Emotion recognition from multichannel EEG signals using K-nearest neighbor classification

@article{Li2018EmotionRF,
  title={Emotion recognition from multichannel EEG signals using K-nearest neighbor classification},
  author={Mi Li and Hongpei Xu and Xingwang Liu and Shengfu Lu},
  journal={Technology and Health Care},
  year={2018},
  volume={26},
  pages={509 - 519}
}
BACKGROUND: Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals. OBJECTIVE: This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels. METHODS: We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were… 

Figures and Tables from this paper

Analysis of EEG Based Emotion Detection of DEAP and SEED-IV Databases Using SVM

TLDR
A supervised machine learning algorithm is developed to recognize human inner emotion states in two-dimensional model and a proposed method to detect inner emotion-states is proposed.

Emotion Recognition from Multidimensional Electroencephalographic Signals on the Manifold of Symmetric Positive Definite Matrices

  • E. Abdel-GhaffarM. Daoudi
  • Computer Science
    2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)
  • 2020
TLDR
The aim of this study is to classify human emotions using Electroencephalographic (EEG) signals using a simple distance metric Log-Euclidean Riemannian Metric on a symmetric positive definite manifold (SPD).

A Comparative Analysis of Frequency Bands in EEG Based Emotion Recognition System

Computer Science department, Baniwalid University, Baniwalid, Libya, kf.aljribi@gmail.com Emotion recognition from brain signals has been interesting research area in computer science to help

EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach

TLDR
The proposed BoDF model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets, which is more accurate compared to other state-of-the-art methods of human emotion recognition.

EEG-Based Emotion Recognition by Exploiting Fused Network Entropy Measures of Complex Networks across Subjects

TLDR
The results fully demonstrate that a more accurate recognition of emotional EEG signals can be achieved relative to the available relevant studies, indicating that the AUC method can provide more generalizability in practical use.

EEG-Based Emotion Recognition with Combined Deep Neural Networks using Decomposed Feature Clustering Model

TLDR
An advanced signal processing method that uses the depth function to extract features from all channels related to emotion using a decomposed feature clustering model to decrease the computational cost of recognizing emotions and achieve better results.

Innovative Poincare's plot asymmetry descriptors for EEG emotion recognition.

TLDR
The experimental results indicated that kNN outperformed the other classifiers with a maximum accuracy of 95.49 and 98.63% using SEED-IV and DEAP datasets, respectively.

Emotion Recognition Using Three-Dimensional Feature and Convolutional Neural Network from Multichannel EEG Signals

TLDR
The experimental results proved that the 3D feature matrix can effectively represent the emotion-related features in multichannel EEG signals and the proposed CNN can efficaciously mine the unique features of each channel and the correlation among channels for emotion recognition.

Emotion Recognition Using the Fusion of Frontal 2-channel EEG Signals and Peripheral Physiological Signals

TLDR
The feasibility of using EEG channels in the hairless area of the prefrontal to recognize emotions is studied, and the innovatively proposed to use peripheral physiological signals to supplement the missing EEG channels for emotion recognition is used.
...

References

SHOWING 1-10 OF 26 REFERENCES

Wavelet-based emotion recognition system using EEG signal

TLDR
In this research, emotional states in arousal/valence dimensions have been classified using minimum number of channels and frequency bands of EEG signal and using the high-frequency bands yields higher accuracy compared to using low- frequencies.

EEG-based emotion recognition using discriminative graph regularized extreme learning machine

TLDR
The experimental results indicate that the EEG patterns for emotion are generally stable among different experiments and subjects, and GELM is more suitable for emotion recognition than SVM.

ReliefF-Based EEG Sensor Selection Methods for Emotion Recognition

TLDR
F-based channel selection methods were systematically investigated for EEG-based emotion recognition on a database for emotion analysis using physiological signals (DEAP), and the study of selecting subject-independent channels, related to emotion processing, was implemented.

EEG feature extraction for classifying emotions using FCM and FKM

TLDR
Results confirm the possibility of using wavelet transform based feature extraction for assessing the human emotions from EEG signal, and of selecting a minimal number of channels for emotion recognition experiment.

Emotion classification based on gamma-band EEG

  • Mu LiBao-Liang Lu
  • Computer Science
    2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
  • 2009
TLDR
The experimental results indicate that the gamma band (roughly 30–100 Hz) is suitable for EEG-based emotion classification, and a frequency band searching method to choose an optimal band into which the recorded EEG signal is filtered is proposed.

Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers

An evaluation of feature extraction in EEG-based emotion prediction with support vector machines

  • Itsara WichakamP. Vateekul
  • Computer Science
    2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE)
  • 2014
TLDR
The results showed that the prediction on EEG signals from 10 channels represented by the band power one-minute features gave the best accuracy and F1.1 showed promising accuracy.

Emotion recognition from EEG signals by using multivariate empirical mode decomposition

TLDR
A MEMD-based feature extraction method is proposed to process multichannel EEG signals for emotion recognition and the results are compared with similar previous studies for benchmarking.

The Research on Emotion Recognition from ECG Signal

TLDR
The feasibility of the method which sought the affective ECG features was shown and it was practicable to apply TS and fisher-KNN classifier for emotion recognition based on ECG signal.