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.