# A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet
## 2 Materials and Methods
### An Emotion Recognition Method from Multi-Band EEG Topology Maps Using ERENet
#### 2.3.1 Multi-Band EEG Topology Maps  
EEG signals are some multi-channel continuous time sequences with the format of \"channel number \(\times\) sampling time\", which are adopted as the input data format of the models in many studies [32, 35, 36]. The disadvantage of this data format is that the electrode measurements at different spatial locations in the cerebral cortex are only aggregated together and the topological structure of EEG signals is ignored. The brain is a complex system, the completion of tasks relies on the cooperation of various regions, and there is a certain internal correlation between the various regions. The spatial information of EEG signals contains significant information related to emotional states, which is of great significance in exploring the correlation between brain regions. Aiming to preserve the topological structure of EEG signals, multi-channel EEG signal sequences are converted into 2D topology maps, so that the signals adjacent to the physical channels are still adjacent in the 2D topology map. The international 10-20 system describes the location of scalp electrodes, in which \"10\" and \"20\" mean the actual distance between adjacent electrodes is 10% or 20% of the total distance between the front and back of the skull or the left and right sides. Figure 2a shows the spatial distribution of electrodes in the international 10-20 system, in which the electrodes marked with blue are the 32 electrodes used in the DEAP dataset. To address the loss of electrode topological location information, 32 electrodes used in the DEAP dataset are relocated to the 2D topological structure based on the spatial distribution of electrodes in the international 10-20 system. For each sampling point, 32-channel EEG signals are mapped to 9 \(\times\) 9 matrix, as shown in Figure 2b. At present, some studies have been conducted on the activity of emotional states in different frequency bands. [PERSON] et al. [42] pointed out that the alpha, beta, and gamma bands of the frontal lobe have relatively significant information to distinguish emotional states. In addition, [PERSON] et al. [43] observed that the features displayed in the theta band are closely related to arousal. Therefore, theta, alpha, beta, and gamma frequency bands are selected to research the emotional state characteristics of the human brain, and four 2D matrices are constructed to describe the significant information related to emotional states in four frequency bands. The construction of multi-band EEG topological maps is shown in Figure 3. The multi-band EEG topology maps can clearly represent the changes in EEG signals on the scalp under different emotional states and can provide more abundant information integrating the frequency domain features, spatial information, and frequency band characteristics of multi-channel EEG signals. Figure 1: The emotion recognition framework from multi-band EEG topology maps using ERENet. EEG signals are non-stationary signals with strong randomness, which cannot be analyzed by some traditional methods. If EEG signals are decomposed into several short signals, the signals in a short time can be approximately stationary. Therefore, the EEG segment \(x_{i}\in\mathbb{R}^{R}\) of each electrode can be regarded as a stationary signal after the samples are segmented by sliding window. Fast Fourier transform (FFT) is performed on \(x_{i}\in\mathbb{R}^{R}\) to extract the spectral power \(\left\{p_{i}^{\theta},p_{i}^{\alpha},p_{i}^{\beta},p_{i}^{\gamma}\right\}\) of theta, alpha, beta, and gamma bands as frequency domain features. The calculation of \(\left\{p_{i}^{\theta},p_{i}^{\alpha},p_{i}^{\beta},p_{i}^{\gamma}\right\}\) is shown in Equation (3):  
\[ \begin{array}{l}x(k)=FFT(x_{i}),\ k=0,1,\ldots,R-1,\\ p_{i}^{band}=\sum\limits_{j=band,FreqStart}^{End}\left|x\left(\frac{i\times R }{\mathrm{S}}\right)\right|,\ band=\theta,\alpha,\beta,\gamma,\end{array}  \tag{3}\]  
where the frequency bands of theta, alpha, beta, and gamma are 4-8 Hz, 8-13 Hz, 13-30 Hz, and 30-45 Hz, respectively.