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What is the optimal number of electrodes one can use in discrimination of tasks for a brain computer interface (BCI)? To address this question, the number and location of scalp electrodes in the acquisition of human electroencephalography (EEG) and discrimination of motor imagery tasks were optimized by using a systematic optimization approach. The systematic analysis results in the most reliable...
Current non-invasive brain-computer interface (BCI) designs use as much electroencephalogram (EEG) features as possible rather than few well known motor-reactive features (e.g. rolandic mu-rhythm picked from C3 and C4 channels). Additionally, motor-reactive rhythms do not provide BCI control for every subject. Thus, a subject-specific feature set needs to be determined from a large feature space....
The aim of this study is to compare 2 EEG pattern classification methods towards the development of BCI. The methods are: (1) discriminant stepwise, and (2) principal component analysis (PCA) - linear discriminant analysis (LDA) joint method. Both methods use Fisher's LDA approach, but differ in the data dimensionality reduction procedure. Data were recorded from 3 male subjects 20-30 years old. Three...
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