Modeling Physiological Data with Deep Belief Networks.

Dan Wang · Yi Shang

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Emotion and Mood Recognition Neural Networks and Applications EEG and Brain-Computer Interfaces

Abstract

Feature extraction is key in understanding and modeling of physiological data. Traditionally hand-crafted features are chosen based on expert knowledge and then used for classification or regression. To determine important features and pick the effective ones to handle a new task may be labor-intensive and time-consuming. Moreover, the manual process does not scale well with new or large-size tasks. In this work, we present a system based on Deep Belief Networks (DBNs) that can automatically extract features from raw physiological data of 4 channels in an unsupervised fashion and then build 3 classifiers to predict the levels of arousal, valance, and liking based on the learned features. The classification accuracies are 60.9%, 51.2%, and 68.4%, respectively, which are comparable with the results obtained by Gaussian Naïve Bayes classifier on the state-of-the-art expert designed features. These results suggest that DBNs can be applied to raw physiological data to effectively learn relevant features and predict emotions.

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References

2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals 2010Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications 2008Emotion recognition based on physiological changes in music listening 2003Affective computing: challenges 1999Measuring Emotion: Behavior, Feeling, and Physiology 2005A systems approach to appraisal mechanisms in emotion 2009Short-term emotion assessment in a recall paradigm 2011Continuous inference of psychological stress from sensory measurements collected in the natural environment

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2016Affective states classification using EEG and semi-supervised deep learning approaches 2022Subject-independent EEG emotion recognition with hybrid spatio-temporal GRU-Conv architecture 2020Emotion Recognition in Valence-Arousal Space from Multi-channel EEG data and Wavelet based Deep Learning Framework 2017Deep Convolutional Neural Network for Emotion Recognition Using EEG and Peripheral Physiological Signal 2018Recognition of Emotions Using Multichannel EEG Data and DBN-GC-Based Ensemble Deep Learning Framework

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