Affective state recognition from EEG with deep belief networks

With the ultimate intent of improving the quality of life, identification of human´s affective states on the collected electroencephalogram (EEG) has attracted lots of attention recently. In this domain, the existing methods usually use only a few labeled samples to classify affective states consisting of over thousands of features. Therefore, important information may not be well utilized and performance is lowered due to the randomness caused by the small sample problem. However, this issue has rarely been discussed in the previous studies. Besides, many EEG channels are irrelevant to the specific learning tasks, which introduce lots of noise to the systems and further lower the performance in the recognition of affective states. To address these two challenges, in this paper, we propose a novel Deep Belief Networks (DBN) based model for affective state recognition from EEG signals. Specifically, signals from each EEG channel are firstly processed with a DBN for effectively extracting critical information from the over thousands of features. The extracted low dimensional characteristics are then utilized in the learning to avoid the small sample problem. For the noisy channel problem, a novel stimulus-response model is proposed. The optimal channel set is obtained according to the response rate of each channel. Finally, a supervised Restricted Boltzmann Machine (RBM) is applied on the combined low dimensional characteristics from the optimal EEG channels. To evaluate the performance of the proposed Supervised DBN based Affective State Recognition (SDA) model, we implement it on the Deap Dataset and compare it with five baselines. Extensive experimental results show that the proposed algorithm can successfully handle the aforementioned two challenges and significantly outperform the baselines by 11.5% to 24.4%, which validates the effectiveness of the proposed algorithm in the task of affective state recognition.