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Handbook of Neuroengineering pp 1–39Cite as

Deep Learning Methods for EEG Neural Classification

Abstract

Classification of patterns of brain activity in neuroengineering research is an important tool for understanding the brain, developing neurodiagnostics, and designing closed-loop neural interfaces. Scalp electroencephalography (EEG), by virtue of its noninvasiveness and lower cost, has been used for neural signal classification, and researchers have utilized various machine learning methods. Recently, deep learning has gained popularity due to its ability to significantly increase the classification performance in numerous domains while elucidating the relevant features for classification. It is a natural step to deploy such promising techniques for EEG classification tasks. This book chapter aims to serve as a comprehensive reference source for both EEG and deep learning researchers interested in EEG-based deep learning studies. Potential pitfalls, challenges, and opportunities in the application of deep learning to EEG data are discussed.

Keywords

  • Deep learning
  • Machine learning
  • Neural networks
  • Electroencephalography
  • Neuroengineering
  • Volitional processes
  • External stimulation
  • Affective computing
  • Brain computer interfaces
  • Deep learning interpretation

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Abbreviations

AD:

Alzheimer’s Disease

AE:

AutoEncoder

AMIGOS:

Dataset for Affect, Personality, and Mood Research on Individuals and Groups

BCI:

Brain Computer Interface

BIDS:

Brain Imaging Data Structure

BLDA:

Bayesian Linear Discriminant Analysis

CCNN:

Channel-wise CNN

CNN:

Convolutional Neural Network

CNN-R:

Residual CNN

CSP:

Common Spatial Pattern

DBN:

Deep Belief Machine

DBS:

Deep Brain Stimulation

DEAP:

Database of Emotion Analysis using Physiological signals

DL:

Deep Learning

DSP:

Digital Signal Processing

EEG:

Electroencephalography

ELU:

Exponential Linear Unit

EMG:

Electromyography

EOG:

Electrooculography

ERN:

Error-related Negativity Response

ERP:

Event Related Potential

FFT:

Fast Fourier Transform

GRU:

Gated Recurrent Unit

HDCA:

Hierarchical Discriminant Component Analysis

ITR:

Information Transfer Rate

KMI:

Kinesthetic Motor Imagery

LDA:

Linear Discriminant Analysis

LRP:

Layer-wise Relevance Propagation

LSTM:

Long-Short Term Memory

LVQ:

Linear Vector Quantization

MASS:

Montreal Archive of Sleep Studies

MCI:

Motor Cognitive Impairment

MDM:

Minimum Distance to Mean

ML:

Machine Learning

MLP:

Multi-Layer Perceptron

MRCP:

Movement-Related Cortical Potential

NIH:

National Institutes of Health

NN:

Neural Network

PCA:

Principal Component Analysis

RBM:

Restricted Boltzmann Machine

RCNN:

Recurrent CNN

ReLU:

Rectified Linear Unit

REM:

Rapid Eye Movement

RNN:

Recurrent Neural Networks

RSVP:

Rapid Serial Visual Presentation

SEED:

SJTU Emotion EEG Database

SELU:

Scaled Exponential Linear Unit

SMR:

Sensory-Motor Rhythms

SNR:

Signal-to-Noise Ratio

SOTA:

State of the Art

SSVEP:

Steady-State Visual Evoked Potential

SVM:

Support Vector Machines

TCN:

Temporal Convolution Network

TSNN:

Two Stream Neural Network

VMI:

Visual Motor Imagery

WoS:

Web of Science

References

  1. Vaughan, T.M., et al.: Brain-computer interface technology: a review of the Second International Meeting (2003)

    Google Scholar 

  2. Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain–computer interfaces. J. Neural Eng. 4(2), R1 (2007)

    CrossRef  Google Scholar 

  3. Yannick, R., Hubert, B., Isabela, A., Alexandre, G., Jocelyn, F., et al.: Deep learning-based electroencephalography analysis: a systematic review. arXiv preprint arXiv:1901.05498 (2019). https://github.com/hubertjb/dleeg-review

  4. Sejnowski, T.J.: The unreasonable effectiveness of deep learning in artificial intelligence. In: Proceedings of the National Academy of Sciences (2020)

