Abstract
Improving the classification accuracy in brain–computer interface (BCI) with a short data length is important to increase the BCI system’s information transfer rate. Least absolute shrinkage and selection operator (LASSO) has been examined to be an effective way to detect the steady-state visual evoked potential (SSVEP) signals with a short time window. In this paper, an improved multiple LASSO model for SSVEP detection is proposed, which can process multichannel electroencephalogram (EEG) signals without electrode selection. EEG data from twelve healthy volunteers were used to test the improved multiple LASSO model. Compared with the traditional LASSO model, the improved multiple LASSO model gives a significantly better performance with multichannel EEG data.
Access provided by CONRICYT-eBooks. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Ebrahimi T (2007) Recent advances in brain-computer interfaces, IEEE 9th Workshop on MMSP, Crete, Greece, 2007, p 17
Nicolas-Alonso LF, Gomez-Gil J (2012) Brain computer interfaces, a review. Sensors (Basel) 12:1211–1279
Cheng M, Gao X, Gao S, Xu D (2002) Design and implementation of a brain–computer interface with high transfer rates. IEEE Trans Biomed Eng 49:1181–1186
Liu Q, Chen K, Ai QS, Xie SQ (2014) Review: recent development of signal processing algorithms for SSVEP-based brain computer interfaces. J Med Biol Eng 34(4):299–309
Dyar MD, Carmosino ML, Breves EA et al (2012) Comparison of partial least squares and lasso regression techniques as applied to laser-induced breakdown spectroscopy of geological samples. Spectrochim Acta Part B 70:51–67
Xu C, Ladouceur M, Dastani Z, Richards JB, Ciampi A et al (2012) Multiple regression methods show great potential for rare variant association tests. PLoS ONE 7(8):e41694
Wang J, Xue F, Li H (2015) Simultaneous channel and feature selection of fused EEG features based on sparse group lasso. Biomed Res Int 2015:703768
Zhang Y, Jin J, Qing XY, Wang B, Wang XY (2012) LASSO based stimulus frequency recognition model for SSVEP BCIs. Biomed Signal Proces 7(2):104–111
Zhang YS, Dong L, Zhang R, Yao DZ, Zhang Y, Xu P (2014) An efficient frequency recognition method based on likelihood ratio test for SSVEP-based BCI. Comput Math Method M 2014:908719
Sheng G, Wang RM, Leng Y, Wang HX, Lin P, and Iramina KA double-partial least-squares model for the detection of steady-state visual evoked potentials. IEEE J Biomed Health Press
Lin ZL, Zhang CS, Wu W, Gao XR (2007) Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng 54(6):1172–1176
Boulesteix AL, Richter A, Bernau C (2013) Complexity selection with cross-validation for lasso and sparse partial least squares using high-dimensional data. In: Lausen B, Van den Poel D, and Ultsch A (eds) Algorithms from and for nature and life. Springer International Publishing, Switzerland, vol 5517, pp 261–268
Acknowledgements
This work was supported by the National Basic Research Program of China (2015CB351704), the Fundamental Research Funds for the Southeast University (CDLS-2015-01).
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
Wang, R., Iramina, K., Ge, S. (2018). An Improved Multiple LASSO Model for Steady-State Visual Evoked Potential Detection. In: Vo Van, T., Nguyen Le, T., Nguyen Duc, T. (eds) 6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6) . BME 2017. IFMBE Proceedings, vol 63. Springer, Singapore. https://doi.org/10.1007/978-981-10-4361-1_72
Download citation
DOI: https://doi.org/10.1007/978-981-10-4361-1_72
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4360-4
Online ISBN: 978-981-10-4361-1
eBook Packages: EngineeringEngineering (R0)