Abstract
From playing games with just the mind to capturing and re-constructing dreams, Brain computer Interfaces (BCIs) have turned fiction into reality. It has set new standards in the world of prosthetics, be it hearing aids or prosthetic arms, legs or vision, helping paralyzed or completely locked-in users. Not only can one get a visual imprint of their own brain activity but the future of BCI will make sharing someone else’s experience possible. The entire functioning of the BCI can be segmented into acquiring the signals, processing it, translation of signals, device that gives the output and the protocol in operation. The translation algorithms can be classical statistical analysis or non-linear methods such as neural networks. Deep learning might serve as one of the translation algorithms that converts the raw signals from the brain into commands that the output devices follow. This chapter aims to give an insight into the various deep learning algorithms that have served in BCI’s today and helped enhance their performances.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
G. Schalk, G. Schalk, D.J. McFarland et al., BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004). https://doi.org/10.1109/tbme.2004.827072
J.R. Wolpaw, D.J. McFarland, G.E. Fabiani et al., Conversion of EEG activity into cursor movement by a brain-computer interface (BCI). IEEE Trans. Neural Syst. Rehabil. Eng. 12, 331–338 (2004). https://doi.org/10.1109/tnsre.2004.834627
D. Marshall, D. Marshall, D. Coyle et al., Games, gameplay, and BCI: the state of the art. IEEE Trans. Comput. Intell. AI Games 5, 82–99 (2013). https://doi.org/10.1109/tciaig.2013.2263555
K.R. Müller, M. Tangermann, G. Dornhege et al., Machine learning for real-time single-trial EEG-analysis: from brain–computer interfacing to mental state monitoring. J. Neurosci. Methods 167, 82–90 (2008). https://doi.org/10.1016/j.jneumeth.2007.09.022
C. Guger, G. Pfurtscheller, W. Harkam, C. Hertnaes, Prosthetic control by an EEG-based brain-computer interface (BCI), in Proceedings of AAATE 5th European Conference for the Advancement of Assistive Technology, pp. 3–6 (1999)
F. Akram, S.M. Han, T.S. Kim, An efficient word typing P300-BCI system using a modified T9 interface and random forest classifier. Comput. Biol. Med. 56, 30–36 (2015). https://doi.org/10.1016/j.compbiomed.2014.10.021
J.R. Wolpaw, N. Birbaumer, W.J. Heetderks et al., Brain-computer interface technology: a review of the first international meeting. IEEE Trans. Rehabil. Eng. 8, 164–173 (2000). https://doi.org/10.1109/tre.2000.847807
F. Lotte, M. Congedo, A. Lécuyer et al., A review of classification algorithms for EEG-based brain–computer interfaces. J. Neural Eng. 4, R1–R13 (2007). https://doi.org/10.1088/1741-2560/4/2/r01
V. Shenoy Handiru, V.S. Handiru, A.P. Vinod, C. Guan, EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement. J. Neural Eng. 14, 046008 (2017). https://doi.org/10.1088/1741-2552/aa6baf
J.R. Wolpaw, G. Pfurtscheller, N. Birbaumer et al., Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002). https://doi.org/10.1016/s1388-2457(02)00057-3
I. Sutskever, A. Krizhevsky, G.E. Hinton, ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017). https://doi.org/10.1145/3065386
S.S. Roy, R. Roy, V.E. Balas, Estimating heating load in buildings using multivariate adaptive regression splines, extreme learning machine, a hybrid model of MARS and ELM. Renew. Sustain. Energ. Rev. 82, 4256–4268 (2018)
S.S. Roy, P. Kulshrestha, P. Samui, Classifying images of drought-affected area using deep belief network, kNN, and random forest learning techniques, Deep Learning Innovations and Their Convergence With Big Data, pp. 102–119. IGI Global
S.S. Roy, A. Mallik, R. Gulati, M.S. Obaidat, P.V. Krishna, A deep learning based artificial neural network approach for intrusion detection, in International Conference on Mathematics and Computing (Springer, Singapore, 2017)
S.S. Roy, V.M. Viswanatham, Classifying spam emails using artificial intelligent techniques. Int. J. Eng. Res. Africa 22 (2016)
G. Pfurtscheller, C. Neuper, Motor imagery and direct brain-computer communication. Proc. IEEE 89, 1123–1134 (2001). https://doi.org/10.1109/5.939829
A. Bandhu, S.S. Roy, Classifying multi-category images using deep learning: a convolutional neural network model, in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 915–919, May, 2017. IEEE
S. Sakhavi, S. Sakhavi, C. Guan et al., Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Trans. Neural Netw. Learn. Syst. 1–11. https://doi.org/10.1109/tnnls.2018.2789927
H. Cecotti, A. Graeser, Convolutional neural network with embedded Fourier transform for EEG classification. IEEE, pp. 1–4 (2008)
P. Bashivan, I. Rish, M. Yeasin, N. Codella, Learning representations from EEG with deep recurrent-convolutional neural networks (2015). arXiv:1511.06448
C. Guan, S. Sakhavi, Convolutional neural network-based transfer learning and knowledge distillation using multi-subject data in motor imagery BCI. IEEE, pp. 588–591 (2017)
C.W. Anderson, Z. Sijercic, Classification of EEG signals from four subjects during five mental tasks, pp. 407–414 (1996)
Z.A. Keirn, Z.A. Keirn, J.I. Aunon, J.I. Aunon, A new mode of communication between man and his surroundings. IEEE Trans. Biomed. Eng. 37, 1209–1214 (1990). https://doi.org/10.1109/10.64464
R.T. Schirrmeister, J.T. Springenberg, L.D.J. Fiederer et al., Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38, 5391–5420 (2017). https://doi.org/10.1002/hbm.23730
H. Cecotti, H. Cecotti, A. Graser, A. Gräser, Convolutional neural networks for P300 detection with application to brain-computer interfaces. IEEE Trans. Pattern Anal. Mach. Intell. 33, 433–445 (2011). https://doi.org/10.1109/tpami.2010.125
H. Cecotti, A time–frequency convolutional neural network for the offline classification of steady-state visual evoked potential responses. Pattern Recogn. Lett. 32, 1145–1153 (2011). https://doi.org/10.1016/j.patrec.2011.02.022
S. Stober, D.J. Cameron, J.A. Grahn, Using convolutional neural networks to recognize rhythm stimuli from electroencephalography recordings, in Neural Information Processing Systems (NIPS), pp. 1–9 (2014)
M. Hajinoroozi, Z. Mao, Y. Huan, Prediction of driver’s drowsy and alert states from EEG signals with deep learning. IEEE, pp. 493–496 (2015)
H. Yang, Y. Huijuan, S. Sakhavi et al., On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification. IEEE, pp. 2620–2623 (2015)
A. Phadtare, A. Bahmani, A. Shah, R. Pietrobon, Scientific writing: a randomized controlled trial comparing standard and on-line instruction. BMC Med. Educ. 9, 27 (2009). https://doi.org/10.1186/1472-6920-9-27
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Bose, A., Roy, S.S., Balas, V.E., Samui, P. (2019). Deep Learning for Brain Computer Interfaces. In: Balas, V., Roy, S., Sharma, D., Samui, P. (eds) Handbook of Deep Learning Applications. Smart Innovation, Systems and Technologies, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-030-11479-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-11479-4_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11478-7
Online ISBN: 978-3-030-11479-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)