Leveraging meaning-induced neural dynamics to detect covert cognition via EEG during natural language listening - a case series

Ludvik Alkhoury¹James O'sullivan¹Giacomo Scanavini¹Jin Dou²Joshitha Arora¹Lilah Hamill¹Abigail Patchell¹Ana Radanovic¹William D Watson³Edmund C Lalor⁴Nicholas D Schiff⁵N Jeremy Hill^6,7Sudhin Abhilash Shah^1*

• ¹Department of Radiology, Weill Cornell Medicine, Cornell University, New York, United States
• ²Department of Biomedical Engineering, Hajim School of Engineering and Applied Sciences, University of Rochester, Rochester, New York, United States
• ³Blythedale Children’s Hospital, Valhalla, NY, United States
• ⁴Department of Biomedical Engineering, Department of Neuroscience, and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY, United States
• ⁵Brain Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, New York, United States
• ⁶National Center for Adaptive Neurotechnologies, Stratton VA Medical Center, Albany, New York, United States
• ⁷Electrical and Computer Engineering Department, State University of New York at Albany, Albany, New York, United States

At least a quarter of adult patients with severe brain injury in a disorder of consciousness may have cognitive abilities that are hidden due to motor impairment. In this case series, we developed a tool that extracted acoustic and semantic processing biomarkers from electroencephalography recorded while participants listened to a story. We tested our method on two male adolescent survivors of severe brain injury and showed evidence of acoustic and semantic processing. Our method identifies cognitive processing while obviating demands on attention, memory, and executive function. This lays a foundation for graded assessments of cognition recovery across the spectrum of covert cognition.