Title :
Determining the Influence of Visual Training on EEG Activity Patterns Using Association Rule Mining
Author :
Yan, Fangfang ; Watter, Paul A. ; Wang, Wei
Author_Institution :
Dept. of Educ. Technol., Nanjing Normal Univ., Nanjing, China
Abstract :
To confirm that visual training can change EEG patterns by association rule mining method, firstly, we collected the EEG of people who are under a long-term visual professional training (visual training group) and novice people (control group) during a specific mental tasks. Secondly, we determined the difference of brain electrical activity between the two groups using machine learning methods. Thirdly, we discovered distinct patterns using association rule algorithm, finding that the two groups were separable based on their completion of visual professional cognitive tasks. In the beta band, visual training group showed a specific and significant association pattern which included FP1 and C4. The results indicate that the EEG patterns were modified because of visual professional training. We further discuss the impact of long-term visual professional training on the EEG.
Keywords :
bioelectric potentials; cognition; data mining; electroencephalography; learning (artificial intelligence); medical signal processing; training; vision; EEG activity patterns; EEG patterns; association pattern; association rule algorithm; association rule mining method; beta band; brain electrical activity; control group; distinct patterns; long-term visual professional training; machine learning methods; mental tasks; novice people; visual professional cognitive tasks; visual training group; Association rules; Brain; Educational institutions; Electroencephalography; Training; Visualization; EEG; association rule mining; brain development; professional training; visual training;
Conference_Titel :
Complexity and Data Mining (IWCDM), 2011 First International Workshop on
Conference_Location :
Nanjing, Jiangsu
Print_ISBN :
978-1-4577-2007-9
DOI :
10.1109/IWCDM.2011.23