    Google Scholar 

  5. Craik, A., He, Y., Contreras-Vidal, J.L.P.: Deep learning for Electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16, 031001 (2019)

    CrossRef  Google Scholar 

  6. Morabito, F.C., et al.: Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer’s disease patients from scalp EEG recordings. In: 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow (RTSI), pp. 1–6. IEEE (2016)

    Google Scholar 

  7. Kim, D., Kim, K.: Detection of early stage Alzheimer’s disease using EEG relative power with deep neural network. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 352–355. IEEE (2018)

    Google Scholar 

  8. Zhao, Y., He, L.: Deep learning in the EEG diagnosis of Alzheimer’s disease. In: Asian Conference on Computer Vision, pp. 340–353. Springer (2014)

    Google Scholar 

  9. Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H., Subha, D.P.: Automated EEG-based screening of depression using deep convolutional neural network. Comput. Methods Programs Biomed. 161, 103–113 (2018)

    CrossRef  Google Scholar 

  10. Baltatzis, V., Bintsi, K.-M., Apostolidis, G.K., Hadjileontiadis, L.J.: Bullying incidences identification within an immersive environment using HD EEG-based analysis: a swarm decomposition and deep learning approach. Sci. Rep. 7(1), 1–8 (2017)

    CrossRef  Google Scholar 

  11. Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H.: Brain Informatics and Health: 8th International Conference, BIH 2015, London, 30 Aug–2 Sept 2015. Proceedings, vol. 9250. Springer (2015)

    Google Scholar 

  12. Bashivan, P., Rish, I., Yeasin, M., Codella, N.: Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv preprint arXiv:1511.06448 (2015)

    Google Scholar 

  13. Le Roux, N., Bengio, Y.: Representational power of restricted Boltzmann machines and deep belief networks. Neural Comput. 20(6), 1631–1649 (2008)

    CrossRef  MathSciNet  MATH  Google Scholar 

  14. Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98–113 (1997)

    CrossRef  Google Scholar 

  15. LeCun, Y.: Deep learning & convolutional networks. In: 27th IEEE Hot Chips Symposium, HCS 2015. Institute of Electrical and Electronics Engineers Inc (2016)

    Google Scholar 

  16. Pearlmutter, B.A.: Learning state space trajectories in recurrent neural networks. Neural Comput. 1(2), 263–269 (1989)

    CrossRef  Google Scholar 

  17. Onose, G., et al.: On the feasibility of using motor imagery EEG-based brain–computer interface in chronic tetraplegics for assistive robotic arm control: a clinical test and long-term post-trial follow-up. Spinal Cord 50(8), 599 (2012)

    CrossRef  Google Scholar 

  18. Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. IEEE 89(7), 1123–1134 (2001)

    CrossRef  Google Scholar 

  19. Féry, Y.-A.: Differentiating visual and kinesthetic imagery in mental practice. Can. J. Exp. Psychol./Revue Canadienne de Psychologie Expérimentale 57(1)), 1 (2003)

    Google Scholar 

  20. Tangermann, M., et al.: Review of the BCI competition IV. Front. Neurosci. 6, 55 (2012)

    CrossRef  Google Scholar 

  21. Abbas, W., Khan, N.A.: DeepMI: deep learning for multiclass motor imagery classification. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 219–222. IEEE (2018)

    Google Scholar 

  22. Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J. Neural Eng. 15(5), 056013 (2018)

    CrossRef  Google Scholar 

  23. Sakhavi, S., Guan, C., Yan, S.: Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Trans. Neural Netw. Learn. Syst. 99, 1–11 (2018)

    MathSciNet  Google Scholar 

  24. Luo, T.-J., Chao, F., et al.: Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network. BMC Bioinformatics 19(1), 344 (2018)

    CrossRef  Google Scholar 

  25. Wang, Z., Cao, L., Zhang, Z., Gong, X., Sun, Y., Wang, H.: Short time Fourier transformation and deep neural networks for motor imagery brain computer interface recognition. Concurr. Comput. Pract. Exp. 30(23), e4413 (2018)

    CrossRef  Google Scholar 

  26. Tefft, B.C., et al.: Prevalence of motor vehicle crashes involving drowsy drivers, United States, 2009–2013. Citeseer (2014)

