Deep Learning Methods for EEG Signals Classification of Motor Imagery in BCI

Muhammad Fawaz Saputra; Noor Akhmad Setiawan; Igi Ardiyanto

doi:10.22146/ijitee.48110

Deep Learning Methods for EEG Signals Classification of Motor Imagery in BCI

https://doi.org/10.22146/ijitee.48110

Muhammad Fawaz Saputra^(1*), Noor Akhmad Setiawan⁽²⁾, Igi Ardiyanto⁽³⁾

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(*) Corresponding Author

Abstract

EEG signals are obtained from an EEG device after recording the user's brain signals. EEG signals can be generated by the user after performing motor movements or imagery tasks. Motor Imagery (MI) is the task of imagining motor movements that resemble the original motor movements. Brain Computer Interface (BCI) bridges interactions between users and applications in performing tasks. Brain Computer Interface (BCI) Competition IV 2a was used in this study. A fully automated correction method of EOG artifacts in EEG recordings was applied in order to remove artifacts and Common Spatial Pattern (CSP) to get features that can distinguish motor imagery tasks. In this study, a comparative studies between two deep learning methods was explored, namely Deep Belief Network (DBN) and Long Short Term Memory (LSTM). Usability of both deep learning methods was evaluated using the BCI Competition IV-2a dataset. The experimental results of these two deep learning methods show average accuracy of 50.35% for DBN and 49.65% for LSTM.

Keywords

Electroencephalograph; Motor Imagery; Mu; Beta; Brain Computer Interface; Deep Learning; Deep Belief Networks; Long Short Term Memory

Full Text:

PDF

References

R. Wang, D. Li, J. Wang, L. Cai, and L. Shi, “Synchrony Analysis Using Different Cross-Entropy Measures of the Electroencephalograph Activity in Alzheimer’s Disease,” Proc. - 2016 9th Int. Congr. Image Signal Process. Biomed. Eng. Informatics, CISP-BMEI 2016, 2016, pp. 1541–1545.

K. Umezawa, T. Saito, T. Ishida, M. Nakazawa, and S. Hirasawa, “An Electroencephalograph-Based Method for Judging the Difficulty of a Task Given to a Learner,” Proc. - IEEE 17th Int. Conf. Adv. Learn. Technol. ICALT 2017, 2017, pp. 384–386.

Y.J. Kim, N.S. Kwak, and S.W. Lee, “Classification of Motor Imagery for Ear-EEG Based Brain-Computer Interface,” 2018 6th Int. Conf. Brain-Computer Interface, BCI 2018, 2018, pp. 1–2.

R.N. Roy, S. Charbonnier, and S. Bonnet, "Detection of Mental Fatigue Using an Active BCI Inspired Signal Processing Chain," IFAC Proc. Volumes, Vol. 19, No. 3, pp. 2963-2968, 2014.

C. Rahmad, R. Ariyanto, and D. Rizky, “Brain Signal Classification using Genetic Algorithm for Right-Left Motion Pattern,” Int. J. Adv. Comput. Sci. Appl., Vol. 9, No. 11, pp. 247–251, 2018.

S. Sanei and J.A. Chambers, EEG Signal Processing, Hoboken, USA: John Wiley & Sons Ltd., 2007.

H.M. Hobson and D.V.M. Bishop, “Mu Suppression – A Good Measure of the Human Mirror Neuron System?,” Cortex, Vol. 82, pp. 290–310, 2016.

S. Shahid, R.K. Sinha, and G. Prasad, “Mu and Beta Rhythm Modulations in Motor Imagery Related Post-stroke EEG: A Study Under BCI Framework for Post-stroke Rehabilitation,” BMC Neurosci., Vol. 11, No. S1, pp. 1–2, 2010.

F. Pichiorri, F. De Vico Fallani, F Cincotti, F. Babiloni, M. Molinari, S.C. Kleih, C. Neuper, A. Kübler, and D. Mattia, “Sensorimotor Rhythm-based Brain-Computer Interface Training: The Impact on Motor Cortical Responsiveness,” J. Neural Eng., Vol. 8, No. 2, pp. 1-9, 2011.

X. Yong and C. Menon, “EEG Classification of Different Imaginary Movements within the Same Limb,” PLoS One, Vol. 10, No. 4, pp. 1-24, 2015.

J.R. Wolpaw and D.J. Mcfarland, “Control of a Two-dimensional Movement Signal by a Noninvasive Brain–Computer Interface in Humans,” Proc. Natl. Acad. Sci. USA, Vol. 101, No. 51, pp. 17849–17854, 2004.

