Study of Power Spectrum Estimation of Steady-State Visual Evoked Potential-Based BCI System Using AR Model Approach
Brain-computer interface (BCI) system is the direct interaction between the human brain and the external electronic devices like robotic arms, electronic wheel chair, etc., through desired mental tasks which enable the different amplitude of brainwaves in
- PDF / 406,720 Bytes
- 8 Pages / 439.37 x 666.142 pts Page_size
- 52 Downloads / 152 Views
Abstract Brain-computer interface (BCI) system is the direct interaction between the human brain and the external electronic devices like robotic arms, electronic wheel chair, etc., through desired mental tasks which enable the different amplitude of brainwaves inferring to humans’ different mental activities. It uses the electrical activity of brain caused by the communication between the two neurons. The neurons communicate with each other in the form of electrical impulses which generate low amplitude and low frequency electromagnetic wave termed as electroencephalogram (EEG), In the past years, different Paradigms have been used to design BCI like Motor imaginary, P300, SSVEP, etc. The objective of this paper is to estimate the power spectral density (PSD) of the SSVEP signal (Steady-state Visual evoke Potential) from recoded EEG Data using AR Model approach. Keywords BCI
SSVEP EEG Feature extraction
1 Introduction Brain-computer interface (BCI) is a direct communication system between human brain and external device that enables a person to send the command to external device only by means of different brain activities [1, 2]. The functional activity of brain signal is measured by placing the sensors on the surface of the brain called electroencephalography (EEG). The appearance of different brain activity depends upon the location of sensor placement on the surface of head, mental status of the subject, and various other parameters. EEG approach to acquire the brain signal is broadly acceptable because of its simple and safe approach.
M.K. Ojha (&) BIT MESRA, Ranchi, India e-mail: [email protected] Anshuman Prakash University of Petroleum and Energy Studies, Dehradun, India © Springer Science+Business Media Singapore 2017 R. Singh and S. Choudhury (eds.), Proceeding of International Conference on Intelligent Communication, Control and Devices, Advances in Intelligent Systems and Computing 479, DOI 10.1007/978-981-10-1708-7_71
625
626
M.K. Ojha and Anshuman Prakash
Different paradigms can be followed in the design of BCI Such as Motor Imagery, P300, and Steady-State Visual Evoked Potential (SSVEP) [3]. In recent years, SSVEP-Based BCI System become more popular among the various research groups because of high information Transfer Rate (ITR), short training time, high signal-to-noise ratio and simple system configuration [4, 5]. This paper is organized as follows; in Sect. 2 fundamental of SSVEP Signal and SSVEP-Based BCI System is presented in detail. In Sect. 3, FFT Approach and AR Model approach to estimate the power spectrum is explained in details. Result and future research work are presented in Sect. 4.
2 SSVEP System 2.1
Fundamentals of Steady-State Visual Evoked Potentials
The various research groups working on SSVEP-based BCI system have suggested that when subject focuses his/her attention on a visual stimulus that flicker at certain frequency, a periodic response occurs into the visual cortex of brain region known as steady-state visual evoked potential [4, 5]. The study of vari
Data Loading...
