Towards High-Frequency SSVEP-Based Target Discrimination with an Extended Alphanumeric Keyboard

Abstract

Despite significant advances in using Steady-State Visually Evoked Potentials (SSVEP) for on-screen target discrimination, existing methods either require intrusive, low-frequency visual stimulation or only support a small number of targets. We propose SSVEPNet: a convolutional long short-term memory (LSTM) recurrent neural network for high-frequency stimulation (>30Hz) using a large number of visual targets. We evaluate our method for discriminating between 43 targets on an extended alphanumeric virtual keyboard and compare three different frequency assignment strategies. Our experimental results show that SSVEPNet significantly outperforms state-of-the-art correlation-based methods and convolutional neural networks. As such, our work opens up an exciting new direction of research towards a new class of unobtrusive and highly expressive SSVEP-based interfaces for text entry and beyond.