# Long-Term EEG Component Analysis Method Based on Lasso Regression
## 5 Conclusions  
Aiming at the lack of any long-term analysis method of EEG signals, a long-term EEG component analysis method based on the Lasso regression analysis is proposed in this article. To consider the temporal variation of emotions, a cognitive experiment based on environmental sounds was designed, and the EEG data of 10 volunteers were collected. In total, 48 dimensional acoustic features and 428 EEG components were extracted, representing different emotional and psychological activities. Then, a new analysis method based on the time-varying characteristics of different spaces (acoustics, physiology) was proposed. The relationship between the time-varying brain component parameters and acoustic features could be found through this method. According to the temporal relations, an optimal offset of acoustic features was found, which enabled better alignment of EEG features. After the regression analysis, the significant EEG components were found, which were in good agreement with cognitive laws.  
The method proposed in this paper effectively solves the problem that existing analysis methods cannot be applied to long-term EEG components. The above work has achieved phased results and provides a basis for the study of auditory emotional cognition. In the future, more in-depth research will be carried out on auditory emotional cognition and calculation methods to explore the universal principles of affective computing. When experimental data is adequately accumulated, the classification model can be carried out. Then, an automatic emotion prediction and recognition system with an auditory and physiological response could be built.  
Conceptualization, H.B. and H.L. ([PERSON]); methodology, B.W.; validation, H.L. ([PERSON]) and L.M.; writing--original draft preparation, H.B.; writing--review and editing, H.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (U20A20838), Shenzhen Key Laboratory of Innovation Environment Project (ZDSYS20170731143710208), Funds for National Scientific and Technological Development (2019, 2021 Szxup087, 2021 Szxup088), Project funded by China Postdoctoral Science Foundation (2020T130431), Basic and Applied Basic Research of Guangdong (2019A151511179, 2021A1515011903), and Shenzhen Foundational Research Funding (JCYJ20180507183608379, JCYJ20200109150814370).  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of Heilongjiang provincial hospital (protocol code (2020)130 and 28 September 2020).  
Informed Consent Statement:Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy agreements.  
The authors declare no conflict of interest.  
Figure 14: The significant brain components selected by the Gaussian fitting. (**a,b**) are the IC#1 and #5 in the alpha band, and (**c**) is the IC#12 in the beta band.