University of Central Florida

Pretrained neural-recording models differ in architecture and input format, so labs configure each one by hand before they can compare them. NS-Copilot puts nine EEG and spike models behind one natural-language interface, with planner, coder, controller and interpreter agents that select models, generate code and close the loop. Over 8 trials each on Alzheimer's, Parkinson's and working-memory benchmarks, its primary metrics were 57.16% macro F1, 71.32% balanced accuracy and 74.43%, above the listed baselines.

Pretrained neural-recording models differ in architecture and input format, so labs configure each one by hand before they can compare them. NS-Copilot puts nine EEG and spike models behind one natural-language interface, with planner, coder, controller and interpreter agents that select models, generate code and close the loop. Over 8 trials each on Alzheimer's, Parkinson's and working-memory benchmarks, its primary metrics were 57.16% macro F1, 71.32% balanced accuracy and 74.43%, above the listed baselines.

The study presents a sparsely supervised AI framework in which frozen ultrasound foundation model (USFM, ViT-B) embeddings of 768 dimensions per frame are passed to a lightweight bidirectional LSTM temporal head that outputs a 0–1 frame-quality score, trained with Gaussian soft labels that peak at expert-marked key frames and decay smoothly with frame distance (width σ = 5 frames); in subject-level five-fold cross-validation on 18 subjects and 323 ultrasound videos spanning nine controlled acquisition sweep types (about 45,500 frames), it selected a usable frame in 82.2% of sweeps containing key frames with a mean minimum distance of 3.07 frames, exceeding the strongest training-free baseline (USFM feature cosine similarity, 49.2%) and a hard-label model (71.9%).

The study presents a sparsely supervised AI framework in which frozen ultrasound foundation model (USFM, ViT-B) embeddings of 768 dimensions per frame are passed to a lightweight bidirectional LSTM temporal head that outputs a 0–1 frame-quality score, trained with Gaussian soft labels that peak at expert-marked key frames and decay smoothly with frame distance (width σ = 5 frames); in subject-level five-fold cross-validation on 18 subjects and 323 ultrasound videos spanning nine controlled acquisition sweep types (about 45,500 frames), it selected a usable frame in 82.2% of sweeps containing key frames with a mean minimum distance of 3.07 frames, exceeding the strongest training-free baseline (USFM feature cosine similarity, 49.2%) and a hard-label model (71.9%).