Frequency Detection of Single Channel Steady State Visual Evoked Potential using Canonical Correlation Analysis
Mukesh Kumar Ojha1, Manoj Kumar Mukul2, Ravinder Nath Rajotia3, Nitin Tyagi4
1Mukesh Kumar Ojha, Department of Electronics & Communication, Birla Institute of Technology, Mesra, Ranchi (Jharkhand), India.
2Manoj Kumar Mukul, Department of Electronics & Communication, Birla Institute of Technology, Mesra, Ranchi (Jharkhand), India.
3Ravinder Nath Rajotia, Department of Electronics & Communication, JIMS Engineering Management Technology Campus, Gr. Noida (U.P), India.
4Nitin Tyagi, Department of Electronics & Communication, JIMS Engineering Management Technology Campus, Gr. Noida (U.P), India.
Manuscript received on 16 November 2019 | Revised Manuscript received on 04 December 2019 | Manuscript Published on 10 December 2019 | PP: 136-139 | Volume-8 Issue-3S2 October 2019 | Retrieval Number: C10221083S219/2019©BEIESP | DOI: 10.35940/ijrte.C1022.1083S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Wave generated into visual cortex of brain, when subject focused his/her attention on visual stimulus flickers at certain frequency. The main challenge with SSVEP Based Brain computer interface (BCI) System is to detect the stimulus frequency from recorded brain signal. Canonical Correlation analysis (CCA) is one of the most popular methods to recognize the frequency of Steady state visual evoked potential (SSVEP). This paper focuses on the study of CCA algorithm to recognize the SSVEP signal frequency. For experiment purpose, a single channel data with flickering frequency in the range of (6Hz-10Hz) is used. The performance of the BCI System is measured in terms of detection accuracy and Information transmission rate (ITR). The maximum accuracy is obtained as 83.90% and ITR is 15.35 at stimulus frequency of 8.2Hz
Keywords: Steady State Visual Evoked Potential (SSVEP); Brain Computer Interface (BCI); PSDA, Electroencephalogram (EEG); Canonical Correlation Analysis (CCA).
Scope of the Article: Visual Analytics