We design large-scale pretraining frameworks tailored for EEG. Unlike vision or language, EEG presents unique challenges: high inter-subject variability, electrode placement sensitivity, and subtle but clinically critical temporal patterns.

Real-time BCI pipelines using research-grade and consumer-grade EEG. The full stack: acquisition, conditioning, feature extraction, classification, and online adaptation.

The brain is a high-dimensional nonlinear dynamical system, and EEG captures its projection onto electrode space. We extract invariant geometric features: Lyapunov exponents, correlation dimensions, and recurrence quantification measures.

Distinguishing epilepsy from psychogenic non-epileptic seizures is one of the most challenging problems in clinical neurology, with misdiagnosis rates exceeding 20% globally.

SNNs for temporal sparse coding, reservoir computing for dynamical state estimation, and E/I-balanced attractor networks guided by biological plausibility principles to perform efficient signal modeling.

The Information Fidelity Bound provides a rigorous basis for quantifying mutual information rates in latent neural source processes, helping formalize representation learning metrics without relying on surrogate performance alone.

The prevailing wisdom in modern machine learning points towards larger networks and boundless datasets. For language and vision, this yields spectacular results. We argue that for medical diagnostics, particularly clinical EEG, this approach is severely handicapped.

Clinical data is sparse, heterogenous, and costly. By embedding domain knowledge into our signal extraction pipelines—adopting a data-centric methodology over a model-centric one—we achieve a more interpretable, stable, and accurate predictive model ready for real-world diagnostic applications.

A deep neural network trained on a hundred EEG scans will almost undoubtedly memorize the training set, failing to generalize to unseen hospitals or device variations. We examine techniques to reduce parameter count explicitly without diminishing representational capacity, finding an elegant balance between model limits and data sparsity.

While spectral bands (Alpha, Beta, Gamma) offer an intuitive look into cognitive states, they assume stationarity over specific windows. EEG signals trace chaotic trajectories. We highlight how measuring the rate of trajectory divergence can uncover robust computational biomarkers missed by standard Fourier methods.

Artifacts like eye-blinks (EOG) routinely ruin EEG recordings. Using heavy foundational models to scrub out noise is computationally impractical for portable BCI units. Our recent work pushes the boundary toward minimal-parameter structures—femtomodels—capable of generalizing artifact removal seamlessly across multiple environments.

When transferring a continuous analog neural signal into a low-dimensional discrete representation, information is inevitably lost. How do we ensure the lost information wasn't clinically vital? We break down the mathematical constraints necessary to preserve fidelity in machine representations.

An accuracy metric achieved during K-fold cross-validation is not an indicator of clinical readiness. Models consistently fail when moved to new wards, uncalibrated hardware, or diverse patient demographics. Preparing an AI tool for true clinical integration requires exhaustive evaluations against data-leakage, calibration drift, and interpretability requirements.