Understanding how the brain encodes inner speech is a fascinating challenge with far-reaching implications for neuroscience, brain–computer interfaces, and assistive technologies. To help make sense of the fast-growing research in this area, we have launched a continuous literature review focused on deep learning methods for decoding inner speech from EEG signals.
As part of this effort, we provide an open spreadsheet that brings together recent publications, highlighting key ideas, methods, and trends across the field. This resource will be regularly updated to reflect new advances, ensuring that it remains a living overview of the state of the art.
By making this evolving body of work easier to explore and compare, we hope to support newcomers, inform ongoing research, and foster collaboration within the community and beyond.
Download the latest version now! (Updated 14-Jan-2026)