In this paper, the comparison between K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) algorithm for classifying the spectrogram images in brain balancing is presented. After producing spectrogram image from ElectroEncephaloGram (EEG) signals, Gray Level Co-occurrence Matrix (GLCM) texture feature were extracted. These features produced huge matrices, therefore to reduce the size of matrices; the Principal Component Analysis (PCA) is applied. The results show that the KNN and ANN were able to classify the spectrogram image with 87.5% to 90% accuracy for the brain balancing application.