Affective computing on machine learning-based emotion recognition using a self-made eeg device

Ngoc Dau Mai, Boon Giin Lee, Wan Young Chung

Research output: Journal PublicationArticlepeer-review

1 Citation (Scopus)

Abstract

In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-electrode placement on the scalp; two of these electrodes were placed in the frontal lobe, and the other six electrodes were placed in the temporal lobe. We performed experiments on eight subjects while they watched emotive videos. Six entropy measures were employed for extracting suitable features from the EEG signals. Next, we evaluated our proposed models using three popular classifiers: a support vector machine (SVM), multi-layer perceptron (MLP), and one-dimensional convolutional neural network (1D-CNN) for emotion classification; both subject-dependent and subject-independent strategies were used. Our experiment results showed that the highest average accuracies achieved in the subject-dependent and subject-independent cases were 85.81% and 78.52%, respectively; these accuracies were achieved using a combination of the sample entropy measure and 1D-CNN. Moreover, our study investigates the T8 position (above the right ear) in the temporal lobe as the most critical channel among the proposed measurement positions for emotion classification through electrode selection. Our results prove the feasibility and efficiency of our proposed EEG-based affective computing method for emotion recognition in real-world applications.

Original languageEnglish
Article number5135
JournalSensors
Volume21
Issue number15
DOIs
Publication statusPublished - 1 Aug 2021

Keywords

  • Affective computing
  • Electroencephalogram (EEG)
  • Emotion recognition
  • Entropy measures
  • Multi-layer perceptron (MLP)
  • One-dimensional convolutional neural network (1D-CNN)
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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