EEG Based Network Connectivity Classification in 7 and 9 Years- Old Children
© 2018 IEEE. Investigating the brain neural pathways requires extensive knowledge of childrens' cognitive development. Significant variations in the cognitive process of a child, across ages, were assessed through the success in recognizing various stimuli. Longitudinal EEG data were gathered f...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/74730 |
| _version_ | 1848763356288122880 |
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| author | Almabruk, T. Tan, Tele Khan, Masood Mehmood |
| author_facet | Almabruk, T. Tan, Tele Khan, Masood Mehmood |
| author_sort | Almabruk, T. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2018 IEEE. Investigating the brain neural pathways requires extensive knowledge of childrens' cognitive development. Significant variations in the cognitive process of a child, across ages, were assessed through the success in recognizing various stimuli. Longitudinal EEG data were gathered from 45 healthy children at the ages of seven and nine years. During the EEG data acquisition, children were asked to respond to the Flanker stimuli for investigating the development of the response conflict process. In each age group, the coherence and imaginary component of coherency were used to assess the network connectivity of each child. The congruent and incongruent stimuli were tried within delta, theta, alpha and beta bands. Following that, efficacies of various classification algorithms were tested in discriminating the coherency data of the two age groups. It was observed that brain connectivity was more helpful in distinguishing between two age groups using the incongruent Flanker stimuli. For the incongruent condition, the imaginary part of the coherency provides better features for classification. Using the features derived from the theta, alpha and beta bands, a classification accuracy of more than 94.31% could be achieved using the naïve Bayes classifier. |
| first_indexed | 2025-11-14T11:02:09Z |
| format | Conference Paper |
| id | curtin-20.500.11937-74730 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:02:09Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-747302019-02-19T05:35:45Z EEG Based Network Connectivity Classification in 7 and 9 Years- Old Children Almabruk, T. Tan, Tele Khan, Masood Mehmood © 2018 IEEE. Investigating the brain neural pathways requires extensive knowledge of childrens' cognitive development. Significant variations in the cognitive process of a child, across ages, were assessed through the success in recognizing various stimuli. Longitudinal EEG data were gathered from 45 healthy children at the ages of seven and nine years. During the EEG data acquisition, children were asked to respond to the Flanker stimuli for investigating the development of the response conflict process. In each age group, the coherence and imaginary component of coherency were used to assess the network connectivity of each child. The congruent and incongruent stimuli were tried within delta, theta, alpha and beta bands. Following that, efficacies of various classification algorithms were tested in discriminating the coherency data of the two age groups. It was observed that brain connectivity was more helpful in distinguishing between two age groups using the incongruent Flanker stimuli. For the incongruent condition, the imaginary part of the coherency provides better features for classification. Using the features derived from the theta, alpha and beta bands, a classification accuracy of more than 94.31% could be achieved using the naïve Bayes classifier. 2018 Conference Paper http://hdl.handle.net/20.500.11937/74730 10.1109/EMBC.2018.8512187 restricted |
| spellingShingle | Almabruk, T. Tan, Tele Khan, Masood Mehmood EEG Based Network Connectivity Classification in 7 and 9 Years- Old Children |
| title | EEG Based Network Connectivity Classification in 7 and 9 Years- Old Children |
| title_full | EEG Based Network Connectivity Classification in 7 and 9 Years- Old Children |
| title_fullStr | EEG Based Network Connectivity Classification in 7 and 9 Years- Old Children |
| title_full_unstemmed | EEG Based Network Connectivity Classification in 7 and 9 Years- Old Children |
| title_short | EEG Based Network Connectivity Classification in 7 and 9 Years- Old Children |
| title_sort | eeg based network connectivity classification in 7 and 9 years- old children |
| url | http://hdl.handle.net/20.500.11937/74730 |