**5. Conclusion**

To recognize the mental tasks by EEG signals, two kinds of temporal-spatial frequency–based feature extraction methods were introduced in this chapter. In **Algorithm I**, event-related intervals of the raw EEG time series data (temporal information) was extracted at first, and the averaged power spectra of frequencies given by FFT within the interval (frequency information) were used as the discriminant features. In **Algorithm II**, event-related frequencies of EEG's FFT were extracted by ROC analysis with high AUCs. The input space for classifiers was composed by all features extracted by two algorithms from multiple channels, so the spatial information was also included in these feature extraction methods.

Pattern recognition of EEG signals has been studied for decades, and it plays an important role in the field of human robot interaction (HRI). So, we expect that the feature extraction methods introduced in this chapter can be adopted in the real HRI systems in the near future.
