International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 02 Issue: 03 | June-2015 www.irjet.net p-ISSN: 2395-0072 © 2015, IRJET.NET- All Rights Reserved Page 2309 DzEFFCIENT HUMAN EMOTION RETRIEVAL USING BCI AND SPEECHdz Priyanka A. Wandile 1 , Mr. Rahul Hiware 2 , Dr.Narendra Bawane 3 1 Student, Department of Electronic Engineering, S. B. Jain Institute Of Technology Management & Research, Maharashtra, India 2 Lecturer, Department of Electronic Engineering, S. B. Jain Institute Of Technology Management & Research, Maharashtra, India 3 Principal, Department of Electronic Engineering, S. B. Jain Institute Of Technology Management & Research, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract -This paper reports on the human emotion reco- gnition using different set of electroencephalogram (EEG) channels using discrete wavelet transform . Emotion recognition could be done from the text, speech, facial. In the past few days, many studies have been done on emotion recognition. Anderson utilized facial expressions to recognize emotion . However, these signals shared the same disadvantage. They are not reliable or perfection is not theirto detect emotion, especially when people want to conceal their feelings. In this paper, The EEG-based emotion recognition algorithm based on spectral features and neural network classifiers is proposed. In this algorithm, spectral, spatial and temporal features are selected from the emotion-related EEG signals by applying wavelet transform. we concentrate on recognition of Dzinnerdz emotions from electroencephalogram (EEG) signals as humans could control their facial expressions or vocal intonation. We observe the different brain position as Left Hemisphere and Right Hemisphere to recognize the significance according to different moods. The powers of alpha are more alert during National, Happy, Romantic mood as compared to Sad mood. We have used Dzdb6dz wavelet function for deriving a set of conventional and modified energy based features from the EEG signals for classifying emotions. Key Words: EEG, Human Emotion, Discrete Wavelet Transform, Speech etc… 1. INTRODUCTION A brain–computer interface (BCI) is a system that can be communicated in between brain and a computer by which a person can send messages without any use of peripheral nerves and muscles. The ability to effectively classify electroencephalograms (EEG) is the basic building block for Brain-Computer Interfaces. In this proposed work EEG signals will be used to specified the extract data and classify with different variety of human emotions using speech. Emotion is most important for humans. It is not only contributes to communication between humans, but also plays a critical role in rational and intelligent behavior. Its functions can be seen in many aspects of our daily lives. It is needful system in human. Thus, the study of emotion recognition is indispensable. The system defined a mechanism of quantification of basic emotions using emotion model. In this study we show that it is possible to recognize the different moods of person using EEG signal. We observe the different brain locations as Left Hemisphere and Right Hemisphere to recognize the significance according to different moods like (happy ,sad ,surprise ,anger) The powers of alpha are more exciting for during National, Happy, Romantic mood as compared to Sad mood. So it is possible to distinguish these different moods using alpha power values. The distance matrices also show that it is possible to differentiate the emotions of persons using alpha power values. Traditionally, EEG- based technologies were used only in medical applications like epilepsy and seizures .EEG used in many sophisticated place that given to perfect result.In the past few decades, many studies have been done on emotion recognition. Anderson and Mc Owan utilized facial expressions to recognize emotion . Ang and colleagues did emotion recognition based on prosody. However, these signals shared the same disadvantage. They are not reliable to detect emotion, especially when people want to conceal their feelings. In recent years, more and more researchers have started to use EEG signals in recognizing emotion because they are reliable. 2. RESEARCH METHODOLOGY 2.1. EEG Data Acquisition A good database is highly essential for developing intelligent emotion recognition system in EEG. There is no