Human disposition detection using EEG signals

The psychological disorders are generally appears in society. The prediction of such disorder is necessary in day to day life. Electroencephalogram (EEG) signal is neuronal activity of brain. A brain signal plays an important role for human disposition detection. EEG signals are non-linear in nature. In this paper, EEG signals are classified into four emotional states such as happy, angry, cry and sad. For development of such system the database which has been collected from the age group of 20 to 25 year old male and female both. In this system the wavelet transform used to measure statistical parameters. These parameters have been used to discriminate input signal based on the features, such as mean, mode, median, skewness, kurtosis etc. The Manhattan distance matrix concept of Hidden Markov Model has been used in this work to classify various dispositions. The Manhattan distance matrix compares trained dataset with input EEG signal. During experimentation on the EEG signal, it is observed that the various emotions like happy, angry, cry and sad are having unique and distinct feature set. The result indicates that signals bring more distinction between various dispositions from the EEG signals.