    Google Scholar 

  27. Hajinoroozi, M., Mao, Z., Huang, Y.: Prediction of driver’s drowsy and alert states from EEG signals with deep learning. In: 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 493–496. IEEE (2015)

    Google Scholar 

  28. Zeng, H., Yang, C., Dai, G., Qin, F., Zhang, J., Kong, W.: EEG classification of driver mental states by deep learning. Cogn. Neurodyn. 12(6), 597–606 (2018)

    CrossRef  Google Scholar 

  29. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  30. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  31. Gao, Z., et al.: EEG-based spatio-temporal convolutional neural network for driver fatigue evaluation. IEEE Trans. Neural Netw. Learn. Syst. 30, 2755–2763 (2019)

    CrossRef  Google Scholar 

  32. Jeong, J.-H., Yu, B.-W., Lee, D.-H., Lee, S.-W.: Classification of drowsiness levels based on a deep spatio-temporal convolutional bidirectional LSTM network using electroencephalography signals. Brain Sci. 9(12), 348 (2019)

    CrossRef  Google Scholar 

  33. Borghini, G., Astolfi, L., Vecchiato, G., Mattia, D., Babiloni, F.: Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci. Biobehav. Rev. 44, 58–75 (2014)

    CrossRef  Google Scholar 

  34. Aghajani, H., Garbey, M., Omurtag, A.: Measuring mental workload with EEG+  fNIRS. Front. Hum. Neurosci. 11, 359 (2017)

    CrossRef  Google Scholar 

  35. Young, M.S., Brookhuis, K.A., Wickens, C.D., Hancock, P.A.: State of science: mental workload in ergonomics. Ergonomics 58(1), 1–17 (2015)

    CrossRef  Google Scholar 

  36. Jiao, Z., Gao, X., Wang, Y., Li, J., Xu, H.: Deep convolutional neural networks for mental load classification based on EEG data. Pattern Recogn. 76, 582–595 (2018)

    CrossRef  Google Scholar 

  37. Zhang, P., Wang, X., Zhang, W., Chen, J.: Learning spatial–spectral–temporal EEG features with recurrent 3D convolutional neural networks for cross-task mental workload assessment. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 31–42 (2018)

    CrossRef  Google Scholar 

  38. Zhang, P., Wang, X., Chen, J., You, W., Zhang, W.: Spectral and temporal feature learning with two-stream neural networks for mental workload assessment. IEEE Trans. Neural Syst. Rehabil. Eng. 27(6), 1149–1159 (2019)

    CrossRef  Google Scholar 

  39. Deepak, K., Kalbande, D.: A review on visual brain computer interface. In: Somsubhra, G., Sandip, B., Karabi, G., Indranath, S., Papun, B. (eds.) Advancements of Medical Electronics, pp. 193–206. Springer, New Delhi (2015). isbn: 978-81-322-2256-9

    Google Scholar 

  40. Gao, S., Wang, Y., Gao, X., Hong, B.: Visual and auditory brain–computer interfaces. IEEE Trans. Biomed. Eng. 61, 1436–1447 (2014)

    CrossRef  Google Scholar 

  41. Donchin, E., Spencer, K.M., Wijesinghe, R.: The mental prosthesis: assessing the speed of a P300-based braincomputer interface. IEEE Trans. Rehabil. Eng. Publ. IEEE Eng. Med. Biol. Soc. 8(2), 174–179 (2000)

    CrossRef  Google Scholar 

  42. Blankertz, B., et al.: The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans. Biomed. Eng. 51, 1044–1051 (2004)

    CrossRef  Google Scholar 

  43. Rakotomamonjy, A., Guigue, V.: BCI competition III: dataset II – ensemble of SVMs for BCI P300 speller. IEEE Trans. Biomed. Eng. 55, 1147–1154 (2008)

    CrossRef  Google Scholar 

  44. Liu, M., Wu, W., Gu, Z., Yu, Z., Qi, F., Li, Y.: Deep learning based on Batch Normalization for P300 signal detection. Neurocomputing 275, 288–297 (2018)

