A Novel Method to Detect Inner Emotion States of Human using Artificial Neural Networks
Thejaswini S1, K M Ravi Kumar2
1Thejaswini S*, Electronics and Telecommunication Engineering, BMS Institute of Technology and Management, Affiliated to VTU, Bangalore, India.
2Dr. K M Ravi Kumar, Principal and Professor Department of Electronics & Communication, Engineering, S J C Institute of Technology, Affiliated to VTU, Chickballapur, India.
Manuscript received on February 28, 2020. | Revised Manuscript received on March 22, 2020. | Manuscript published on March 30, 2020. | PP: 5820-5825 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9588038620/2020©BEIESP | DOI: 10.35940/ijrte.F9588.038620
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Human computer interaction is a fast growing area of research where in the physiological signals are used to identify human emotion states. Identifying emotion states can be done using various approaches. One such approach which gained interest of research is through physiological signals using EEG. In the present work, a novel approach is proposed to elicit emotion states using 3-D Video-audio stimuli. Around 66 subjects were involved during data acquisition using 32 channel Enobio device. FIR filter is used to preprocess the acquired raw EEG signals. The desired frequency bands like alpha, delta, beta and theta are extracted using 8-level DWT. The statistical features, Hurst exponential, entropy, power, energy, differential entropy of each bands are computed. Artificial Neural network is implemented using Sequential Keras model and applied on the extracted features to classify in to four classes (HVLA, HVHA, LVHA and LVLA) and eight discrete emotion states like clam, relax, happy, joy, sad, fear, tensed and bored. The performance of ANN classifier found to perform better for 4- classes than 8-classes with a classification rate of 90.835% and 74.0446% respectively. The proposed model achieved better performance rate in detecting discrete emotion states. This model can be used to build applications on health like stress / depression detection and on entertainment to build emotional DJ.
Keywords: DWT, EEG, Emotion states, ANN, Keras.
Scope of the Article: High Performance Computing.