NDLI: A partial least squares-based stimulus frequency recognition model for steady-state visual evoked potentials detection

Content Provider	IEEE Xplore Digital Library
Author	Ruimin Wang Yue Leng Yuankui Yang Wen Wu Iramina, K. Sheng Ge
Copyright Year	2014
Description	Author affiliation: Sch. of Electron. Eng. & Optoelectron. Technol., Nanjing Univ. of Sci. & Technol., Nanjing, China (Ruimin Wang; Wen Wu; Sheng Ge) \|\| Key Lab. of Child Dev. & Learning Sci., Southeast Univ., Nanjing, China (Yue Leng; Yuankui Yang) \|\| Grad. Sch. of Syst. Life Sci., Kyushu Univ., Fukuoka, Japan (Iramina, K.)
Abstract	With shorter calibration times and higher information transfer rates, steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been studied most activity in recent years. Target identification is the ongoing core task in BCI researches, and plays a significant role in practical applications. In order to improve the performance of SSVEP-based BCI system, we proposed a partial least squares (PLS)-based stimulus frequency recognition model for SSVEP detection. Moreover, we compared the proposed method with canonical correlation analysis (CCA) and least absolute shrinkage and selection operator (LASSO) method, respectively. The experiment results showed that PLS can not only extract the SSVEP features effectively, but also can increase the classification accuracies of SSVEP-based BCI systems.
Starting Page	699
Ending Page	703
File Size	219178
Page Count	5
File Format	PDF
e-ISBN	9781479958382
DOI	10.1109/BMEI.2014.7002863
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Accuracy Correlation least absolute shrinkage and selection operator (LASSO) Brain modeling Feature extraction canonical correlation analysis (CCA) Brain-computer interface (BCI) Electroencephalography Vectors steady-state visual evoked potential (SSVEP) partial least squares (PLS)
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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