M. Dai, D. Zheng, R. Na, S. Wang, and S. Zhang, “EEG Classification of Motor Imagery Using a Novel Deep Learning Framework,” Sensors (Switzerland), Vol. 19, No. 3, pp. 1–16, 2019.

S. Amari, “A Multichannel Deep Belief Network for the Classification of EEG Data,” J. Soc. Mech. Eng., Vol. 90, No. 823, pp. 758–759, 2017.

L.T. Xuyen, L.T. Thanh, D.V. Viet, T.Q. Long, N.L. Trung, and N.D. Thuan, “Deep Learning for Epileptic Spike Detection,” VNU J. Sci. Comput. Sci. Commun. Eng., Vol. 33, No. 2, pp. 1–13, 2018.

W.L. Zheng, J.Y. Zhu, Y. Peng, and B.L. Lu, “EEG-based Emotion Classification Using Deep Belief Networks,” Proc. - IEEE Int. Conf. Multimed. Expo, 2014, pp. 1-6.

N. Michielli, U.R. Acharya, and F. Molinari, “Cascaded LSTM Recurrent Neural Network for Automated Sleep Stage Classification Using Single-channel EEG Signals,” Comput. Biol. Med., Vol. 106, pp. 71–81, 2019.

S. Jawed, H.U. Amin, A.S. Malik, and I. Faye, “EEG Visual and Non- visual Learner Classification Using LSTM Recurrent Neural Networks,” Proc. 2018 IEEE EMBS Conf. Biomed. Eng. Sci. IECBES 2018, 2018, pp. 467–471.

A. Schlögl, C. Keinrath, D. Zimmermann, R. Scherer, R. Leeb, and G. Pfurtscheller, “A Fully Automated Correction Method of EOG Artifacts in EEG Recordings,” Clin. Neurophysiol., Vol. 118, No. 1, pp. 98–104, 2007.

H. Mo and Y. Zhao, “Motor Imagery Electroencephalograph Classification Based on Optimized Support Vector Machine by Magnetic Bacteria Optimization Algorithm,” Neural Processing Letters, Vol. 44, No. 1, pp. 185-197, Aug. 2016.

L. Duan, Z. Hongxin, M.S. Khan, and M. Fang, “Recognition of Motor Imagery Tasks for BCI Using CSP and Chaotic PSO Twin SVM,” J. China Univ. Posts Telecommun., Vol. 24, No. 3, pp. 83–90, 2017.

A. Craik, Y. He, and J.L. Contreras-Vidal, “Deep Learning for Electroencephalogram (EEG) Classification Tasks: A Review,” J. Neural Eng., Vol. 16, No. 3, pp. 1-28, 2019.

G.E. Hinton, S. Osindero, and Y.-W. Teh, “A Fast Learning Algorithm for Deep Belief Nets,” Neural Comput., Vol. 18, pp. 1527–1554, 2006.

X. An, D. Kuang, X. Guo, Y. Zhao, and L. He, “A Deep Learning Method for Classification of EEG Data Based on Motor Imagery," Proc. 10th Int. Conf. Intelligent Computing in Bioinformatics (ICIC 2014), 2014, pp. 203-210.

M.A. Keyvanrad and M.M. Homayounpour, “A Brief Survey on Deep Belief Networks and Introducing a New Object Oriented Toolbox (DeeBNet),” Lab. for Intelligent Multimedia Processing (LIMP), Amirkabir University of Technology, Tehran, Iran, Tech. Report, 2014, pp. 1–27.

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, Vol. 9, No. 8, pp. 1735–1780, 1997.

S. Alhagry, A.A. Fahmy, and R.A. El-Khoribi, “Emotion Recognition based on EEG using LSTM Recurrent Neural Network,” Int. J. Adv. Comput. Sci. Appl., Vol. 8, No. 10, pp. 8–11, 2017.

M.-H. Horng, “Fine-Tuning Parameters of Deep Belief Networks Using Artificial Bee Colony Algorithm,” DEStech Trans. Comput. Sci. Eng., pp. 69–72, 2018.

B. Nakisa, M.N. Rastgoo, A. Rakotonirainy, F. Maire, and V. Chandran, “Long Short Term Memory Hyperparameter Optimization for a Neural Network Based Emotion Recognition Framework,” IEEE Access, Vol. 6, pp. 49325–49338, 2018.

DOI: https://doi.org/10.22146/ijitee.48110