Meaning based covert speech classification for brain-computer interface based on electroencephalography
- Authors
- Kim, Taekyung; Lee, Jeyeon; Choi, Hoseok; Lee, Hojong; Kim, In-Young; Jang, Dong Pyo
- Issue Date
- Jan-2014
- Publisher
- IEEE
- Citation
- International IEEE/EMBS Conference on Neural Engineering, NER, pp 53 - 56
- Pages
- 4
- Indexed
- SCOPUS
- Journal Title
- International IEEE/EMBS Conference on Neural Engineering, NER
- Start Page
- 53
- End Page
- 56
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/160983
- DOI
- 10.1109/NER.2013.6695869
- ISSN
- 1948-3546
- Abstract
- In this study, we investigated that whether covertly spoken words with different meaning are discriminable during electroencephalography (EEG) recording. Neural activities were recorded from 30 channel 10-20 system electrodes. By employing a paired T-test, we briefly identify the difference in spatio-spectro-temporal characteristics between two categories of meaning (number and face). EEG features were then classified by support vector machine. On average, 71.69% of the trials were correctly classified. After extract optimized features using support vector machine based recursive feature elimination, the accuracy was improved up to 92.46%. Our preliminary results shed light on the construction of meaning based speech brain-computer interface.
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