    CrossRef  Google Scholar 

  45. Manor, R., Geva, A.B.: Convolutional neural network for multi-category rapid serial visual presentation BCI. Front. Comput. Neurosci. 9, 146 (2015)

    CrossRef  Google Scholar 

  46. Cecotti, H., Gräser, A.: Convolutional neural networks for P300 detection with application to brain-computer interfaces. IEEE Trans. Pattern Anal. Mach. Intell. 33, 433–445 (2011)

    CrossRef  Google Scholar 

  47. Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Map (2017). https://github.com/robintibor/braindecode

  48. Shamwell, J., Lee, H., Kwon, H., Marathe, A.R., Lawhern, V., Nothwang, W.: Single-trial EEG RSVP classification using convolutional neural networks, vol. 9836 (2016). https://doi.org/10.1117/12.2224172

  49. Chen, X., Wang, Y., Nakanishi, M., Gao, X., T.-P. Jung, and Gao, S. “High-speed spelling with a noninvasive braincomputer interface. Proc. Natl. Acad. Sci. U. S. A. 112(44), E6058–E6067 (2015)

    Google Scholar 

  50. Volosyak, I., Valbuena, D., Luth, T., Malechka, T., Graser, A.: BCI demographics II: how many (and what kinds of) people can use a high-frequency SSVEP BCI? IEEE Trans. Neural Syst. Rehabil. Eng. 19, 232–239 (2011)

    CrossRef  Google Scholar 

  51. Guger, C., et al.: How many people could use an SSVEP BCI? Front. Neurosci. 6, 169 (2012)

    CrossRef  Google Scholar 

  52. Aznan, N.K.N., Bonner, S., Connolly, J.D., Moubayed, N.A., Breckon, T.P.: On the classification of SSVEP-based dry-EEG signals via convolutional neural networks. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3726–3731 (2018)

    Google Scholar 

  53. Kwan, P., Brodie, M.J.: Early identification of refractory epilepsy. N. Engl. J. Med. 342(5), 314–319 (2000)

    CrossRef  Google Scholar 

  54. Andrade, D., et al.: Long-term follow-up of patients with thalamic deep brain stimulation for epilepsy. Neurology 66(10), 1571–1573 (2006)

    CrossRef  Google Scholar 

  55. Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 64(6), Pt 1, 061907 (2001). http://epileptologie-bonn.de/cms/front_content.php?idcat=193&lang=3&changelang=3

  56. Winterhalder, M., Maiwald, T., Voss, H., Aschenbrenner-Scheibe, R., Timmer, J., Schulze-Bonhage, A.: The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods. Epilepsy Behav. 4(3), 318–325 (2003)

    CrossRef  MATH  Google Scholar 

  57. Ullah, I., Qazi, E.-H., Aboalsamh, H.A.: An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Syst. Appl. 107, 61–71 (2018)

    CrossRef  Google Scholar 

  58. Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100, 270–278 (2017)

    CrossRef  Google Scholar 

  59. Emami, A., Kunii, N., Matsuo, T., Shinozaki, T., Kawai, K., Takahashi, H.K.: Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images. NeuroImage: Clin. 22, 3 (2019)

    Google Scholar 

  60. Tjepkema-Cloostermans, M.C., de Carvalho, R.C., van Putten, M.J.: Deep learning for detection of focal epileptiform discharges from scalp EEG recordings. Clin. Neurophysiol. 129(10), 2191–2196 (2018)

    CrossRef  Google Scholar 

  61. Morabito, F.C., et al.: Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer’s disease patients from scalp EEG recordings. In: 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow (RTSI), pp. 1–6 (2016)

    Google Scholar 

  62. Kim, D., Kim, K.: Detection of early stage Alzheimer’s disease using EEG relative power with deep neural network. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 352–355 (2018)

    Google Scholar 

  63. Aboalayon, K., Faezipour, M., Almuhammadi, W., Moslehpour, S.: Sleep stage classification using EEG signal analysis: a comprehensive survey and new investigation. Entropy 18(9), 272 (2016)

