Improving the Feature Stability and Classification Performance of Bimodal Brain and Heart Biometrics

  • Ramaswamy Palaniappan
  • Samraj Andrews
  • Ian P. Sillitoe
  • Tarsem Shira
  • Raveendran Paramesran
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 425)

Abstract

Electrical activities from brain (electroencephalogram, EEG) and heart (electrocardiogram, ECG) have been proposed as biometric modalities but the combined use of these signals appear not to have been studied thoroughly. Also, the feature stability of these signals has been a limiting factor for biometric usage. This paper presents results from a pilot study that reveal the combined use of brain and heart modalities provide improved classification performance and furthermore, an improvement in the stability of the features over time through the use of binaural brain entrainment. The classification rate was increased, for the case of the neural network classifier from 92.4% to 95.1% and for the case of LDA, from 98.6% to 99.8%. The average standard deviation with binaural brain entrainment using all the inter-session features (from all the subjects) was 1.09, as compared to 1.26 without entrainment. This result suggests the improved stability of both the EEG and ECG features over time and hence resulting in higher classification performance. Overall, the results indicate that combining ECG and EEG gives improved classification performance and that through the use of binaural brain entrainment, both the ECG and EEG features are more stable over time.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ramaswamy Palaniappan
    • 1
  • Samraj Andrews
    • 2
  • Ian P. Sillitoe
    • 3
  • Tarsem Shira
    • 4
  • Raveendran Paramesran
    • 5
  1. 1.School of ComputingUniversity of KentChathamUK
  2. 2.Department of Information TechnologyMahendra Engineering CollegeSalemIndia
  3. 3.School of EngineeringUniversity of WolverhamptonTelfordUK
  4. 4.Department of Engineering and MathematicsSheffield Hallam UniversitySheffieldUK
  5. 5.Department of Electrical EngineeringUniversity of MalayaKuala LumpurMalaysia

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