Improved Feature Extraction of Hand Movement EEG Signals based on Independent Component Analysis and Spatial Filter
- Authors
- 응웬탄하; 박승민; 고광은; 심귀보
- Issue Date
- 2012
- Publisher
- 한국지능시스템학회
- Keywords
- Brain-Computer Interface (BCI); Electroencephalogram (EEG); Common Spatial Patterns (CSP); Independent Component Analysis; Auditory Stimuli
- Citation
- 한국지능시스템학회 논문지, v.22, no.4, pp 515 - 520
- Pages
- 6
- Journal Title
- 한국지능시스템학회 논문지
- Volume
- 22
- Number
- 4
- Start Page
- 515
- End Page
- 520
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/35487
- ISSN
- 1976-9172
- Abstract
- In brain computer interface (BCI) system, the most important part is classification of human thoughts in order to translate into commands. The more accuracy result in classification the system gets, the more effective BCI system is. To increase the quality of BCI system, we proposed to reduce noise and artifact from the recording data to analyzing data. We used auditory stimuli instead of visual ones to eliminate the eye movement, unwanted visual activation, gaze control. We applied independent component analysis (ICA) algorithm to purify the sources which constructed the raw signals. One of the most famous spatial filter in BCI context is common spatial patterns (CSP), which maximize one class while minimize the other by using covariance matrix. ICA and CSP also do the filter job, as a raw filter and refinement, which increase the classification result of linear discriminant analysis (LDA).
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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