    CrossRef  Google Scholar 

  64. Supratak, A., Dong, H., Wu, C., Guo, Y.: DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 1998–2008 (2017)

    CrossRef  Google Scholar 

  65. Krishnamoorthy, V., Shoorangiz, R., Weddell, S.J., Beckert, L., Jones, R.D.: Deep learning with convolutional neural network for detecting microsleep states from EEG: a comparison between the oversampling technique and cost-based learning. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4152–4155. IEEE (2019)

    Google Scholar 

  66. Chambon, S., Galtier, M.N., Arnal, P.J., Wainrib, G., Gramfort, A.: A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Trans. Neural Syst. Eng. 26(4), 758–769 (2018)

    CrossRef  Google Scholar 

  67. Koelstra, S., et al.: DEAP: a database for emotion analysis; Using physiological signals. IEEE Trans. Affect. Comput. (2012). issn: 19493045. https://doi.org/10.1109/T-AFFC.2011.15

  68. Zheng, W.L., Lu, B.L.: Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans. Auton. Ment. Dev. (2015). issn: 19430604. https://doi.org/10.1109/TAMD.2015.2431497

  69. Zheng, W.-L., Zhu, J.-Y., Lu, B.-L.: Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans. Affect. Comput. (2017). issn: 1949–3045

    Google Scholar 

  70. Miranda Correa, J.A., Abadi, M.K., Sebe, N., Patras, I.: AMIGOS: a dataset for affect, personality and mood research on individuals and groups (2018). https://doi.org/10.1109/TAFFC.2018.2884461. arXiv: 1702.02510v3

  71. Song, T., Zheng, W., Lu, C., Zong, Y., Zhang, X., Cui, Z.: MPED: a multi-modal physiological emotion database for discrete emotion recognition. IEEE Access 7, 12177–12191 (2019). issn: 2169–3536

    Google Scholar 

  72. Katsigiannis, S., Ramzan, N.: DREAMER: a database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE J. Biomed. Health Informatics 22(1), 98–107 (2018). issn: 2168–2194

    Google Scholar 

  73. Jirayucharoensak, S., Pan-Ngum, S., Israsena, P.: EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation. Sci. World J. (2014). issn: 1537744X. https://doi.org/10.1155/2014/627892. arXiv: 627892

  74. Jia, X., Li, K., Li, X., Zhang, A.: A novel semi-supervised deep learning framework for affective state recognition on eeg signals. In: 2014 IEEE International Conference on Bioinformatics and Bioengineering, pp. 30–37. IEEE (2014). isbn: 1479975028

    Google Scholar 

  75. Zheng, W.L., Zhu, J.Y., Peng, Y., Lu, B.L.: EEG-based emotion classification using deep belief networks. In: Proceedings – IEEE International Conference on Multimedia and Expo (2014). isbn: 978-1-4799-4761-4. https://doi.org/10.1109/ICME.2014.6890166

  76. Liu, W., Zheng, W.L., Lu, B.L.: Emotion recognition using multimodal deep learning. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2016). isbn: 9783319466712. https://doi.org/10.1007/978-3-319-46672-9_58

  77. Li, J., Zhang, Z., He, H.: Implementation of EEG emotion recognition system based on hierarchical convolutional neural networks. In: International Conference on Brain Inspired Cognitive Systems, pp. 22–33. Springer (2016)

    Google Scholar 

  78. Tripathi, S., Acharya, S., Ranti, S., Mittal, S., Bhattacharya, S.: Using deep and convolutional neural networks for accurate emotion classification on DEAP dataset. In: Proceedings of the Twenty-Ninth AAAI Conference on Innovative Applications (2017). issn: 00415782

    Google Scholar 

  79. Xu, H., Plataniotis, K.N.: Affective states classification using EEG and semi-supervised deep learning approaches. In: 2016 IEEE 18th International Workshop on Multimedia Signal Processing, MMSP 2016 (2017). isbn: 9781509037247. https://doi.org/10.1109/MMSP.2016.7813351

  80. Li, X., Song, D., Zhang, P., Yu, G., Hou, Y., Hu, B.: Emotion recognition from multi-channel EEG data through Convolutional Recurrent Neural Network. In: Proceedings – 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (2017). isbn: 9781509016105. https://doi.org/10.1109/BIBM.2016.7822545

