# A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG
###### Abstract  
Electroencephalography (EEG)-based emotion recognition technologies can effectively help robots to perceive human behavior, which have attracted extensive attention in human-machine interaction (HMI). Due to the complexity of EEG data, current researchers tend to extract different types of hand-crafted features and connect all frequency bands for further study. However, this may result in the loss of some discriminative information of frequency band combinations and make the classification models unable to obtain the best results. In order to recognize emotions accurately, this paper designs a novel EEG-based emotion recognition framework using complementary information of frequency bands. First, after the features of the preprocessed EEG data are extracted, the combinations of all the adjacent frequency bands in different scales are obtained through permutation and reorganization. Subsequently, the improved classification method, homogeneous-collaboration-representation-based classification, is used to obtain the classification results of each combination. Finally, the circular multi-grained ensemble learning method is put forward to re-exact the characteristics of each result and merge the machine learning methods and simple majority voting for the decision fusion. In the experiment, the classification accuracies of our framework in arousal and valence on the DEAP database are 95.09% and 94.38% respectively, and that in the four classification problems on the SEED IV database is 96.37%. electroencephalogram; emotion recognition; homogeneous collaboration representation; circular multi-grained scanning; ensemble learning +
Footnote †: journal: Medical  
divided into five frequency bands based on the rhythmic characteristics. As the information interaction of brain waves is a cross-frequency coupling process among the frequency bands [10; 11], it is reasonable to take this interaction effect into account when establishing an EEG-based emotion recognition model. Additionally, from the perspective of statistical parametric maps (SPMs), the band energy of EEG signals has a certain correlation, and the frequency band beta is especially correlated with alpha [12]. This can prove that there is an interaction between different EEG frequency bands. To utilize the interacted information from different frequency bands, the emotion recognition framework that uses the combinations of all the adjacent frequency bands is designed to mine the complementary information as much as possible. The entire process is shown in Figure 1. In the data preparation process, the raw EEG signals are preprocessed for power spectrum density (PSD) extraction. After that, in the first step of the framework, the combinations of the adjacent frequency bands are considered as a subset, each subset is divided into a training subset and testing subset, respectively, and each training subset and the corresponding testing subset are both tested by the homogeneous-collaboration-representation-based classification (HCRC) method. In the second step, the testing results of all the training subsets and testing subsets are spliced together, respectively, to form a testing decision set and a training decision set. Then, the circular multi-grained ensemble learning (CGEL) method is used to complete the decision fusion and obtain the final classification result of the testing decision set. The main contributions of our work include:  
1. The CRC_RLS method is optimized to retain the same dimension of the representation coefficient in each category;
2. The CGEL decision fusion method is designed to improve the prediction accuracy;
3. An EEG-based classification framework HCRC-CGEL is constructed to utilize the complementary information from different frequency bands;
4. The experiments on two databases demonstrate the performance of the framework. The remaining chapters of this paper are arranged as described below. We review the different types of EEG features, current EEG-based emotion recognition methods, and decision fusion methods in Section 2.