Abstract
Physiological signals contain considerable information regarding emotions. This paper investigated the ability of photoplethysmogram (PPG) signals to recognize emotion, adopting a two-dimensional emotion model based on valence and arousal to represent human feelings. The main purpose was to recognize short term emotion using a single PPG signal pulse. We used a one-dimensional convolutional neural network (1D CNN) to extract PPG signal features to classify the valence and arousal. We split the PPG signal into a single 1.1 s pulse and normalized it for input to the neural network based on the personal maximum and minimum values. We chose the dataset for emotion analysis using physiological (DEAP) signals for the experiment and tested the 1D CNN as a binary classification (high or low valence and arousal), achieving the short-term emotion recognition of 1.1 s with 75.3% and 76.2% valence and arousal accuracies, respectively, on the DEAP data.
Highlights
Emotions are conscious and/or unconscious feelings about a phenomenon or work, and are linked to mood, disposition, personality, and motivation
We preprocessed the raw data and segmented it as a single pulse, and normalized the signals to reduce PPG signal size variations between people. This resulted in the single PPG signal pulse being expressed as a 140 × 1 vector, which was used as the input for the convolutional neural network (CNN)
The proposed 1D CNN method achieved short term emotion recognition, providing acceptably high accuracy compared with other methods for both arousal and valence, but only requiring 1.1 s which indicates the shortest interval of recognition
Summary
Emotions are conscious and/or unconscious feelings about a phenomenon or work, and are linked to mood, disposition, personality, and motivation. If someone does not reveal their feelings through their facial expressions, i.e., “poker face”, or does not say anything, it is difficult to detect their real emotion from their external appearance Their physiological signals, such as respiration or heart rate, are more specific, Appl. Emotion recognition using physiological signals has been an active research field, developing many methods [12,13,14], but tending to two main approaches: classifying emotions based on hand-crafted features or deep learning frameworks to extract features. Alhagry et al proposed a long-short term memory (LSTM) model to learn EEG features and classify emotion depending on arousal and valence values [22].
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