Heuristic Feature Extraction Method for BCI with Harmony Search and DiscreteWavelet Transform

Heuristic Feature Extraction Method for BCI with Harmony Search and DiscreteWavelet Transform

For the brain-computer interface system (BCI), pre-processing has an important role to ensure systemperformance. However, the speech recognition system using electroencephalogram (EEG) is weak against temporaleffects. Therefore, in general cases, wavelet transform has been used to cope with the temporal effects and nonstationarycharacteristic of EEG. The discrete version of wavelet transform, called DWT, requires a filter of thesystem for use in downsampling the signal. In other words, it is important to determine the suitable type of filter. Inmany cases, it is difficult to find an adequate filter for DWT because of differences in the characteristics of the inputsignal. In this paper, we proposed a heuristic approach to finding the optimal filter of the system for EEG signals. The harmony search algorithm (HSA) was used for finding of the optimal filter. In the learning process with theEEG system, the optimal wavelet filter could be found, which is automatically designed for subject personality.

For the brain-computer interface system (BCI), pre-processing has an important role to ensure systemperformance. However, the speech recognition system using electroencephalogram (EEG) is weak against temporaleffects. Therefore, in general cases, wavelet transform has been used to cope with the temporal effects and nonstationarycharacteristic of EEG. The discrete version of wavelet transform, called DWT, requires a filter of thesystem for use in downsampling the signal. In other words, it is important to determine the suitable type of filter. Inmany cases, it is difficult to find an adequate filter for DWT because of differences in the characteristics of the inputsignal. In this paper, we proposed a heuristic approach to finding the optimal filter of the system for EEG signals. The harmony search algorithm (HSA) was used for finding of the optimal filter. In the learning process with theEEG system, the optimal wavelet filter could be found, which is automatically designed for subject personality.