Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface

By B. Jarosiewicz, Anish A. Sarma, D. Bacher, Nicolas Y. Masse, John D. Simeral, B. Sorice, Erin M. Oakley, C. Blabe, C. Pandarinath, V. Gilja and 7 more. Science Translational Medicine, 2015

336 citations recorded by the source corpus.

Individuals with tetraplegia are able to type self-paced for hours across multiple days using a self-calibrating point-and-click intracortical brain-computer interface. Prolonged typing with refined BCI The fact that the brain can be hooked up to a computer to allow paralyzed individuals to type is already a technological feat. But, these so-called brain-computer interface technologies can be tiring and burdensome for users, requiring frequent disruptions for recalibration when the decoded neural signals change. Jarosiewicz and colleagues therefore combined three calibration methods—retrospective target interference, velocity bias correction, and adaptive tracking of neural features—for seamless typing and stable neural control. This combination allowed two individuals with tetraplegia and with cortical microelectrode arrays to compose long texts at their own paces, with no need to interrupt typing for recalibration. Brain-computer interfaces (BCIs) promise to restore independence for people with severe motor disabilities by translating decoded neural activity directly into the control of a computer. However, recorded neural signals are not stationary (that is, can change over time), degrading the quality of decoding. Requiring users to pause what they are doing whenever signals change to perform decoder recalibration routines is time-consuming and impractical for everyday use of BCIs. We demonstrate that signal nonstationarity in an intracortical BCI can be mitigated automatically in software, enabling long periods (hours to days) of self-paced point-and-click typing by people with tetraplegia, without degradation in neural control. Three key innovations were included in our approach: tracking the statistics of the neural activity during self-timed pauses in neural control, velocity bias correction during neural control, and periodically recalibrating the decoder using data acquired during typing by mapping neural activity to movement intentions that are inferred retrospectively based on the user’s self-selected targets. These methods, which can be extended to a variety of neurally controlled applications, advance the potential for intracortical BCIs to help restore independent communication and assistive device control for people with paralysis.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/6d5a032bb6bf6e5ce3ba5bd12415b9068d359a80

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

High-performance brain-to-text communication via handwriting (2021, methodology): “To train the RNN to account for drifts in the means of neural features that naturally accrue over time in neural recordings (Downey et al., 2018; Jarosiewicz et al., 2015), we added artificial perturbations to the feature means, similar to (Sussillo et al.”
High performance communication by people with paralysis using an intracortical brain-computer interface (2017, methodology): “…et al., 2003 ; Musallam et al., 2004 ; Santhanam et al., 2006 ; Hochberg et al., 2006 ; Ganguly et al., 2011 ; O’Doherty et al., 2011 ; Gilja et al., 2012 ; Simeral et al., 2011 ; Bacher et al., 2015 ; Nuyujukian et al., 2015 ; Aflalo et al., 2015 ; Gilja et al., 2015 ; Jarosiewicz et al., 2015 ).”