Heuristic feature extraction method for BCI with harmony search and discrete wavelet transform

For the brain-computer interface system (BCI), pre-processing has an important role to ensure system performance. However, the speech recognition system using electroencephalogram (EEG) is weak against temporal effects. Therefore, in general cases, wavelet transform has been used to cope with the temporal effects and non-stationary characteristic of EEG. The discrete version of wavelet transform, called DWT, requires a filter of the system for use in downsampling the signal. In other words, it is important to determine the suitable type of filter. In many cases, it is difficult to find an adequate filter for DWT because of differences in the characteristics of the input signal. 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 the EEG system, the optimal wavelet filter could be found, which is automatically designed for subject personality.