Brainwaves feature classification by applying K-Means clustering using single-sensor EEG

(1) * Ahmad Azhari Mail (Universitas Ahmad Dahlan, Indonesia)
(2) Leonel Hernandez Mail (Institucion Universitaria – ITSA, Colombia)
*corresponding author

Abstract


The use of brainwave signal is a step in the introduction of the individual identity using biometric technology based on characteristics of the body. Brainwave signal has unique characteristics and different on each individual because the brainwave cannot be read or copied by people so it is not possible to have a similarity of one person with another person. To be able to process the identification of individual characteristics, which obtained from the signal brainwave, required a pattern of brain activity that is prominent and constant. Cognitive activity testing using a single-sensor EEG (Electroencephalogram) divided into two categories, called the activity of cognitive involving the ability of the right brain (creativity, imagination, holistic thinking, intuition, arts, rhythms, nonverbal, feelings, visualization, tune of songs, daydreaming) and the left brain (logic, analysis, sequences, linear, mathematics, language, facts, think in words, word of songs, computation) give a different cluster based on two times the test on mathematical activities (no cluster slices of experiment 1 and experiment 2). The result showed that cognitive activity based on math activity can provide a signal characteristic that can be used as the basis for a brain-computer interface applications development by utilizing EEG single-sensor.

Keywords


EEG; Brainwaves; Cognitive Task; Kmeans Clustering

   

DOI

https://doi.org/10.26555/ijain.v2i3.86
      

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References


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.

A. A. Ghodake and S. D. Shelke, “Brain controlled home automation system,” in Intelligent Systems and Control (ISCO), 2016 10th International Conference on, 2016, pp. 1–4.

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.

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.

B. Trowbridge, C. Rodriguez, J. Prine, M. Holzemer, J. McCormack, and R. Integlia, “Gaming, fitness, and relaxation,” in Games Entertainment Media Conference (GEM), 2015 IEEE, 2015, pp. 1–1.

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.

Y. Yoshimura et al., “The Brain’s Response to the Human Voice Depends on the Incidence of Autistic Traits in the General Population,” PLoS One, vol. 8, no. 11, p. e80126, Nov. 2013.

D. Bright, A. Nair, D. Salvekar, and S. Bhisikar, “EEG-based brain controlled prosthetic arm,” in Advances in Signal Processing (CASP), Conference on, 2016, pp. 479–483.

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.

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.

A. Azhari, A. Susanto, and I. Soesanti, “Studi Perbandingan: Cognitive Task Berdasarkan Hasil Ekstraksi Ciri Gelombang Otak.pdf,” 2015, vol. 3.1, p. 7.

D. Putra and K. Gede, “Sistem Verifikasi Biometrika Telapak Tangan dengan Metode Dimensi Fraktal dan Lacunarity,” Maj. Ilm. Teknol. Elektro, vol. 8, no. 2, 2012.

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, “Ekstraksi Ciri Gelombang Otak Menggunakan Alat Neurosky Mindset Berbasis Korelasi Silang.pdf,” Universitas Gadjah Mada, Yogyakarta, 2015.

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

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

A. Saha, A. Konar, P. Das, B. Sen 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.

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.




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