Determining the influence of visual training on EEG activity patterns using association rule mining

Fangfang Yan; Paul A. Watters; Wei Wang

doi:10.1109/IWCDM.2011.23

Determining the influence of visual training on EEG activity patterns using association rule mining

Fangfang Yan, Paul A. Watters, Wei Wang^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings: 2011 First International Workshop on Complexity and Data Mining
Subtitle of host publication	IWCDM 2011
Place of Publication	Piscataway, USA
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	64-67
Number of pages	4
ISBN (Electronic)	9780769546851
DOIs	https://doi.org/10.1109/IWCDM.2011.23
Publication status	Published - 2011
Externally published	Yes
Event	2011 1st International Workshop on Complexity and Data Mining, IWCDM 2011 - Nanjing, Jiangsu, China Duration: 24 Sept 2011 → 28 Sept 2011

Conference

Conference	2011 1st International Workshop on Complexity and Data Mining, IWCDM 2011
Country/Territory	China
City	Nanjing, Jiangsu
Period	24/09/11 → 28/09/11

Keywords

Association rule mining
Brain development
EEG
Professional training
Visual training

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10.1109/IWCDM.2011.23

Cite this

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title = "Determining the influence of visual training on EEG activity patterns using association rule mining",

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 = "Association rule mining, Brain development, EEG, Professional training, Visual training",

author = "Fangfang Yan and Watters, {Paul A.} and Wei Wang",

year = "2011",

doi = "10.1109/IWCDM.2011.23",

language = "English",

pages = "64--67",

booktitle = "Proceedings: 2011 First International Workshop on Complexity and Data Mining",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2011 1st International Workshop on Complexity and Data Mining, IWCDM 2011 ; Conference date: 24-09-2011 Through 28-09-2011",

}

Yan, F, Watters, PA & Wang, W 2011, Determining the influence of visual training on EEG activity patterns using association rule mining. in Proceedings: 2011 First International Workshop on Complexity and Data Mining: IWCDM 2011. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, USA, pp. 64-67, 2011 1st International Workshop on Complexity and Data Mining, IWCDM 2011, Nanjing, Jiangsu, China, 24/09/11. https://doi.org/10.1109/IWCDM.2011.23

Determining the influence of visual training on EEG activity patterns using association rule mining. / Yan, Fangfang; Watters, Paul A.; Wang, Wei.
Proceedings: 2011 First International Workshop on Complexity and Data Mining: IWCDM 2011. Piscataway, USA: Institute of Electrical and Electronics Engineers (IEEE), 2011. p. 64-67.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Determining the influence of visual training on EEG activity patterns using association rule mining

AU - Yan, Fangfang

AU - Watters, Paul A.

AU - Wang, Wei

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - Association rule mining

KW - Brain development

KW - EEG

KW - Professional training

KW - Visual training

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DO - 10.1109/IWCDM.2011.23

M3 - Conference proceeding contribution

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SP - 64

EP - 67

BT - Proceedings: 2011 First International Workshop on Complexity and Data Mining

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, USA

T2 - 2011 1st International Workshop on Complexity and Data Mining, IWCDM 2011

Y2 - 24 September 2011 through 28 September 2011

ER -

Determining the influence of visual training on EEG activity patterns using association rule mining

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