Classification of human emotion from EEG using discrete wavelet transform

By M. Murugappan, Nagarajan Ramachandran, Y. Sazali. 2010

467 citations recorded by the source corpus.

In this paper, we summarize the human emotion recognition using different set of electroencephalogram (EEG) channels using discrete wavelet transform. An audio-visual induction based protocol has been designed with more dynamic emotional content for inducing discrete emotions (disgust, happy, surprise, fear and neutral). EEG signals are collected using 64 electrodes from 20 subjects and are placed over the entire scalp using International 10-10 system. The raw EEG signals are preprocessed using Surface Laplacian (SL) filtering method and decomposed into three different frequency bands (alpha, beta and gamma) using Discrete Wavelet Transform (DWT). We have used “db4” wavelet function for deriving a set of conventional and modified energy based features from the EEG signals for classifying emotions. Two simple pattern classification methods, K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA) methods are used and their performances are compared for emotional states classification. The experimental results indicate that, one of the proposed features (ALREE) gives the maximum average classification rate of 83.26% using KNN and 75.21% using LDA compared to those of conventional features. Finally, we present the average classification rate and subsets of emotions classification rate of these two different classifiers for justifying the performance of our emotion recognition system.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/33cf92dde9f5eac788ff091f013ed3e2dc5fab07

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Wei-Long Zheng
2. Bao-Liang Lu
3. Jia-Yi Zhu
4. Angelika Peer
5. Martin Buss
6. Robert Jenke
7. Dan Nie
8. Xiao-Wei Wang

In Signal Processing

1. Bao-Liang Lu
2. Martin Buss
3. Dan Nie
4. Xiao-Wei Wang
5. Wei-Long Zheng

Citations in the corpus

• Feature Extraction and Selection for Emotion Recognition from EEG (2014, background): “[7] K. Schaaff and T. Schultz,“Towards emotion recognition from electroencephalographic signals,”in Proc.”
• A Review of Emotion Recognition Using Physiological Signals (2018, methodology): “The reference work [98] decomposed the EEG signal with Db-4 wavelet into five levels.”
• Emotional state classification from EEG data using machine learning approach (2014, methodology): “[44] showed an emotion recognition approach using different set of EEG channels with a maximum accuracy of 83.”
• Identifying Stable Patterns over Time for Emotion Recognition from EEG (2016, background): “[9] Video 62 Wavelet features of alpha, beta and gamma, 20 participants, classification with KNN and LDA disgust, happy, surprise, fear and neutral 83.”