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Emotion Recognition using Spatiotemporal Analysis of Electroencephalographic Signals

Aspiras, Theus H.
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1343992574

Abstract Details

2012, Master of Science (M.S.), University of Dayton, Electrical Engineering.
Emotion recognition using electroencephalographic (EEG) recordings is a new area of research which focuses on recognition of emotional states of mind rather than impulsive responses. EEG recordings are found useful for the detection of emotions through monitoring the emotion characteristics of spatiotemporal variations of activations inside the brain. To distinguish between different emotions using EEG data, we need to provide specific spectral descriptors as features to quantify these spatiotemporal variations. We propose several new features, namely Normalized Root Mean Square (NRMS), Absolute Logarithm Normalized Root Mean Square (ALRMS), Logarithmic Power (LP), Normalized Logarithmic Power (NLP), and Absolute Logarithm Normalized Logarithmic Power (ALNLP) for the classification of emotions. A protocol has been established to elicit five distinct emotions: joy, sadness, disgust, fear, surprise, and neutral. EEG signals are collected using a 256-channel system, preprocessed using band-pass filters and a Laplacian Montage, and decomposed into five frequency bands using Discrete Wavelet Transform. The decomposed signals are transformed into different spectral descriptors and are classified using a two-layer Multilayer Perceptron (MLP) neural network. The Logarithmic Power descriptor produces the highest recognition rates, 91.82% and 94.27% recognition for two different experiments, which is more than 2% higher than when using other features.
Vijayan Asari, PhD (Committee Chair)
Tarek Taha, PhD (Committee Member)
Eric Balster, PhD (Committee Member)
Computer Engineering; Electrical Engineering; Engineering; Neurosciences; Psychology
Emotion Recognition; Electroencephalography; Wavelet Decomposition; Multilayer Perceptron; Laplacian Montage; International Affective Picture System

Recommended Citations

Citations

  • Aspiras, T. H. (2012). Emotion Recognition using Spatiotemporal Analysis of Electroencephalographic Signals [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1343992574

    APA Style (7th edition)

  • Aspiras, Theus. Emotion Recognition using Spatiotemporal Analysis of Electroencephalographic Signals. 2012. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1343992574.

    MLA Style (8th edition)

  • Aspiras, Theus. "Emotion Recognition using Spatiotemporal Analysis of Electroencephalographic Signals." Master's thesis, University of Dayton, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1343992574

    Chicago Manual of Style (17th edition)

dayton1343992574
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© 2012, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.