Journal
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 7, Issue 2, Pages 104-111Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2011.02.002
Keywords
Brain-computer interfaces (BCIs); Electroencephalogram (EEG); Least absolute shrinkage and selection operator (LASSO); Steady-state visual evoked potential (SSVEP); Time window (TW)
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Funding
- Nation Nature Science Foundation of China [61074113]
- Shanghai Leading Academic Discipline Project [B504]
- Fundamental Research Funds for the Central Universities [WH0914028]
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Steady-state visual evoked potential (SSVEP) has been increasingly used for the study of brain-computer interface (BC!). How to recognize SSVEP with shorter time and lower error rate is one of the key points to develop a more efficient SSVEP-based BCI. To achieve this goal, we make use of the sparsity constraint of the least absolute shrinkage and selection operator (LASSO) for the extraction of more discriminative features of SSVEP, and then we propose a LASSO model using the linear regression between electroencephalogram (EEG) recordings and the standard square-wave signals of different frequencies to recognize SSVEP without the training stage. In this study, we verified the proposed LASSO model offline with the EEG data of nine healthy subjects in contrast to canonical correlation analysis (CCA). In the experiment, when a shorter time window was used, we found that the LASSO model yielded better performance in extracting robust and detectable features of SSVEP, and the information transfer rate obtained by the LASSO model was significantly higher than that of the CCA. Our proposed method can assist to reduce the recording time without sacrificing the classification accuracy and is promising for a high-speed SSVEP-based BCI. (C) 2011 Elsevier Ltd. All rights reserved.
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