## Frontmatter | | | | --- | --- | | Authors | [[Marcel van Gerven]], [[Katja Seeliger]], [[Umut Güçlü]], [[Yağmur Güçlütürk]] | | Date | 2019/09 | | Source | [[Explainable AI - Interpreting, Explaining and Visualizing Deep Learning]] | | URL | https://doi.org/10.1007/978-3-030-28954-6_21 | | Citation | van Gerven, M., Seeliger, K., Güçlü, U., & Güçlütürk, Y. (2019). [[Current advances in neural decoding]]. In _Explainable AI - Interpreting, Explaining and Visualizing Deep Learning_. [[URL](https://doi.org/10.1007/978-3-030-28954-6_21)]. #Chapter | ## Abstract Neural decoding refers to the extraction of semantically meaningful information from brain activity patterns. We discuss how advances in machine learning drive new advances in neural decoding. While linear methods allow for the reconstruction of basic stimuli from brain activity, more sophisticated nonlinear methods are required when reconstructing complex naturalistic stimuli. We show how deep neural networks and adversarial training yield state-of-the-art results. Ongoing advances in machine learning may one day allow the reconstruction of thoughts from brain activity patterns, providing a unique insight into the contents of the human mind. ## PDF ![[Current advances in neural decoding.pdf]]