Classification of EEG Signals for Cognitive Load Estimation Using Deep Learning Architectures

  • Anushri Saha
  • Vikash Minz
  • Sanjith Bonela
  • S. R. Sreeja
  • Ritwika Chowdhury
  • Debasis Samanta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11278)


Measuring cognitive load is crucial for many applications such as information personalization, adaptive intelligent tutoring systems, etc. Cognitive load estimation using Electroencephalogram (EEG) signals is widespread as it produces clear indications of cognitive activities by measuring changes of neural activation in the brain. However, the existing cognitive load estimation techniques are based on machine learning algorithms, which follow signal denoising and hand-crafted feature extraction to classify different loads. There is a need to find a better alternative to the machine learning approach. Of late, deep learning approach has been successfully applied to many applications namely, computer vision, pattern recognition, speech processing, etc. However, deep learning has not been extensively studied for the classification of cognitive load data captured by an EEG. In this work, two deep learning models are studied, namely stacked denoising autoencoder (SDAE) followed by a multilayer perceptron (MLP) and long short term memory (LSTM) followed by an MLP to classify cognitive load data. SDAE and LSTM are used for feature extraction and MLP for classification. It is observed that deep learning models perform significantly better than the conventional machine learning classifiers such as support vector machine (SVM), k-nearest neighbors (KNN), and linear discriminant analysis (LDA).


Cognitive load Stacked denoising autoencoder Long short term memory Multilayer perceptron 


  1. 1.
    Introduction Auto-Encoder. Accessed 1 June 2018
  2. 2.
    Baars, B.J., Gage, N.M.: Cognition, Brain, and Consciousness: Introduction to Cognitive Neuroscience. Academic Press, Burlington (2010)Google Scholar
  3. 3.
    Das, D., Chatterjee, D., Sinha, A.: Unsupervised approach for measurement of cognitive load using EEG signals. In: 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 1–6. IEEE (2013)Google Scholar
  4. 4.
    Dong, H., Supratak, A., Pan, W., Wu, C., Matthews, P.M., Guo, Y.: Mixed neural network approach for temporal sleep stage classification. IEEE Trans. Neural Syst. Rehabil. Eng. 26, 324–333 (2017)CrossRefGoogle Scholar
  5. 5.
    Flesch, R.: A new readability yardstick. J. Appl. Psychol. 32(3), 221 (1948)CrossRefGoogle Scholar
  6. 6.
    Goldwater, B.C.: Psychological significance of pupillary movements. Psychol. Bull. 77(5), 340 (1972)CrossRefGoogle Scholar
  7. 7.
    Hinton, G.: Multilayer Perceptron. Accessed 1 June 2018
  8. 8.
    Kawasaki, K., Yoshikawa, T., Furuhashi, T.: Visualizing extracted feature by deep learning in P300 discrimination task. In: 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 149–154. IEEE (2015)Google Scholar
  9. 9.
    Lu, N., Li, T., Ren, X., Miao, H.: A deep learning scheme for motor imagery classification based on restricted boltzmann machines. IEEE Trans. Neural Syst. Rehabil. Eng. 25(6), 566–576 (2017)CrossRefGoogle Scholar
  10. 10.
    Olah, C.: Understanding LSTM Networks. Accessed 1 June 2018
  11. 11.
    Sanei, S., Chambers, J.A.: EEG Signal Processing. John Wiley & Sons, Chichester (2013)Google Scholar
  12. 12.
    Vidyaratne, L., Glandon, A., Alam, M., Iftekharuddin, K.M.: Deep recurrent neural network for seizure detection. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 1202–1207. IEEE (2016)Google Scholar
  13. 13.
    Werbos, P.J.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550–1560 (1990)CrossRefGoogle Scholar
  14. 14.
    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), pp. 1–6. IEEE (2016)Google Scholar
  15. 15.
    Xu, H., Plataniotis, K.N.: EEG-based affect states classification using deep belief networks. In: Digital Media Industry & Academic Forum (DMIAF), pp. 148–153. IEEE (2016)Google Scholar
  16. 16.
    Yin, Z., Zhang, J.: Recognition of cognitive task load levels using single channel EEG and stacked denoising autoencoder. In: 2016 35th Chinese Control Conference (CCC), pp. 3907–3912. IEEE (2016)Google Scholar
  17. 17.
    Zarjam, P., Epps, J., Chen, F.: Spectral EEG featuresfor evaluating cognitive load. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 3841–3844. IEEE (2011)Google Scholar
  18. 18.
    Zarjam, P., Epps, J., Lovell, N.H.: Beyond subjective self-rating: EEG signal classification of cognitive workload. IEEE Trans. Auton. Mental Dev. 7(4), 301–310 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Anushri Saha
    • 1
  • Vikash Minz
    • 1
  • Sanjith Bonela
    • 1
  • S. R. Sreeja
    • 1
  • Ritwika Chowdhury
    • 2
  • Debasis Samanta
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology KharagpurKharagpurIndia
  2. 2.Department of Electronics and Electrical Communication EngineeringIndian Institute of Technology KharagpurKharagpurIndia

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