Training Free Signal Enhancement by Random Sampling for Steady State Visual Evoked Potential BCI

The steady-state visual evoked potential (SSVEP) is widely used for electroencephalography (EEG) based brain computer interfaces (BCIs). In order to determine the target commands for SSVEP-BCIs, deep neural networks have been widely used. However, learning-based approaches require training data, and the upfront costs are high. This study proposes a training free signal enhancement method for SSVEP-BCIs. The proposed method enhances SSVEP components by repeatedly performing random sampling and pasting of EEG signals. Our experiments using synthesized signals and open benchmark datasets demonstrate the performance of the proposed method. The proposed method outperforms conventional methods for all signal lengths, demonstrating an improvement in information transfer rate of up to 11.30 bit/min. .