## 2 Related Work
### EEG Feature Extraction  
We extend the study in [12] and review a wide range of EEG feature extraction methods proposed in the past 10 years. As seen in Table 1, most previous EEG feature extraction methods only focused on the time and frequency dimensions, and rarely combined them with spatial dimension information.  
The time domain features study EEG signal through the variation of signal time series. The features include Hjorth features (Activity, Mobility, and Complexity [13; 14]), statistics features (Power, Mean, Standard Deviation, etc. [15]), High Order Crossing features (HOC [16; 17]), and so on. Time-domain features are not predominant. However, there are still many studies that have researched human emotion through time domain characteristics.  
The frequency domain features study EEG signal by transforming the raw time domain EEG signal into frequency domain EEG signal with Fourier Transform method usually. The most popular features in the frequency domain are power features of different sub-frequency bands known as alpha, beta, theta, and delta. The most widely used algorithm is the Fast Fourier Transform (FFT),which is applied in [18; 19; 20; 21; 22; 23; 24], and alternatives include Short-Time Fourier Transform (STFT) [25; 26; 27; 28]. Another frequency feature is Power Spectra Density (PSD), which is usually estimated by [PERSON]'s method [29].  
Since EEG signals are non-stationary, people proposed new methods combining time and frequency domain features to access additional information. The Hilbert-Huang Transform (HHT) is one method of studying EEG signals from both time and frequency domain. It decomposes the signal into Intrinsic Mode Functions (IMF) along with a trend, and obtains instantaneous frequency data. [PERSON] et al. extracted HHS-based energy as the EEG features to study the music liking of the subjects [32]. They found that time-frequency features were more resistant to noise than the STFT-based features, which only extracted frequency features. [PERSON] et al. used HHT to improve the extraction of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
* EEG, electroencephalographic; DE, density estimate; DWT, discrete wavelet transform; FFT, Fast Fourier transform; STFT, Short-time Fourier transform; HHS, Hilbert-Huang spectrum; PSD, power spectra density; ASM, asymmetry; DASM, differential asymmetry; RASM, rational asymmetry; DCAU, differential caudality. MFDFA, multifractal detrended fluctuation analysis; NLD, normalized length density; NSU, non-stationarity index.  
\end{table}
Table 1: A summary of feature extraction for emotion recognition from EEG 1.  
multi-scale entropy as the EEG emotional features [36], and their results demonstrated that the time-frequency combined feature obtained better results than the traditional single-domain features.  
EEG signals are obtained by measuring the electrical voltage signals of the multiple electrodes affixed to different positions on the scalp. From the obtaining method of the EEG signal, we can see that the information is highly correlated with the spatial, time, and frequency dimensions. However, seldom have previous studies paid attention to the spatial domain. The spatial information studies were limited to the asymmetry between the electrode pairs. The methods mostly calculate the differences in the power bands of the corresponding electrodes pairs on the left/right hemisphere of the scalp [21; 37]. Recently, [PERSON] et al. transformed EEG activities into a sequence of topology-preserving multi-spectral images to study human cognitive function [23], but few studies have analyzed human emotions with the spatial information of the EEG signals.  
The method to integrate EEG multidimensional features is based on the spatial distribution of EEG electrodes (according to the 10-20 system [38]), and map the frequency domain characteristics to a two-dimensional image. With this method, we obtain a sequence of images from consecutive time windows from the EEG signal. The details of the construction method are presented in Section 3.