Benyamin Meschede-Krasa

All Publications

• Inner speech in motor cortex and implications for speech neuroprostheses. Cell Kunz, E. M., Abramovich Krasa, B., Kamdar, F., Avansino, D. T., Hahn, N., Yoon, S., Singh, A., Nason-Tomaszewski, S. R., Card, N. S., Jude, J. J., Jacques, B. G., Bechefsky, P. H., Iacobacci, C., Hochberg, L. R., Rubin, D. B., Williams, Z. M., Brandman, D. M., Stavisky, S. D., AuYong, N., Pandarinath, C., Druckmann, S., Henderson, J. M., Willett, F. R. 2025

  Abstract

  Speech brain-computer interfaces (BCIs) show promise in restoring communication to people with paralysis but have also prompted discussions regarding their potential to decode private inner speech. Separately, inner speech may be a way to bypass the current approach of requiring speech BCI users to physically attempt speech, which is fatiguing and can slow communication. Using multi-unit recordings from four participants, we found that inner speech is robustly represented in the motor cortex and that imagined sentences can be decoded in real time. The representation of inner speech was highly correlated with attempted speech, though we also identified a neural "motor-intent" dimension that differentiates the two. We investigated the possibility of decoding private inner speech and found that some aspects of free-form inner speech could be decoded during sequence recall and counting tasks. Finally, we demonstrate high-fidelity strategies that prevent speech BCIs from unintentionally decoding private inner speech.

  View details for DOI 10.1016/j.cell.2025.06.015

  View details for PubMedID 40816265

• 5-year follow-up of a fully implanted brain-computer interface in a spinal cord injury patient JOURNAL OF NEURAL ENGINEERING Davis, K. C., Wyse-Sookoo, K. R., Raza, F., Meschede-Krasa, B., Prins, N. W., Fisher, L., Brown, E. N., Cajigas, I., Ivan, M. E., Jagid, J. R., Prasad, A. 2025; 22 (2)

  Abstract

  Spinal cord injury (SCI) affects over 250 000 individuals in the US. Brain-computer interfaces (BCIs) may improve quality of life by controlling external devices. Invasive intracortical BCIs have shown promise in clinical trials but degrade in the chronic period and tether patients to acquisition hardware. Alternatively, electrocorticography (ECoG) records data from electrodes on the cortex,and studies evaluating fully implanted BCI-ECoG systems are scarce. Objective. We seek to address this need using a fully implanted ECoG-based BCI that allows for home use in SCI.Approach.The patient used a long-term BCI system, initially controlling an functional electrical stimulation orthosis in the lab and later using an external mechanical orthosis at home. To evaluate its long-term viability, electrode contact impedance, signal quality, and decoder performance were measured. Signal quality was assessed using signal-to-noise ratio and maximum bandwidth of the signal. Decoder performance was monitored using the area under the receiver operator characteristic curve (AUROC).Main results.The study analyzed data from the patient's home environment over 54 months, revealing that the device was used at home for 38 ± 24 min on average daily. After six months, we observed stable event-related desynchronization that aided in determining the onset of motor intention. The decoder's average AUROC across months was 0.959. Importantly, 40 months of the data collected was gather from the subject's home or community environment. The results indicate long-term ECoG recordings were stable for motor-imagery classification and motor control in the community environment in a case of an individual with SCI.Significance.This study presents the long-term feasibility and viability of an ECoG-based BCI system that persists in the home environment in a case of SCI. Future research should explore larger electrode counts with more participants to confirm this stability. Understanding these trends is crucial for clinical utility and chronic viability in broader patient populations.

  View details for DOI 10.1088/1741-2552/adc48c

  View details for Web of Science ID 001462321500001

  View details for PubMedID 40127544

  View details for PubMedCentralID PMC12600028

• Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia PLOS ONE Abel, J. H., Badgeley, M. A., Meschede-Krasa, B., Schamberg, G., Garwood, I. C., Lecamwasam, K., Chakravarty, S., Zhou, D. W., Keating, M., Purdon, P. L., Brown, E. N. 2021; 16 (5): e0246165

  Abstract

  In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without directly monitoring the brain. Drug- and patient-specific electroencephalographic (EEG) signatures of anesthesia-induced unconsciousness have been identified previously. We applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. We used cross-validation to select and train the best performing models using 33,159 2s segments of EEG data recorded from 7 healthy volunteers who received increasing infusions of propofol while responding to stimuli to directly assess unconsciousness. Cross-validated models of unconsciousness performed very well when tested on 13,929 2s EEG segments from 3 left-out volunteers collected under the same conditions (median volunteer AUCs 0.99-0.99). Models showed strong generalization when tested on a cohort of 27 surgical patients receiving solely propofol collected in a separate clinical dataset under different circumstances and using different hardware (median patient AUCs 0.95-0.98), with model predictions corresponding with actions taken by the anesthesiologist during the cases. Performance was also strong for 17 patients receiving sevoflurane (alone or in addition to propofol) (median AUCs 0.88-0.92). These results indicate that EEG spectral features can predict unconsciousness, even when tested on a different anesthetic that acts with a similar neural mechanism. With high performance predictions of unconsciousness, we can accurately monitor anesthetic state, and this approach may be used to engineer infusion pumps to intelligibly respond to patients' neural activity.

  View details for DOI 10.1371/journal.pone.0246165

  View details for Web of Science ID 000664611400004

  View details for PubMedID 33956800

  View details for PubMedCentralID PMC8101756

• An early phase of instructive plasticity before the typical onset of sensory experience NATURE COMMUNICATIONS Roy, A., Wang, S., Meschede-Krasa, B., Breffle, J., Van Hooser, S. D. 2020; 11 (1): 11

  Abstract

  While early experience with moving stimuli is necessary for the development of direction selectivity in visual cortex of carnivores, it is unclear whether experience exerts a permissive or instructive influence. To test if the specific parameters of the experienced stimuli could instructively sculpt the emergent responses, visually naive ferrets were exposed to several hours of experience with unusual spatiotemporal patterns. In the most immature ferrets, cortical neurons developed selectivity to these patterns, indicating an instructive influence. In animals that were 1-10 days more mature, exposure to the same patterns led to a developmentally-typical increase in direction selectivity. We conclude that visual development progresses via an early phase of instructive plasticity, when the specific patterns of neural activity shape the specific parameters of the emerging response properties, followed by a late phase of permissive maturation, when sensory-driven activity merely serves to enhance the response properties already seeded in cortical circuits.

  View details for DOI 10.1038/s41467-019-13872-1

  View details for Web of Science ID 000510942300001

  View details for PubMedID 31896763

  View details for PubMedCentralID PMC6940391