Contravariance Theory methodology — formal proof that minimal DNN solutions to hard tasks exhibit strong alignment of privileged axes, with alignment "zipping" up the network hierarchy. Bridges NeuroAI convergent evolution theory and brain-DNN comparison methods.
Brain-Inspired Unsupervised Self-Reflection (BUS) framework for enhancing VLM reasoning without labeled data. Uses neuroscience-backed backward prediction to enable self-verification on unlabeled data.
Dynamic neural manifold architecture for flexible closed-loop control on neuromorphic hardware — mapping spiking activity to low-dimensional manifold trajectories with sensory-modulated geometry for explainable neural computation.
Dynamic neural manifolds methodology for flexible closed-loop control on neuromorphic hardware. Uses ring attractor networks with sensory-modulated control neurons (speed, shape, selection) to drive subspace rotations and fine-grained trajectory control in neural state space. Implemented on SpiNNaker 2 chip with robotic maze navigation validation.
Graph-regularized learning framework for EEG-based emotion recognition using psychological emotion topology. Conceptualizes emotions as nodes in a graph with edges encoding proximity based on dimensional emotion theories. Use when building EEG emotion classifiers, affective BCI systems, or applying graph regularization to psychological classification tasks.
Interpretable machine learning methodology for predicting Parkinson's disease motor severity (MDS-UPDRS Part III) from neuroimaging features — Quantitative Susceptibility Mapping (QSM) MRI and multiband multiecho resting-state fMRI Regional Homogeneity (ReHo). Uses SVR, Elastic Net, Random Forest, XGBoost with nested CV and SHAP interpretability. Full multimodal model explains 45.4% variance. QSH+c clinical model achieves 75% within ±5 UPDRS points. Activation: Parkinson's prediction, QSM MRI, ReHo fMRI, MDS-UPDRS, motor severity prediction, SHAP neuroimaging, multiband multiecho fMRI, interpretable ML Parkinson, quantitative susceptibility mapping
Sample-Adaptive Hyperbolic Graph Neural Network (SA-HGNN) for EEG-based depression recognition. Combines sample-adaptive graph construction with hyperbolic graph convolution and attention pooling to capture hierarchical brain network structure in EEG signals.
STST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Predictive Architecture for EEG self-supervised learning. Largest EEG foundation model (47,703 sessions, ages 5-81) using JEPA-style latent prediction with EMA tokenizer + auxiliary signal reconstruction. Rank 1 on NeuralBench for sex, age, psychopathology. Brain age gap correlates with cognitive efficiency. Activation: stst-jepa, eeg foundation model, brain age, self-supervised eeg, eeg self-supervised learning, JEPA eeg, EEG2Rep, brain space, neuralbench, brain age gap, cognitive efficiency