Using N400-Component for Automatic Thought Identification In a Natural Reading Task
(2011) KOGM11 20092Cognitive Science
- Abstract
- Recent advances in human brain imaging techniques have shown that it is possible to correctly identify thoughts associated with specific categorises of conceptual knowledge. A semantic space is created through word clustering from a corpus and cross-referenced with words read during a natural reading task while at the same time recording EEG. These results show that it is possible to correctly classify a words category 25% of the time (out of 12 possible categories) based on neural activity within the N400 component with the help of a artifical neural network. Results also visualises the ANN’s black-box behaviour in which electrodes it deem most contributing when classifying. These results contribute to the discussion on how the semantic... (More)
- Recent advances in human brain imaging techniques have shown that it is possible to correctly identify thoughts associated with specific categorises of conceptual knowledge. A semantic space is created through word clustering from a corpus and cross-referenced with words read during a natural reading task while at the same time recording EEG. These results show that it is possible to correctly classify a words category 25% of the time (out of 12 possible categories) based on neural activity within the N400 component with the help of a artifical neural network. Results also visualises the ANN’s black-box behaviour in which electrodes it deem most contributing when classifying. These results contribute to the discussion on how the semantic memory is stored in the brain; modularly or connectionisticly, and also how far it is possible to subcategories thoughts and still correctly identify them with brain imagining techniques. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/1971007
- author
- Emilsson, Johan LU
- supervisor
- organization
- course
- KOGM11 20092
- year
- 2011
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- EEG, N400, ANN, Artificial Neural Network, Word cluster, PCA, Semantic space, categorisation, reading task
- language
- English
- id
- 1971007
- date added to LUP
- 2011-06-23 16:25:28
- date last changed
- 2011-06-23 16:25:28
@misc{1971007, abstract = {{Recent advances in human brain imaging techniques have shown that it is possible to correctly identify thoughts associated with specific categorises of conceptual knowledge. A semantic space is created through word clustering from a corpus and cross-referenced with words read during a natural reading task while at the same time recording EEG. These results show that it is possible to correctly classify a words category 25% of the time (out of 12 possible categories) based on neural activity within the N400 component with the help of a artifical neural network. Results also visualises the ANN’s black-box behaviour in which electrodes it deem most contributing when classifying. These results contribute to the discussion on how the semantic memory is stored in the brain; modularly or connectionisticly, and also how far it is possible to subcategories thoughts and still correctly identify them with brain imagining techniques.}}, author = {{Emilsson, Johan}}, language = {{eng}}, note = {{Student Paper}}, title = {{Using N400-Component for Automatic Thought Identification In a Natural Reading Task}}, year = {{2011}}, }