Principal component analysis implementation for brainwave signal reduction based on cognitive activity

(1) * Ahmad Azhari Mail (Universitas Ahmad Dahlan, Indonesia)
(2) Murein Miksa Mardhia Mail (Universitas Ahmad Dahlan, Indonesia)
*corresponding author

Abstract


Human has the ability to think that comes from the brain. Electrical signals generated by brain and represented in wave form.  To record and measure the activity of brainwaves in the form of electrical potential required electroencephalogram (EEG). In this study a cognitive task is applied to trigger a specific human brain response arising from the cognitive aspect.  Stimulation is given by using nine types of cognitive tasks including breath, color, face, finger, math, object, password thinking, singing, and sports. Principal component analysis (PCA) is implemented as a first step to reduce data and to get the main component of feature extraction results obtained from EEG acquisition. The results show that PCA succeeded reducing 108 existing datasets to 2 prominent factors with a cumulative rate of 65.7%. Factor 1 (F1) includes mean, standard deviation, and entropy, while factor 2 (F2) includes skewness and kurtosis.

Keywords


EEG;PCA;Brainwave;Cognitive Activity;Pattern Recognition

   

DOI

https://doi.org/10.26555/ijain.v3i3.118
      

Article metrics

Abstract views : 7085 | PDF views : 368

   

Cite

   

Full Text

Download

References


J. Katona, I. Farkas, T. Ujbanyi, P. Dukan, and A. Kovari, “Evaluation of the NeuroSky MindFlex EEG headset brain waves data,” in Applied Machine Intelligence and Informatics (SAMI), 2014 IEEE 12th International Symposium on, 2014, pp. 91–94.

G. Rebolledo-Mendez et al., “Assessing neurosky’s usability to detect attention levels in an assessment exercise,” in International Conference on Human-Computer Interaction, 2009, pp. 149–158.

N. Inc., Brainwave Signal (EEG) of Neurosky, Inc. Neurosky, Inc., 2009.

J. Klonovs, C. K. Petersen, H. Olesen, and J. S. Poulsen, “Development of a Mobile EEG-Based Feature Extraction and Classification System for Biometric Authentication,” Aalborg University Copenhagen, 2012.

A. Azhari and L. Hernandez, “Brainwaves feature classification by applying K-Means clustering using single-sensor EEG,” Int. J. Adv. Intell. Inform., vol. 2, no. 3, pp. 167–173, Nov. 2016.

M. Diykh, Y. Li, and P. Wen, “EEG Sleep Stages Classification Based on Time Domain Features and Structural Graph Similarity,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 24, no. 11, pp. 1159–1168, Nov. 2016.

Z. H. Murat, M. N. Taib, S. Lias, R. S. S. A. Kadir, N. Sulaiman, and M. Mustafa, “EEG Analysis for Brainwave Balancing Index (BBI),” 2010, pp. 389–393.

R. Zafar, A. S. Malik, H. U. Amin, N. Kamel, S. Dass, and R. F. Ahmad, “EEG spectral analysis during complex cognitive task at occipital,” in Biomedical Engineering and Sciences (IECBES), 2014 IEEE Conference on, 2014, pp. 907–910.

A. Patil, C. Deshmukh, and A. R. Panat, “Feature extraction of EEG for emotion recognition using Hjorth features and higher order crossings,” in Advances in Signal Processing (CASP), Conference on, 2016, pp. 429–434.

I. Jayarathne, M. Cohen, and S. Amarakeerthi, “BrainID: Development of an EEG-based biometric authentication system,” in Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2016 IEEE 7th Annual, 2016, pp. 1–6.

B. H. Tan, “Using a low-cost eeg sensor to detect mental states,” Carnegie Mellon University, 2012.

P. Ackermann, C. Kohlschein, J. Á. Bitsch, K. Wehrle, and S. Jeschke, “EEG-based automatic emotion recognition: Feature extraction, selection and classification methods,” in e-Health Networking, Applications and Services (Healthcom), 2016 IEEE 18th International Conference on, 2016, pp. 1–6.

Y. Zhong and Z. Jianhua, “Recognition of Cognitive Task Load levels using single channel EEG and Stacked Denoising Autoencoder.pdf,” in Proceedings of the 35th Chinese Control Conference, 2016, pp. 3907–3912.

A. Mohamed, K. B. Shaban, and A. Mohamed, “Directed graph-based wireless EEG sensor channel selection approach for cognitive task classification,” in Wireless Communications and Mobile Computing Conference (IWCMC), 2016 International, 2016, pp. 176–181.

Z. Pang, J. Li, H. Ji, and M. Li, “A new approach for EEG feature extraction for detecting error-related potentials,” in Progress in Electromagnetic Research Symposium (PIERS), 2016, pp. 3595–3597.

A. Saha, A. Konar, P. Das, B. S. Bhattacharya, and A. K. Nagar, “Data-point and feature selection of motor imagery EEG signals for neural classification of cognitive tasks in car-driving,” in 2015 International Joint Conference on Neural Networks (IJCNN), 2015, pp. 1–8.

M. Varela, “Raw EEG signal processing for BCI control based on voluntary eye blinks,” in 2015 IEEE Thirty Fifth Central American and Panama Convention (CONCAPAN XXXV), 2015, pp. 1–6.

B. Johnson, T. Maillart, and J. Chuang, “My thoughts are not your thoughts,” 2014, pp. 1329–1338.

R. Chai et al., “Classification of EEG based-mental fatigue using principal component analysis and Bayesian neural network,” in Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the, 2016, pp. 4654–4657.

J. Braeken and M. A. L. M. van Assen, “An empirical Kaiser criterion.,” Psychol. Methods, vol. 22, no. 3, pp. 450–466, Sep. 2017.




Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
   andri.pranolo.id@ieee.org (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0