Multi-Target Positive Emotion Recognition From EEG Signals

Compared with the widely studied negative emotions in which different classes are easy to distinguish, nowadays less attention is paid to the recognition of positive emotions that are not fully independent. In this article, we propose to recognize multiple continuous positive emotions that exhibit statistical dependencies using multi-target regression — by analyzing brain activities when an individual watches emotional film clips — and explore the neural representation of different positive emotions. Thirty-seven participants volunteered to participate in our study, in which their brain activities were recorded when watching five selected film clips (corresponding to five positive emotions: amusement, happiness, romance, tenderness and warmth). First, 150 well-known power features extracted from Electroencephalography (EEG) signals and 105 multimedia content analysis features were collected as the pool of candidate features. Second, based on the collected features, we propose to use a linear model (linear regression) and a nonlinear model (long short-term memory network, LSTM) to predict the percentage of five positive emotions. Then, percentage values were converted to ranking numbers and Kendall rank correlation coefficients were calculated. Our results showed that (1) ensemble of regressor chains (ERC) using LSTM as unit regressor obtained both the best regression results (with lowest RMSE = 8.325 and highest
$\text{R }^{2} = 0.346$
) and the best Kendall rank correlation coefficient (0.165) on EEG features merely, and (2) selective features from alpha frequency bands of EEG signals could represent different positive emotions. These results demonstrate the effectiveness of selective EEG features on recognizing different positive emotions.