KLASIFIKASI EMOSI BERDASARKAN SINYAL EEG MENGGUNAKAN TRANSFORMASI WAVELET DAN K-NEAREST NEIGHBOR; EMOTION CLASSIFICATION BASED ON EEG SIGNAL USING WAVELET TRANSFORM AND K-NEAREST NEIGHBOR

GHALEB, FAJRUL, Agfianto Eko Putra

2016 | Skripsi | FMIPA

In the area of affective computing technology, the classification of emotions can be used for a variety of things such as health, entertainment, education, etc. This study determined the classification of emotions based on EEG signals. EEG signals from the human brain is the result of various activities undertaken, one of which is human emotion. Emotions are classified according to 2-dimensional graphic modeling of arousal and valence. This study uses a wavelet decomposition method to get features from EEG signal. Features taken from the signal is a power signal decomposition of sub-band theta, alpha, beta, and gamma. This feature is derived from the 5 levels decomposition using Coiflet 2 and Daubechies 2 mother wavelet. Classification is done using k-Nearest Neighbor (kNN) with the closest neighbor calculated based on correlation distance. Data validation is done using 5-folds cross validation for validation of test data and training data. Highest accuracy obtained by using the mother wavelet Coiflet 2 with kNN parameter k = 21. Valence classification accuracy is 57.5%, and accuracy of arousal is 63.98%.

Kata Kunci : Classification, Emotion, Wavelet, kNN