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Intent inferral on a hand orthosis for stroke patients is challenging due to the difficulty of data collection. Additionally, EMG signals exhibit significant variations across different conditions, sessions, and subjects, making it hard for classifiers to generalize. Traditional approaches require a large labeled dataset from the new condition, session, or subject to train intent classifiers; however, this data collection process is burdensome and time-consuming. In this paper, we propose ChatEMG, an autoregressive generative model that can generate synthetic EMG signals conditioned on prompts (i.e., a given sequence of EMG signals). ChatEMG enables us to collect only a small dataset from the new condition, session, or subject and expand it with synthetic samples conditioned on prompts from this new context. ChatEMG leverages a vast repository of previous data via generative training while still remaining context-specific via prompting. Our experiments show that these synthetic samples are classifier-agnostic and can improve intent inferral accuracy for different types of classifiers. We demonstrate that our complete approach can be integrated into a single patient session, including the use of the classifier for functional orthosis-assisted tasks. To the best of our knowledge, this is the first time an intent classifier trained partially on synthetic data has been deployed for functional control of an orthosis by a stroke survivor.

Background and Purpose: MRI is crucial for multiple sclerosis (MS), but the relative value of portable ultra-low field MRI (pULF-MRI), a technology that holds promise for extending access to MRI, is unknown. We assessed white matter lesion (WML) detection on pULF-MRI compared to high-field MRI (HF-MRI), focusing on blinded assessments, assessor self-training, and multiplanar acquisitions., Methods: Fifty-five adults with MS underwent pULF-MRI following their HF-MRI. Two neuroradiologists independently assessed pULF-MRI images in an evaluation process, including initial assessment blinded to HF-MRI, self-training with reference to HF-MRI and evaluation of 20 cases with additional T2-fluid-attenuated inversion recovery in an additional plane. A third rater conducted cross-referenced analysis with HF-MRI data to determine true-positive lesions, false-positive areas, and case-level sensitivity and positive predictive value., Results: The mean age of participants was 50 years (standard deviation: 11; 74% women). Initially, Rater 2 marked more false-positive areas than Rater 1 (p = 0.003). After self-training, both raters embraced a conservative approach, with Rater 2 marking fewer false-positive areas (p = 0.01). Both raters maintained 100% case-level sensitivity and positive predictive value for detecting at least one WML, particularly in periventricular areas. Multiplanar acquisitions reduced both false-positive areas and true-positive lesions. True-positive lesions and false-positive areas had similar contrast-to-noise ratios in the juxtacortical region (p = 0.73) but not in periventricular, deep parenchymal regions (p = 0.004, p = 0.01)., Conclusion: With adequate training, radiological interpretation of pULF-MRI has high sensitivity and positive predictive value for MS lesions but should be approached conservatively. These results suggest utility for patient triage, potentially reducing diagnostic delay, and screening high-risk individuals., (© 2025 The Author(s). Journal of Neuroimaging published by Wiley Periodicals LLC on behalf of American Society of Neuroimaging.)