Abstract
EEG-based automatic emotion recognition can help brain-inspired robots in improving their interactions with humans. This paper presents a novel framework for emotion recognition using multi-channel electroencephalogram (EEG). The framework consists of a linear EEG mixing model and an emotion timing model. Our proposed framework considerably decomposes the EEG source signals from the collected EEG signals and improves classification accuracy by using the context correlations of the EEG feature sequences. Specially, Stack AutoEncoder (SAE) is used to build and solve the linear EEG mixing model and the emotion timing model is based on the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The framework was implemented on the DEAP dataset for an emotion recognition experiment, where the mean accuracy of emotion recognition achieved 81.10% in valence and 74.38% in arousal, and the effectiveness of our framework was verified. Our framework exhibited a better performance in emotion recognition using multi-channel EEG than the compared conventional approaches in the experiments.
Highlights
Emotion has a great influence on human cognition (Yoo et al, 2014), behavior and communication
We present a novel framework for EEG emotion recognition, where Stack AutoEncoder (SAE) is used (Hinton and Salakhutdinov, 2006) to build the linear EEG mixing model and decompose the EEG source signals from the collected EEG signals
Decomposition Results To conduct the training of our linear EEG mixing model, minibatch gradient descent was used as the optimizer algorithm, which was an upgraded version of traditional stochastic gradient descent (SGD) and was generally used as the optimizer of the neural network
Summary
Emotion has a great influence on human cognition (Yoo et al, 2014), behavior and communication. Since emotion can reflect information of hobbies, personality, interests and even health, recognition of human emotions can help machines and robots in improving the reliability of human-machine interaction (Yin et al, 2017) and help them in action processing and social cognition (Urgen et al, 2013). Research on EEG-based automatic emotion recognition is very important and significance for brain-inspired robots and machines, as it enables them to read people’s interactive intentions and states through the wirelessly acquired EEG. Compared with facial expression (Zhang et al, 2016) and speech (Mao et al, 2014), emotion recognition based on physiological signals such as EEG, ECG (electrocardiogram), and EMG (electromyography) (Alzoubi et al, 2012; Chen et al, 2015a; Shu et al, 2018) are more objective and reliable. The main component of the EEG signals are brain rhythm signals from different brain regions, which reflect the activity of the region (Niedermeyer and da Silva, 2005)
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