Biology subjects

Kunigk, N. G.

Publications and source records attributed to Kunigk, N. G..

4 recordsLinked to original sources

bioRxiv2026

Closed-loop error damping in human BCI using pre-error motor cortex activity

Intracortical brain-computer interfaces (BCIs) are used to decode motor intent from neural population activity; their main clinical application is to restore function for individuals with motor or communication deficits. However, when trying to reconstruct movement trajectories, such as in computer cursor control, even state-of-the-art decoders fall short of able-bodied performance during online BCI control. This calls for alternative approaches to improve the usability of motor BCIs. Here, we leveraged an error signal, i.e. a neural correlate of faulty motor control that can be detected from neural activity. By detecting this error signal in parallel to performing movement decoding, it is possible to perform error modulation, i.e. real-time error detection and correction during a closed-loop motor BCI task. We analyzed data from four individuals with upper limb impairment due to cervical spinal cord injury who each used an intracortical BCI to perform a continuous cursor control task with visual feedback. A classifier was trained to detect the error signal and was used to perform online error detection during BCI control to limit ongoing errors (defined as movement of the controller away from its target) without requiring any specific action from the participants. Our contribution is three-fold. First, we show that the error signal has a pre-error component. Cortical activity was already significantly modulated before the onset of the kinematically-defined error, theoretically allowing for earlier detection. Second, we show that error modulation significantly improves performance during online BCI control of cursor kinematics. Finally, we show that the error signal can be robustly leveraged across contexts, as error modulation improves performance in more complex motor tasks (involving for instance grasp and drag actions) or other environments without task-specific calibration. Overall, our results suggest that the error signal can be robustly disentangled from motor intent in cortical activity, and that even a simple linear classifier can enable error modulation in parallel to a continuous kinematic decoder, yielding more reliable and accurate BCI control.

neuroscience↗

bioRxiv2025

Finite element model predicts micromotion-induced strain profiles that correlate with the functional performance of Utah arrays in humans and non-human primates

ObjectiveUtah arrays are widely used in both humans and non-human primates (NHPs) for intracortical brain-computer interfaces (BCIs), primarily for detecting electrical signals from cortical tissue to decode motor commands. Recently, these arrays have also been applied to deliver electrical stimulation aimed at restoring sensory functions. A key challenge limiting their longevity is the micromotion between the array and cortical tissue, which may induce mechanical strain in surrounding tissue and contribute to performance decline. This strain, due to mechanical mismatch, can exacerbate glial scarring around the implant, reducing the efficacy of Utah arrays in recording neuronal activity and delivering electrical stimulation. ApproachTo investigate this, we employed a finite element model (FEM) to predict tissue strains resulting from micromotion. Main ResultsOur findings indicated that strain profiles around edge and corner electrodes were greater than those around interior shanks, affecting both maximum and average strains within 50 {micro}m of the electrode tip. We then correlated these predicted tissue strains with in-vivo electrode performance metrics. We found negative correlations between 1 kHz impedance and tissue strains in human motor arrays and NHP area V4 arrays at 1-mo, 1-yr, and 2-yrs post-implantation. In human motor arrays, the peak-to-peak waveform voltage (PTPV) and signal-to-noise ratio (SNR) of spontaneous activity were also negatively correlated with strain. Conversely, we observed a positive correlation between the evoked SNR of multi-unit activity and strain in NHP area V4 arrays. SignificanceThis study establishes a spatial dependence of electrode performance in Utah arrays that correlates with tissue strain.

neuroscience↗

bioRxiv2025

A Generalist Intracortical Motor Decoder

Mapping the relationship between neural activity and motor behavior is a central aim of sensori-motor neuroscience and neurotechnology. While most progress to this end has relied on restricting complexity, the advent of foundation models instead proposes integrating a breadth of data as an alternate avenue for broadly advancing downstream modeling. We quantify this premise for motor decoding from intracortical microelectrode data, pretraining an autoregressive Transformer on 2000 hours of neural population spiking activity paired with diverse motor covariates from over 30 monkeys and humans. The resulting model is broadly useful, benefiting decoding on 8 downstream decoding tasks and generalizing to a variety of neural distribution shifts. However, we also highlight that scaling autoregressive Transformers seems unlikely to resolve limitations stemming from sensor variability and output stereotypy in neural datasets. Code: https://github.com/joel99/ndt3

neuroscience↗

bioRxiv2024

How different immersive environments affect intracortical brain computer interfaces

AO_SCPLOWBSTRACTC_SCPLOWAs brain-computer interface (BCI) research advances, many new applications are being developed. Tasks can be performed in different environments, and whether a BCI user can switch environments seamlessly will influence the ultimate utility of a clinical device. Here we investigate the importance of the immersiveness of the virtual environment used to train BCI decoders on the resulting decoder and its generalizability between environments. Two participants who had intracortical electrodes implanted in their precentral gyrus used a BCI to control a virtual arm, either viewed immersively through virtual reality goggles or at a distance on a flat television monitor. Each participant performed better with a decoder trained and tested in the environment they had used the most prior to the study, one for each environment type. The neural tuning to the desired movement was minimally influenced by the immersiveness of the environment. Finally, in further testing with one of the participants, we found that decoders trained in one environment generalized well to the other environment, but the order in which the environments were experienced within a session mattered. Overall, experience with an environment was more influential on performance than the immersiveness of the environment, but BCI performance generalized well after accounting for experience.

neuroscience↗