Human emotion classification using wavelet transform and KNN

M. Murugappan

90 citations0/0 files storedInternational Conference on Pattern Analysis and Intelligence Robotics (ICPAIR)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Emotion and Mood Recognition EEG and Brain-Computer Interfaces Face and Expression Recognition

Abstract

Emotion is one of the most important features of humans. Without the ability of emotions processing, computers and robots cannot communicate with human in natural way. In this paper we presented the classification of human emotions using Electroencephalogram (EEG) signals. EEG signals are collected from 20 subjects through 62 active electrodes, which are placed over the entire scalp based on International 10-10 system. An audio-visual (video clips) stimuli based protocol has been designed for evoking the discrete emotions. The raw EEG signals are preprocessed through Surface Laplacian filtering method and decomposed into five different EEG frequency bands (delta, theta, alpha, beta and gamma) using Wavelet Transform (WT). We have considered three different wavelet functions namely: “db4”, “db8”, “sym8” and “coif5” for extracting the statistical features from the preprocessed signal. In this work, we have investigated the efficacy of emotion classification for two different set of EEG channels (62 channels & 24 channels). The validation of statistical features is performed using 5 fold cross validation and classified by using linear non-linear (KNN - K Nearest Neighbor) classifier. KNN gives a maximum average classification rate of 82.87 % on 62 channels and 78.57% on 24 channels, respectively. Finally we present the average classification accuracy and individual classification accuracy of KNN for justifying the performance of our emotion recognition system.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 90 citations exist upstream.

References

2001Emotion recognition in human-computer interaction 2004Emotion recognition system using short-term monitoring of physiological signals 2007Valence-arousal evaluation using physiological signals in an emotion recall paradigm 2008Time-Frequency Analysis of EEG Signals for Human Emotion Detection

Cited by

2012Emotion Recognition Using the Emotiv EPOC Device 2013EEG-based emotion recognition using Recurrence Plot analysis and K nearest neighbor classifier 2018A Systematic Review for Human EEG Brain Signals Based Emotion Classification, Feature Extraction, Brain Condition, Group Comparison 2018Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals 2018Emotion classification using EEG signals based on tunable‐ Q wavelet transform 2019Recognition of emotional states using EEG signals based on time-frequency analysis and SVM classifier 2022Deep Learning and Machine Learning based Facial Emotion Detection using CNN 2018Emotion classification using flexible analytic wavelet transform for electroencephalogram signals

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.