Implantable epiretinal prostheses aim to restore visual capability for patients suffering from diseases such as Macular Degeneration and Retinitis Pigmentosa by bypassing damaged photoreceptors and electrically exciting Retinal Ganglion Cells (RGCs) directly. Next-generation devices will require single-cell resolution, and bidirectional capabilities to classify and selectively stimulate different cell types in the RGC population as to produce an image closer to healthy eye operation. The classification process requires the capture of direct neural responses immediately following stimulus. A significant challenge to overcome before the next generation of devices can be realised is the stimulation artifact. Stimulation artifacts are large unwanted voltages resulting from interactions between the stimulation current and the tissue-electrode impedance that can last for tens to hundreds of milliseconds, saturating the recording electronics and obscuring the direct responses. This work aims to develop a novel stimulation artifact reduction method that can be implemented in high-resolution, high channel-count brain-machine interfaces. The algorithm requires generating a model of the tissue-electrode interface from discrete measurements in the time or frequency domain, and constructing a stimulation waveform that maximally reduces the duration of the residual artifact without the need for complex recording front-end electronics. The proposed method also includes a trimming step that corrects for small variations in the model parameters and stimulation currents. Using a custom stimulation and recording test board, the algorithm was tested for 30 µm diameter gold/polyurethane electrodes in saline solution. Anodic stimulation current pulses of -100 to -500nA and 50 to 250 µs were successfully corrected for, resulting in an average artifact recovery time of 124 µs at the stimulating electrode when measured from the end of the anodic (working) phase. This corresponds to a mean 81% improvement from the next best conventional charge-balanced stimulation waveform, and 79% improvement when compared to an active-discharge recovery method. Recovery times at non-stimulating electrodes remain largely unaffected. Future work should focus on exploring alteration of the working phase for further artifact reduction, and reducing the computational cost of the algorithm for implementation on-chip