Statistical Features Based Decision for Placement of Optical Sensor on Prefrontal Lobe for the Acquisition of Cranial PPG Signal in Emotion Analysis

This is the authors' abstract. We don't add key points for this paper.

In this work, the effect of sensor placement on the prefrontal lobe for emotion classification is analyzed with the objective of achieving maximum classification accuracy. Photoplethysmography (PPG) signals are acquired from the prefrontal region by positioning sensors at the left, central, and right prefrontal locations.Prefrontal PPG signals are recorded for two emotional states, namely happy, sad. A six-level wavelet decomposition is performed on the acquired PPG signals to obtain approximation (A6) and detail (D6) coefficients. Statistical features such as mean, variance,energy, and entropy are extracted from the wavelet-decomposed signals.The extracted features from the original PPG signal, as well as from the A6 and D6 components, effectively discriminate among different emotional states. The results demonstrate significant variations in statistical feature values across emotions. Furthermore, box plot analysis reveals that the differentiation between happy and sad emotions is more pronounced in the central prefrontal PPG signals compared to the left and right prefrontal signals. Consequently, features derived from the central prefrontal PPG signal are identified as more suitable for emotion classification. Central Prefrontal CPPG: Difference in mean between Happy and Sad is 35.58 and difference in variance between Happy and Sad 2924.66 for central A6 scale. For central D6 scale difference in variance between Happy and Sad is 1.14E+006. Difference in energy between Happy and Sad is 7176 and difference in entropy between Happy and Sad is 8.8E+006 for central A6 scale. For central D6 scale difference in energy between happy and sad 4422.59 and difference in entropy between Happy and Sad is 1.14E+006.