13 papers · ranked by Valyu relevance
Pedro Alcolea, Xuan Ma, Kevin Bodkin, Lee E. Miller + 1 more
We designed the discrete direction selection (DDS) decoder for intracortical brain computer interface (iBCI) cursor control and showed that it outperformed currently used decoders in a human-operated real-time iBCI simulator and in monkey iBCI use. Unlike virtually all existing decoders that map between neural activity…
Lei Zhang, Mohan Yuan, Asaf Gilboa, Claude Alain
When recalling past episodes, different features of an experience, such as conceptual meaning and perceptual detail, are reconstructed and reinstated across distributed cortical regions. However, current models of human memory remain unclear how these feature-specific reinstatements unfold over time, and whether they…
Jerry Tang, Amanda LeBel, Shailee Jain, Alexander G. Huth
A brain-computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, decoders that reconstruct continuous language use invasive recordings from surgically implanted electrodes^1–3^, while decoders that use non-invasive…
Bianca M. Coleman, Melissa E Cook, Md. Robin Khan, Amanda K. Vogel + 10 more
Vulvovaginal candidiasis (VVC), caused by the commensal pathobiont Candida albicans, affects >75% of women, marring quality of life and incurring significant health costs. Estrogen (E2) activity is tightly linked to VVC susceptibility, and preclinical models employ E2 to establish vaginal colonization. Unlike most…
Sophie Austermeier, Axel Dietschmann, Gianluca Vascelli, Kar On Cheng + 21 more
Vulvovaginal candidiasis (VVC) is a mucosal yeast infection where symptoms are driven by inflammatory responses. The onset of VVC is instigated by the yeast Candida albicans, but the underlying causes of disease-driving hyperinflammation remains incompletely resolved. We found that vaginal albumin concentrations are…
Young Joon Kim, Nora Brackbill, Ella Batty, JinHyung Lee + 4 more
Decoding sensory stimuli from neural activity can provide insight into how the nervous system might interpret the physical environment, and facilitates the development of brain-machine interfaces. Nevertheless, the neural decoding problem remains a significant open challenge. Here, we present an efficient nonlinear…
Eva L. Dyer, Mohammad Gheshlaghi Azar, Hugo L. Fernandes, Matthew G. Perich + 3 more
Brain decoders use neural recordings to infer a user’s activity or intent. To train a decoder, we generally need infer the variables of interest (covariates) using simultaneously measured neural activity. However, there are many cases where this approach is not possible. Here we overcome this problem by introducing a…
Shoeb Shaikh, Rosa So, Tafadzwa Sibindi, Camilo Libedinsky + 1 more
Fully implantable wireless intra-cortical Brain Machine Interfaces (iBMI) is one of the most promising next frontiers in the nascent field of neurotechnology. However, scaling the number of channels in such systems by another 10X is difficult due to power and bandwidth requirements of the wireless transmitter. One…
J. Brendan Ritchie, David Michael Kaplan, Colin Klein
Since its introduction, multivariate pattern analysis (MVPA), or “neural decoding”, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the Decoder’s Dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence…
Hong-Yun Ou, Takahiro Hasegawa, Osamu Fukayama, Eizo Miyashita
Brain–machine interfaces (BMIs) aim to decode motor intentions from neural activity to enable direct control of external devices. However, most existing decoders rely on monolithic architectures that fail to capture the distinct neural representations of different joint movement directions, limiting their…
Islam S. Badreldin, Karim G. Oweiss
Brain-machine interfaces rely on extracting motor control signals from brain activity in real time to actuate external devices such as robotic limbs. Whereas biomimetic approaches to neural decoding use motor imagery/observation signals, non-biomimetic approaches assign an arbirary transformation that maps neural…
Hisham Temmar, Matthew S. Willsey, Joseph T. Costello, Matthew J. Mender + 6 more
Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by ‘decoding’ neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear…
R. Jabakhanji, A.D. Vigotsky, J. Bielefeld, L. Huang + 3 more
High-profile studies claim to assess mental states across individuals using multi-voxel decoders of brain activity. The fixed, fine-grained, multi-voxel patterns in these “optimized” decoders are purportedly necessary for discriminating between, and accurately identifying, mental states. Here, we present compelling…