  81. Yanagimoto, M., Sugimoto, C.: Recognition of persisting emotional valence from EEG using convolutional neural networks. In: 2016 IEEE 9th International Workshop on Computational Intelligence and Applications, IWCIA 2016 – Proceedings (2017). isbn: 9781509027750. https://doi.org/10.1109/IWCIA.2016.7805744

  82. Zhang, T., Zheng, W., Cui, Z., Zong, Y., Li, Y.: Spatial-Temporal Recurrent Neural Network for Emotion Recognition (2018). https://doi.org/10.1109/TCYB.2017.2788081. arXiv: 1705.04515

  83. Bozhkov, L., Koprinkova-Hristova, P., Georgieva, P.: Learning to decode human emotions with Echo State Networks. Neural Netw. (2016). issn: 18792782. https://doi.org/10.1016/j.neunet.2015.07.005

  84. Mehmood, R.M., Du, R., Lee, H.J.: Optimal feature selection and deep learning ensembles method for emotion recognition from human brain EEG sensors. IEEE Access (2017). issn: 21693536. https://doi.org/10.1109/ACCESS.2017.2724555

  85. Chao, H., Zhi, H., Dong, L., Liu, Y.: Recognition of emotions using multichannel EEG data and DBN-GC-based ensemble deep learning framework. Comput. Intell. Neurosci. (2018). issn: 1687–5265

    Google Scholar 

  86. Song, T., Zheng, W., Song, P., Cui, Z.: EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans. Affect. Comput. (2018). issn: 1949–3045

    Google Scholar 

  87. Miranda-Correa, J.A., Patras, I.: A multi-task cascaded network for prediction of affect, personality, mood and social context using EEG signals. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 373–380. IEEE (2018). isbn: 1538623358

    Google Scholar 

  88. Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H., Subha, D.P.: Automated EEG-based screening of depression using deep convolutional neural network. Comput. Methods Programs Biomed. (2018). issn: 18727565. https://doi.org/10.1016/j.cmpb.2018.04.012

  89. Li, X., et al.: EEG-based mild depression recognition using convolutional neural network. Med. Biol. Eng. Comput. 1–12 (2019). issn: 0140–0118

    Google Scholar 

  90. Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1–38 (2019)

    CrossRef  MathSciNet  MATH  Google Scholar 

  91. Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., Giannotti, F.: A survey of methods for explaining black box models. ACM Comput. Surv. 51, 93:1–93:42 (2018)

    Google Scholar 

  92. Zhang, Q., Zhu, S.-C.: Visual interpretability for deep learning: a survey. Front. Inf. Technol. Electron. Eng. 19, 27–39 (2018)

    CrossRef  Google Scholar 

  93. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)

    CrossRef  Google Scholar 

  94. Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 80–89 (2018)

    Google Scholar 

  95. Haufe, S., et al.: On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage 87, 96–110 (2014)

    CrossRef  Google Scholar 

  96. Sturm, I., Bach, S., Samek, W., Müller, K.-R.: Interpretable deep neural networks for single-trial EEG classification. J. Neurosci. Methods 274, 141–145 (2016)

    CrossRef  Google Scholar 

  97. Molnar, C.: Interpretable Machine Learning. A Guide for Making Black Box Models Explainable. https://christophm.github.io/interpretable-ml-book/. leanpub.com (2019)

  98. Cecotti, H., Eckstein, M.P., Giesbrecht, B.: Single-trial classification of event-related potentials in rapid serial visual presentation tasks using supervised spatial filtering. IEEE Trans. Neural Netw. Learn. Syst. 25, 2030–2042 (2014)

    CrossRef  Google Scholar 

  99. Ravindran, A.S., Mobiny, A., Cruz-Garza, J.G., Paek, A., Kopteva, A., Contreras-Vidal, J.L.: Assaying neural activity of children during video game play in public spaces: a deep learning approach. J. Neural Eng. 16, 036028 (2019)

    CrossRef  Google Scholar 

  100. Erhan, D., Bengio, Y., Courville, A., Vincent, P.: Visualizing higher-layer features of a deep network. Tech Rep Univ Montreal 1341(3), 1 (2009)

