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Decoding Neural Activity to Anticipate Eye Movements

Neural interfaces for motor control read out motor planning activity in the brain and use it to control a computer or physical device in order to restore or replace motor functions of the brain. Applications involving the readout of intended eye movements however remain comparatively undeveloped. In this study, we aim to develop decoding algorithms that can predict eye movement signals from neural responses during the planning period, before the animal makes an eye movement. The single-trial local field potentials of neurons in Frontal Eye Field (FEF), a cortical area that contributes to the control of eye movements, as well as, in area V4 of nonhuman primates are used to train the eye movement decoders. The algorithms identified and optimized in this study can facilitate the development of brain machine interface systems for eye movements, with an ultimate goal of providing assistive technologies to correct for the impaired gaze control in patients with eye movement disorders.