Abstract
Electroencephalogram (EEG) related analyses have a vast extent of enquiries, preliminary from Brain-Computer Interfaces (BCI) on motor imagery tasks, neurotechnology, human-computer interaction, clinical and medical applications, sleep research, as well as the neural basis of emotional recognition and behavioural responses. This study purposely investigates the application of feature extraction methods from the emotion recognition arena against the motor imagery arena, using the electroencephalogram (EEG) data from the BCI Competition IV. As the same methods are used in both fields, binary classification has been embraced, emotion recognition’s valence and arousal, using its equivalent motor imageries of left and right classes. Essentially, six EEG feature extraction methods were used in the classification accuracy process, including statistical features, wavelet analysis, higher-order spectra (HOS), Hjorth, fractal dimension (Katz, Higuchi, Petrosian) and three-dimensional combination of fractal, wavelet and Hjorth. Further, the classifier performance methods were the Gaussian radial basis function RBF SVM (GSVM) and the regression tree (CART). Remarkably, the statistical method has better accuracy than the other feature sets, precisely the fractal dimension, which was the highest considering the emotion recognition span. Also, GSVM generally has better accuracy on the BCI IV dataset than CART. By contemplating the novel outcome notices, we can deduce that in the methods used in emotion recognition when applied to motor imagery, unique results are obtained, feature-wise and classifier-wise.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yuvaraj, R., Thagavel, P., Thomas, J., Fogarty, J., Ali, F.: Comprehensive analysis of feature extraction methods for emotion recognition from multichannel EEG recordings. Sensors 23(2), 915 (2023). https://doi.org/10.3390/s23020915
Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M.: A multimodal database for affect recognition and implicit tagging. IEEE Trans. Affect. Comput. 3(1), 42–55 (2012)
Koelstra, S., et al.: DEAP: a database for emotion analysis using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)
Zheng, W.L., Lu, B.L.: Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans. Auton. Ment. Dev. 7(3), 162–175 (2015)
Miranda-Correa, J.A., Abadi, M.K., Sebe, N., Patras, I.: AMIGOS: a dataset for affect, personality, and mood research on individuals and groups. IEEE Trans. Affect. Comput. 12(4), 479–493 (2017)
Katsigiannis, S., Ramzan, N.: DREAMER: a database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE J. Biomed. Health Inform. 22(1), 98–107 (2018)
Jenke, R., Peer, A., Buss, M.: Feature extraction and selection for emotion recognition from EEG. IEEE Trans. Affect. Comput. 5(3), 327–339 (2014)
Liu, Y., Sourina, O.: Real-time subject-dependent EEG-based emotion recognition algorithm. In: Gavrilova, M.L., Kenneth Tan, C.J., Mao, X., Hong, L. (eds.) Transactions on Computational Science XXIII: Special Issue on Cyberworlds, pp. 199–223. Springer Berlin Heidelberg, Berlin, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43790-2_11
Yuvaraj, R., et al.: Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson’s disease. Int. J. Psychophysiol. 94(3), 482–495 (2014)
Nawaz, R., Cheah, K.H., Nisar, H., Yap, V.V.: Comparison of different feature extraction methods for EEG-based emotion recognition. Biocybern. Biomed. Eng. 40(4), 910–926 (2020)
Liu, J., Meng, H., Li, M., Zhang, F., Qin, R., Nandi, A.K.: Emotion detection from EEG recordings based on supervised and unsupervised dimension reduction. Concurrency Comput. Pract. Exp. 30, e4466 (2018)
Katz, M.J.: Fractals and the analysis of waveforms. Comput. Biol. Med. 18(3), 145–156 (1998)
Hatamikia, S., Nasrabadi, A.M.: Recognition of emotional states induced by music videos based on nonlinear feature extraction and SOM classification. In: Proceedings of the 21st Iranian Conference on Biomedical Engineering (ICBME), Tehran, Iran, pp. 333–337 (2014)
Higuchi, T.: Approach to an irregular time series on the basis of the fractal theory. Physica D 31(1–2), 277–283 (1988). https://doi.org/10.1016/0167-2789(88)90081-4
Ang, K.K., Chin, Z.Y., Wang, C., Guan, C., Zhang, H.: Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front. Neurosci. 6, 39 (2012). https://doi.org/10.3389/fnins.2012.00039
Schlögl, A., et al.: BCI Competition 2008 – Graz data set A. Institute for Knowledge Discovery (Laboratory of Brain-Computer Interfaces), Graz University of Technology (2008). http://www.bbci.de/competition/iv/
Wang, X.W., Nie, D., Lu, B.L.: Emotional state classification from EEG data using machine learning approach. Neurocomputing 129, 94–106 (2014)
Hjorth, B.: EEG analysis based on time domain properties. Electroencephalogr. Clin. Neurophysiol. 29(3), 306–310 (1970)
Hjorth, B.: The physical significance of time domain descriptors in EEG analysis. Electroencephalogr. Clin. Neurophysiol. 34(3), 321–325 (1973)
Hosseini, S.A.: Classification of brain activity in emotional states using HOS analysis. Int. J. Image Graph. Sig. Process. 4(1), 21 (2012)
Hirano, K., Nishimura, S., Mitra, S.: Design of digital notch filters. IEEE Trans. Commun. 22(7), 964–970 (1974)
Hussin, S.F., Birasamy, G., Hamid, Z.: Design of butterworth band-pass filter. Politeknik Kolej Komuniti J. Eng. Technol. 1(1) (2016)
Youssef, A.: Image downsampling and upsampling methods. National Institute of Standards and Technology (1999)
Lemos, M.S., Fisch, B.J.: The weighted average reference montage. Electroencephalogr. Clin. Neurophysiol. 79(5), 361–370 (1991)
Yang, J., et al.: Parameter selection of Gaussian kernel SVM based on local density of training set. Inverse Prob. Sci. Eng. 29(4), 536–548 (2021)
Azuaje, F.: Witten IH, Frank E: data mining: practical machine learning tools and techniques. Biomed. Eng. Online 5, 51 (2006)
Diamantidis, N.A., Karlis, D., Giakoumakis, E.A.: Unsupervised stratification of cross-validation for accuracy estimation. Artif. Intell. 116(1–2), 1–16 (2000)
Nawaz, R., Cheah, K.H., Nisar, H., Yap, V.V.: Comparison of different feature extraction methods for EEG-based emotion recognition. Biocybern. Biomed. Eng. 40(3), 910–926 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mohamed, A.F., Jusas, V. (2024). Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition on Motor Imagery from Multichannel EEG Recordings. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Poniszewska-Marańda, A. (eds) Good Practices and New Perspectives in Information Systems and Technologies. WorldCIST 2024. Lecture Notes in Networks and Systems, vol 986. Springer, Cham. https://doi.org/10.1007/978-3-031-60218-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-60218-4_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-60217-7
Online ISBN: 978-3-031-60218-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)