification based on FBCSP combined with voting mechanism. IEEE Inter Conference on Comput Intelligence Virtual Environ Measurement Syst Appl. https://doi.org/10.1109/CIVEMSA45640.2019.9071618CrossRef

31.

Wang J, Feng Z, Lu N (2017) Feature extraction by common spatial pattern in frequency domain for motor imagery tasks classification. 29th Chinese Control and Decision Conference (CCDC). https://doi.org/10.1109/CCDC.2017.7978220CrossRef

32.

Mishuhina V, Jiang X (2018) Feature weighting and regularization of common spatial patterns in EEG-based motor imagery BCI. IEEE Signal Process Lett 25(6):783–787. https://doi.org/10.1109/LSP.2018.2823683CrossRef

33.

Molla KI, Shiam AA, Islam R, Tanaka T (2020) Discriminative feature selection-based motor imagery classification using EEG signal. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2996685CrossRef

34.

Jin Z, Zhou G, Gao D, Zhang Y (2020) EEG classification using sparse Bayesian extreme learning machine for brain–computer interface. Neural Comput Appl 32:6601–6609. https://doi.org/10.1007/s00521-018-3735-3CrossRef

35.

Ahangi A, Karamnejad M, Mohammadi N, Ebrahimpour R, Bagheri N (2013) Multiple classifier system for EEG signal classification with application to brain–computer interfaces. Neural Comput Appl 23:1319–1327. https://doi.org/10.1007/s00521-012-1074-3CrossRef

36.

BCI Competitions 3 (2005), http://www.bbci.de/competition/iii/.

37.

BCI Competitions 4 (2008), http://www.bbci.de/competition/iv/.

38.

Olmez T, Dokur Z (2021) Strengthening the training of convolutional neural networks by using Walsh matrix. arXiv:2104.00035.

39.

Dokur Z, Olmez T (2020) Heartbeat classification by using a convolutional neural network trained with Walsh functions. Neural Comput Appl 32(16):12515–12534. https://doi.org/10.1007/s00521-020-04709-wCrossRef

40.

Phang CR, Ko LW (2020) Global cortical network distinguishes motor imagination of the left and right foot. IEEE Access 8:103734–103745. https://doi.org/10.1109/ACCESS.2020.2999133CrossRef

41.

Leon LC, Bougrain L (2015) A multi-label classification method for detection of combined motor imageries. IEEE International Conference on Systems, Man, and Cybernetics. https://doi.org/10.1109/SMC.2015.543CrossRef

42.

Yi W, Qiu S et al (2016) EEG oscillatory patterns and classification of sequential compound limb motor imagery. Jour of Neuro Engineering and Rehabilitation 13:11. https://doi.org/10.1186/s12984-016-0119-8CrossRef

43.

Yijie Z, Bin G et al (2018) A multiuser collaborative strategy for MI-BCI system. 23rd IEEE Internat Conf Digital Signal Proces. https://doi.org/10.1109/ICDSP.2018.8631864CrossRef

44.

Chen Z, Wang Z, Wang K, Yi W, Qi H (2019) Recognizing motor imagery between hand and forearm in the same limb in a hybrid brain computer interface paradigm: An online study. IEEE Access 7:59631–59639. https://doi.org/10.1109/ACCESS.2019.2915614CrossRef

45.

León CL, Rimbert S, Bougrain L (2020) Multiclass classification based on combined motor imageries. Front Neurosci 14:1–14. https://doi.org/10.3389/fnins.2020.559858CrossRef

46.

Yi W, Qiu S, Qi H, Zhang L, Wan B, Ming D (2013) EEG feature comparison and classification of simple and compound limb motor imagery. Journal of Neuro Engineering and Rehabilitation 10:106. https://doi.org/10.1186/1743-0003-10-106CrossRef

47.

Fahimi F, Zhang Z, Goh WB, Ang KK, Guan C (2019) Towards EEG Generation Using GANs for BCI Applications. IEEE EMBS Inter Conf on Biomed Health Inform (BHI). https://doi.org/10.1109/BHI.2019.8834503CrossRef

48.

Roy S, Dora S, McCreadie K, Prasad G (2020) MIEEG-GAN: Generating Artificial Motor Imagery Electroencephalography Signals. Inter Joint Conf Neural Netw (IJCNN). https://doi.org/10.1109/IJCNN48605.2020.9206942CrossRef

49.

Dinarès-Ferran J, Ortner R, Guger C, Solé-Casals J (2018) A New method to generate artificial frames using the empirical mode decomposition for an EEG-Based motor imagery bcI. Frontiers in Neurosci. https://doi.org/10.3389/fnins.2018.00308CrossRef

Title: Generating ten BCI commands using four simple motor imageries and classification by divergence-based DNN
Authors: Nuri Korhan
Tamer Olmez
Zümray Dokur
Publication date: 26-09-2022
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 2/2023
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-07787-0

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 2/2023

Deep reinforcement learning for automated search of model parameters: photo-fenton wastewater disinfection case study

Transparent deep machine learning framework for predicting traffic crash severity

Evaluating the performance of meta-heuristic algorithms on CEC 2021 benchmark problems

3 s-STNet: three-stream spatial–temporal network with appearance and skeleton information learning for action recognition

Adapting deep learning models between regional markets

Multi-modal traffic event detection using shapelets

Premium Partner