Emotion Analysis for Personality Inference from EEG Signals

Aim:

             To design a MATLAB Model for Individual’s Emotion detection through computed EEG using DEAP dataset and making the people to relax by playing music spontaneously

Abstract:

               Emotion plays an important role in our daily life and work. Real-time assessment and regulation of emotion will improve people’s life and make it better. For example, in the communication of human-machine-interaction, emotion recognition will make the process more easy and natural. Another example, in the treatment of patients, especially those with expression problems, the real emotion state of patients will help doctors to provide more appropriate medical care. In recent years, emotion recognition from EEG has gained mass attention. Also it is a very important factor in brain computer interface (BCI) systems, which will effectively improve the communication between human and machines. Various features and extraction methods have been proposed for emotion recognition from EEG signals, including time domain techniques, frequency domain techniques, joint time-frequency analysis techniques, and other strategies. Here we proposed a reliable method which efficient in detection of individual’s health This design can detect the condition of the individual person using image classifier and EEG analyzer using adaptive threshold detection method.

Proposed System:

                  In this system, emotions are predicted by EEG signal acquired from brain waves. This waves are grouped in the form of alpha, beta, delta, and so on. Later this are splitted and processed to find feelings of individuality. Then if suppose person is in depressed state are relaxed by playing music and in joy state person are maintained in that state for prolonged time.

Advantages of Proposed System:

Three dimensional condition Detection

Emotion recognition will help us to get accurate info.

Emotion in depressed state are recovered by relaxing mode.