Using Artificial Neural Networks on Multi-channel EEG Data to Detect the Effect of Binaural Stimuli in Resting State

  • Maurício da Silva Júnior
  • Rafaela Covello de Freitas
  • Washington Wagner Azevedo da Silva
  • Marcelo Cairrão Araújo Rodrigues
  • Erick Francisco Quintas Conde
  • Wellington Pinheiro dos SantosEmail author
Part of the Series in BioEngineering book series (SERBIOENG)


More than 7% of the population of the world is afflicted by anxiety disorders. If related to mood disorders, anxiety can trigger or escalate other symptoms and affects mental diseases, akin depression, and suicidal behavior. Recent works have shown that binaural beats are able to reduce anxiety and modify other psychological conditions, significantly changing cognitive processes and mood states. They can be defined as a low-frequency acoustic stimuli perceived when a given individual is subjected to two marginally different wave frequencies, from 200 to 900 Hz. In the present work, we applied a 5 Hz binaural beat to 6 different subjects, to detect if relevant changes could be noticed in their brainwaves after the stimuli. Twenty minutes stimuli in ten separate sessions were applied. In order to detect these possible differences, we used a single hidden layer Multi-Layer Perceptron neural network and compared its results to non-parametric statistical tests and Low-Resolution Brain Electromagnetic Tomography (eLORETA). Results obtained on eLORETA point to a strong increase in the current distribution, mostly in the Alpha 2 band, at the Anterior Cingulate, pertinent to the recognition and expression of emotions, as well as the monitoring of mistakes regarding social conduct. Many Artificial neural networks models, principally Multi-Layer Perceptron architectures, proved to be able to highlight the main differences with high separability in Delta and Theta spectral bands.



We would like to express our gratitude to Dr. Marcelo Cairrão and Dr. Sílvia Laurentino for their valuable and constructive suggestions during the development of this research work. We also gratefully acknowledge the Brazilian federal funding agency, CAPES, for partial financial support. Also, this chapter was modified from the paper published by our group in Cognitive Systems Research (Maurício da Silva Junior, Rafaela Covello de Freitas, Wellington Pinheiro dos Santos, Washington Wagner Azevedo da Silva, Marcelo Cairrão Araújo Rodrigues, Erick Francisco Quintas Conde; Available online on 16 of November of 2018; Volume 54; pages 1–20). The related contents are re-used with permission.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Maurício da Silva Júnior
    • 1
  • Rafaela Covello de Freitas
    • 2
  • Washington Wagner Azevedo da Silva
    • 1
  • Marcelo Cairrão Araújo Rodrigues
    • 1
  • Erick Francisco Quintas Conde
    • 3
  • Wellington Pinheiro dos Santos
    • 1
    Email author
  1. 1.Universidade Federal de PernambucoRecifeBrazil
  2. 2.Escola Politécnica de Universidade de PernambucoRecifeBrazil
  3. 3.Universidade Federal FluminenseCampos dos GoytacazesBrazil

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