Electroencephalograph based Human Emotion Recognition Using Artificial Neural Network and Principal Component Analysis

Abstract

In recent decades, automatic human emotion detection plays a crucial role in human and machine interaction. Electroencephalograph (EEG) based human emotion detection is a challenging process due to the diversity, and complexity of human emotions. For recognizing diverse emotions, a novel model is presented in this paper. Initially, an average mean reference technique is used to eliminate the environmental artifacts, instrumentation artifacts, and biological artifacts from the EEG signals, which are collected from DEAP dataset. Next, feature extraction is carried out using Fast Fourier transform (FFT) with Power Spectral Density (PSD) to extract feature vectors from the denoised EEG signals. Further, feature dimensionality reduction is performed utilizing Principal Component Analysis (PCA) to diminish the dimensions of the extracted features. A total of 230 EEG feature vectors are given as the input to Artificial Neural Network (ANN) for classifying valence and arousal emotion states. The proposed PCA-ANN model performance is validated in terms of average classification accuracy and f-score. The experimental outcome demonstrates that the proposed PCA-ANN model achieved an improved accuracy in emotion classification, which is effective compared to the existing models such as ensemble learning algorithm, a convolutional neural network with the statistical method, and sparse autoencoder with logistic regression. The proposed PCA-ANN model achieved 87.14% and 86.31% of accuracy in valence and arousal states, and obtained 90.45% and 92.03% of f-score value in valence and arousal emotion states.

KEYWORDS:

• Artificial neural network
• Average mean reference
• Electroencephalograph
• Emotion classification
• Fast Fourier transform
• Principal component analysis
• Power Spectral Density

Additional information

Notes on contributors

Satyanarayana Naga V. Kanuboyina

Satyanarayana Naga V Kanuboyina is presently working as an assistant professor in Department of Electronics and Communication Engineering, SRKR Engineering College, Bhimavaram, A P, India. He is currently pursuing PhD from Annamalai University. His current research interests include image processing, signal processing and internet of things.

Shankar T

T Shankar, received his BE, ME and doctoral degree from Annamalai University, Chidambaram, Tamil Nadu, India. At present, he is working as an assistant professor in the Department of Electronics and Communication Engineering at Government College of Engineering, Srirangam, Tamil Nadu, India. He published 28 research articles in various international journals. His area of interest is image processing. E-mail: [email protected]

Rama Raju Venkata Penmetsa

Rama Raju Venkata Penmetsa is presently working as a professor of Department of Electronics and Communication Engineering, SRKR Engineering College, AP, India. His research interests include biomedical signal processing, signal processing, image processing, VLSI design, antennas and microwave anechoic chambers design. He is the author of several research studies published in national and international journals and conference proceedings. E-mail: [email protected]