    Google Scholar 

  101. Ravindran, A.S., et al.: Interpretable deep learning models for single trial prediction of balance loss. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2020, Accepted)

    Google Scholar 

  102. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)

    Google Scholar 

  103. Munafò, M.R., et al.: A manifesto for reproducible science. Nat. Hum. Behav. 1, 0021 (2017)

    CrossRef  Google Scholar 

  104. Pernet, C., Appelhoff, S., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R.: BIDS-EEG: an extension to the Brain Imaging Data Structure (BIDS) Specification for electroencephalography (2018). https://doi.org/10.31234/osf.io/63a4y

    Google Scholar 

  105. Papers with Code. https://paperswithcode.com/sota

  106. Lee, B.D.: Ten simple rules for documenting scientific software. PLoS Comput. Biol. 14, e1006561 (2018)

    CrossRef  Google Scholar 

  107. Boettiger, C.: An introduction to Docker for reproducible research. Oper. Syst. Rev. 49, 71–79 (2015)

    CrossRef  Google Scholar 

  108. Kurtzer, G.M., Sochat, V.V., Bauer, M.W.: Singularity: scientific containers for mobility of compute. PloS One 12, e0177459 (2017)

    CrossRef  Google Scholar 

  109. Buck, I.: GPU computing with NVIDIA CUDA. In: ACM SIGGRAPH 2007 Courses. SIGGRAPH’07. ACM, San Diego (2007). isbn: 978-1-4503-1823-5. https://doi.org/10.1145/1281500.1281647

  110. Li, R., et al.: Training on the test set? An analysis of Spampinato et al. [arXiv:1609.00344] (2018)

    Google Scholar 

  111. Spampinato, C., Palazzo, S., Kavasidis, I., Giordano, D., Shah, M., Souly, N.: Deep learning human mind for automated visual classification. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4503–4511 (2017)

    Google Scholar 

  112. Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Trans. Biomed. Circuits Syst. 13(5), 804–813 (2019)

    CrossRef  Google Scholar 

  113. Kilicarslan, A., Grossman, R.G., Contreras-Vidal, J.L.: A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements. J. Neural Eng. 13(2), 026013 (2016)

    CrossRef  Google Scholar 

  114. Mullen, T., et al.: Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 2184–2187 (2013), isbn: 9781457702167. https://doi.org/10.1109/EMBC.2013.6609968. arXiv: NIHMS150003

  115. Bigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K.-M., Robbins, K.A.: The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Front. Neuroinformatics 9, 16 (2015)

    CrossRef  Google Scholar 

  116. Cruz-Garza, J.G., et al.: Deployment of mobile EEG technology in an art museum setting: evaluation of signal quality and usability. Front. Hum. Neurosci. 11, 527 (2017)

    CrossRef  Google Scholar 

  117. Lotte, F., et al.: A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J. Neural Eng. 15(3), 031005 (2018)

    CrossRef  Google Scholar 

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Authors and Affiliations

  1. Laboratory for Noninvasive Brain-Machine Interface Systems, IUCRC BRAIN, University of Houston, Houston, TX, USA

    Sho Nakagome, Alexander Craik, Akshay Sujatha Ravindran, Yongtian He, Jesus G. Cruz-Garza & Jose L. Contreras-Vidal

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  1. Sho Nakagome
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  2. Alexander Craik
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  3. Akshay Sujatha Ravindran
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  4. Yongtian He
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  5. Jesus G. Cruz-Garza
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  6. Jose L. Contreras-Vidal
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Corresponding author

Correspondence to Jose L. Contreras-Vidal .

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Editors and Affiliations

  1. Singapore Institute for Neurotechnology, National University of Singapore, Singapore, Singapore

    Prof. Dr. Nitish V. Thakor

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  1. N.1. Institute for Health, National University of Singapore, Singapore, Singapore

    Andrei Dragomir

  2. Department of Biomedical Engineering, University of Houston, Houston, USA

    Andrei Dragomir

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Nakagome, S., Craik, A., Sujatha Ravindran, A., He, Y., Cruz-Garza, J.G., Contreras-Vidal, J.L. (2022). Deep Learning Methods for EEG Neural Classification. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-2848-4_78-1

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