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Although face-to-face communication includes the richest information, amyotrophic lateral sclerosis patients cannot smoothly communicate with others and express their emotions because of paralyzed muscles. Therefore, the N170 responses of EEG signals were analyzed to detect face stimuli in real time. We also proposed an analytical method for feature extraction of a support vector machine (SVM) classifier with the bag of features scheme to overcome the general difficulty in setting of kernel parameters of SVM. The proposed method resulted in a constantly high accuracy in the face classification; the SVM classifier based on image pattern recognition in the time frequency domain efficiently enables easier setting of the non-linear kernel parameter. Further studies will be required to apply the proposed method for feature extraction to practical devices.