Emotion perception in stroke patients is affected since there is abnormality in the brain. Here, researchers focused on the impact of left brain damage and right brain damage towards emotion recognition. Due to the impaired emotion recognition, it is a challenge for stroke patients to express themselves in daily communication. Hence, it is inspiring to see the possibility to predict patient’s emotional state so as to prevent recurrent stroke. In this work, electroencephalograph (EEG) of 19 left brain damage patients (LBD), 19 right brain damage patients (RBD) and 19 normal control (NC) are collected as database. During data collection, six emotions (sad, disgust, fear, anger, happy and surprise) are induced by using audio visual stimuli. After normalization, EEG signals are filtered by using Butterworth 6th order band-pass filter at the cut-off frequencies of 0.5�Hz and 49�Hz. Then, wavelet packet transform (WPT) technique is implemented to localize five frequency bands: alpha (8�Hz–13�Hz), beta (13�Hz–30�Hz), gamma (30�Hz–49�Hz), alpha-to-gamma (8�Hz–49�Hz), beta-to-gamma (13�Hz–49�Hz). In WPT, four wavelet families are chosen: daubechies 4 (db4), daubechies 6 (db6), coiflet 5 (coif5) and symmlet 8 (sym8). Hurst exponents are extracted from each band and wavelet family and are classified by using K-nearest Neighbour (KNN) and Probabilistic Neural Network (PNN). Two classifications are done: comparison between three groups and comparison between six emotions. The results showed that all the H values are anti-correlated (0�<�H�<�0.5). From classification, the best frequency band is beta band, where sad emotion recorded the accuracy of 82.32% for LBD group. Meanwhile, both sad and fear emotion recorded 0.89 sensitivity score in LBD and RBD respectively. Due to its overall poor performance, RBD is found to have greater impairment in emotion recognition.