# Long-Term EEG Component Analysis Method Based on Lasso Regression
### Related Works of Emotional EEG Analysis  
Temporal signals carry emotional information. The research of dynamic emotion recognition attempts to identify an emotional state based on information before that moment, which is also the process of people's perception of emotions. [PERSON] et al. [8] found that the temporal structure of music is essential for musical emotion cognition. Based on the kernel Generalized Linear Model (kGLM), they obtained a lower classification error in both continuous emotions (valence, arousal) and discrete emotion recognition. [PERSON] et al. [9] obtained a better emotion prediction effect by extracting long-term semantic features. They built large-scale predictive models for moods in music based on the semantic layer projection (SLP) technique, and obtained prediction rates from moderate (R2 = 0.248 for happy) to considerably high (R2 = 0.710 for arousal). [PERSON]'s research team found a higher emotion recognition rate by merging frame-based timing information and long-term global features [10]. They proposed a global-control Elman (GC-Elman) network to combine utterance-based features and segment-based features together and obtained a recognition rate for anger, happiness, sadness, and surprise of 66.0%. These studies have proved that there are emotion-related cues in time-series information, which is of great help to emotion prediction. Then question, then, is how to describe emotional changes in long-term brain activity. The EEG is a complex non-stationary random signal which is easily disturbed by the external environment and subject movement [11]. At present, the most commonly used methods of EEG analysis are event-related processing methods such as event-related potentials (ERP), event-related synchronization (ERS), and event-related desynchronization (ERD) [12]. The principle is that the specific stimulus could cause regular potential changes in the brain. Therefore, neural activity in the cognitive process could be measured through the electrophysiological changes of the brain [13]. [PERSON] et al. [14] successfully extracted the N1 and P2 components caused by music through the ERP technique. The potential changes are similar to the same type of stimulus, while the noise in response is random. Therefore, the ERP component could be obtained, and the random noise balanced out, by large amounts of superposition and averages of EEG, as shown in Figure 1. The horizontal axis represents time and the vertical axis represents voltage amplitude. Furthermore, by analyzing the contribution of the ERP component, the EEG component that has the largest contribution could be found. The numbers (1, 3, 4, 7, 8, 11, and 13) on the topographic maps are the index of the independent components of EEG signals, which were obtained by Independent Component Analysis. At present, most EEG signal processing methods are aimed at short-term brain activities, and there are few analysis methods for long-term EEG response. Among many analysis methods, the power spectral density analysis and Auto-Regressive (AR) model estimation are frequently used. Power Spectral Density (PSD) represents a power change with a frequency, that is, the distribution of signal power in the frequency domain. Generally, the nonparametric estimation, based on Fourier transform, is used for PSD calculation. The power spectrum estimation method proposed by [PERSON] (1955) is widely used. [PERSON] et al. combined PSD and brain topographic maps for visualizing energy changes of different frequency bands of long-term EEG ([PERSON] et al., 2016). The band energies of EEG signals were calculated with sliding windows and plotted on brain topographic maps in chronological order. With music playing, the change process of brain activity could be dynamically displayed and measured. The Auto-Regressive (AR) Model ([PERSON] et al., 2016) is a linear regression model that combines previous information to describe a random variable at a specific time. It is essentially a linear prediction. [PERSON] et al.