Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the “decoder” at the heart of the iBCI typically degrades over time due to turnover of recorded neurons.

Here we propose a method based on Cycle-Consistent Adversarial Networks (Cycle-GAN, Zhu et al., 2017), which aligns the distributions of the full-dimensional neural recordings across different days.

Please open the notebook Cycle_GAN_aligner.ipynb to view a hands-on tutorial and all codes to implement this method.