Combined Method of EMD with CCA or LASSO to Detect SSVEP Frequency

Nowadays, brain-computer interface system based on steady-state visual evoked potentials is increased due to advantages such as accepted accuracy and minimal need for user training. Despite these benefits, the unwanted noise that affects SSVEP is one of the issues that can reduce the efficiency of such systems. This paper uses the EMD algorithm in the initial phase and CCA or LASSO for the recognition of the stimulation frequency. In the first step, the EMD algorithm is applied so that non-stationary SSVEP signal breaks into oscillating functions and meaningful information are extracted. Among the IMFs obtained from the EMD method, only IMFs whose amplitude of the frequency spectrum in the frequency ranges corresponding to the excitation is higher were selected. With this selection, noisy signals and unprofitable information can be omitted. In the proposed method, two CCA and LASSO diagnostic methods were performed on the sum of selected signals to identify the frequency of stimulation. The simulation results show the recognition accuracy of 81.76% and 82.26% for the proposed method EMD-CCA and EMD-LASSO, respectively. While detection accuracy is 78.10% and 78.72% for conventional methods of CCA and LASSO.