Here are some highlights. You can find the full list here.

Castaño-Candamil (2020), Machine learning methods for motor performance decoding in adaptive deep brain stimulation. Doctoral dissertation. Universität Freiburg. Link

Castaño-Candamil et al. (2020). A pilot study on data-driven adaptive deep brain stimulation in chronically implanted essential tremor patients. Frontiers in Human Neuroscience, 14, 541625. Link

Castaño-Candamil et al. (2015). Solving the EEG inverse problem based on space–time–frequency structured sparsity constraints. NeuroImage, 118, 598-612. Link

Castaño-Candamil et al. (2020). Identifying controllable cortical neural markers with machine learning for adaptive deep brain stimulation in Parkinson’s disease. NeuroImage: Clinical, 28, 102376. Link

Castaño-Candamil et al. (2019). Post-hoc labeling of arbitrary M/EEG recordings for data-efficient evaluation of neural decoding methods. Frontiers in Neuroinformatics, 13, 55. Link