Population Transformer: Learning Population-level Representations of Intracranial Activity.

We present a self-supervised framework that learns population-level codes for intracranial neural recordings at scale, unlocking the benefits of representation learning for a key neuroscience recording modality. The Population Transformer (PopT) lowers the amount of data required for decoding experiments, while increasing accuracy, even on never-before-seen subjects and tasks. We address two key challenges in developing PopT: sparse electrode distribution and varying electrode location across patients. PopT stacks on top of pretrained representations and enhances downstream tasks by enabling learned aggregation of multiple spatially-sparse data channels. Beyond decoding, we interpret the pretrained PopT and fine-tuned models to show how it can be used to provide neuroscience insights learned from massive amounts of data. We release a pretrained PopT to enable off-the-shelf improvements in multi-channel intracranial data decoding and interpretability, and code is available at https://github.com/czlwang/PopulationTransformer.

Decoding motor plans using a closed-loop ultrasonic brain-machine interface.

Brain-machine interfaces (BMIs) enable people living with chronic paralysis to control computers, robots and more with nothing but thought. Existing BMIs have trade-offs across invasiveness, performance, spatial coverage and spatiotemporal resolution. Functional ultrasound (fUS) neuroimaging is an emerging technology that balances these attributes and may complement existing BMI recording technologies. In this study, we use fUS to demonstrate a successful implementation of a closed-loop ultrasonic BMI. We streamed fUS data from the posterior parietal cortex of two rhesus macaque monkeys while they performed eye and hand movements. After training, the monkeys controlled up to eight movement directions using the BMI. We also developed a method for pretraining the BMI using data from previous sessions. This enabled immediate control on subsequent days, even those that occurred months apart, without requiring extensive recalibration. These findings establish the feasibility of ultrasonic BMIs, paving the way for a new class of less-invasive (epidural) interfaces that generalize across extended time periods and promise to restore function to people with neurological impairments.

Affective response to volitional input perturbations in obstacle avoidance and target tracking games.

We present the use of two game-like tasks, Catnip and Dinorun, to explore affective responses to volitional control perturbations. We analyze behavioral and physiological measures with the self-assessment manikin (SAM), pupillometry, and electroencephalography (EEG) responses to provide intratrial emotional state as well as inter-trial correlates with selfreported survey responses. We find that subject gameplay characteristics significantly correlate with valence and dominance scores for both games, and that perturbations to the games produce a measurable decrease in response scores for Dinorun. During perturbation events, pupillometry analysis reveals considerable SAM-agnostic dilation, with stronger responses in more rigid trialized event structures. Furthermore, analyses of neural activity from central and parietal regions demonstrate significant measurable evoked responses to perturbed events across the majority of subjects for both games. By introducing perturbations, this set of experiments and analyses inform and enable further studies of affective responses to the loss of volitional control during engaging, game-like tasks.