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

  • Anushri SahaEmail author
  • 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)

Abstract

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).

Keywords

Cognitive load Stacked denoising autoencoder Long short term memory Multilayer perceptron 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Anushri Saha
    • 1
    Email author
  • 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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