Abstract
So far Electroencephalography (EEG) has been analyzed by the re- search community for interaction with the computers. Studies regarding EEG signals has gained attention in the recent past as it gives an alternate way of com- munication for the persons suffering from partially or fully paralytic disability. Every second different activities are performed by millions of neurons. Decoding and detecting such complex activity of the brain while analyzing the EEG signals is a challenging task. In this paper, we have proposed an activity recognition system using EEG signals. The two activities, namely, eyes open (EO) and eyes close (EC) have been considered in this work. The recorded signals are then decomposed using Discrete Wavelet Transform (DWT) to analyze the impact of both the activities. The recognition of activities has been performed using Support Vector Machine (SVM) classifier. For experimentation, a publicly available dataset i.e. PhysioNet consisting data of 109 users while performing one minute EO and EC activity has been used. A notable activity recognition rate of 86.08% has been recorded using gamma band feature. The paper further proposes that the system can be used as a reference to detect different types of activities performed at different instance of time and for rehabilitation purposes also.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alomari, M.H., Baniyounes, A.M., Awada, E.A.: EEG-based classification of imagined fists movements using machine learning and wavelet transform analysis. Int. J. Adv. Electron. Electr. Eng. 83–87 (2014)
Alomari, M.H., Samaha, A., AlKamha, K.: Automated classification of L/R hand movement EEG signals using advanced feature extraction and machine learning. Int. J. Adv. Comput. Sci. Appl. 4, 207–212 (2013)
Azevedo, F.A., Carvalho, L.R., Grinberg, L.T., Farfel, J.M., Ferretti, R.E., Leite, R.E., Lent, R., Herculano-Houzel, S., et al.: Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Comp. Neurol. 513, 532–541 (2009)
Gauba, H., Kumar, P., Roy, P.P., Singh, P., Dogra, D.P., Raman, B.: Prediction of advertisement preference by fusing EEG response and sentiment analysis. Neural Networks 92, 77–88 (2017)
Kaur, B., Singh, D., Roy, P.P.: A novel framework of EEG-based user identification by analyzing music-listening behavior. Multimed. Tools Appl. 76, 1–22 (2017)
Kumar, P., Saini, R., Roy, P.P., Dogra, D.P.: A bio-signal based framework to secure mobile devices. J. Netw. Comput. Appl. 89, 62–71 (2017)
Kumar, P., Saini, R., Roy, P.P., Sahu, P.K., Dogra, D.P.: Envisioned speech recognition using EEG sensors. Pers. Ubiquit. Comput. 22(1–15), 2017 (2017)
Öner, M., Hu, G.: Analyzing one-channel EEG signals for detection of close and open eyes activities. In: International Conference on Advanced Applied Informatics, pp. 318–323 (2013)
Palaniappan, R., Ravi, K.: A new method to identify individuals using signals from the brain. In: International Conference on Information, Communications and Signal Processing, vol. 3, pp 1442–1445 (2003)
Qidwai, U., khazaal Shams, W.: A source-discrimination approach for detection of ASD using EEG data. Int. J. Biosci. Biochem. Bioinf. 3, 492–496 (2013)
Saghafi, A., Tsokos, C.P., Goudarzi, M., Farhidzadeh, H.: Random eye state change detection in real-time using EEG signals. Expert Syst. Appl. 72, 42–48 (2017)
Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI 2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004)
Trivedi, P., Bhargava, N.: Comparing alpha wave activity of left and right hemisphere of brain recorded using EEGlab. Int. J. Sci., Eng. Technol. Res. 6, 170–174 (2017)
Yadava, M., Kumar, P., Saini, R., Roy, P.P., Dogra, D.P.: Analysis of EEG signals and its application to neuromarketing. Multimed. Tools Appl. 76(18), 19087–19111 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kaur, B., Singh, D., Roy, P.P. (2018). Eyes Open and Eyes Close Activity Recognition Using EEG Signals. In: Nagabhushan, T., Aradhya, V.N.M., Jagadeesh, P., Shukla, S., M.L., C. (eds) Cognitive Computing and Information Processing. CCIP 2017. Communications in Computer and Information Science, vol 801. Springer, Singapore. https://doi.org/10.1007/978-981-10-9059-2_1
Download citation
DOI: https://doi.org/10.1007/978-981-10-9059-2_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-9058-5
Online ISBN: 978-981-10-9059-2
eBook Packages: Computer ScienceComputer Science (R0)