From classical signal theory to physiological intelligence.
Signals define the perception layer of Neuroide. This topic covers how information is gathered from raw measurements and sensory streams across digital signal theory, biosignals, physiological sensing, EEG, EDA, ECG, PPG, and multimodal data.
Sampling, transforms, filtering, stochastic signals, spectral estimation, wavelets, and state-space views of time series.
ECG, EEG, EMG, EDA, respiration, PPG, sleep data, wearable sensing, and multimodal physiological measurement.
Sequence learning, feature engineering, self-supervision, transfer across devices, and subject-specific adaptation.
Monitoring, anomaly detection, digital health, and machine intelligence grounded in noisy human-centered data.
Physiological Signals as Dynamic Systems
An introduction to physiological data as partial observations of adaptive, multiscale, and coupled biological systems.
Classical Signal Concepts for Physiological Data
Sampling, filtering, spectra, time-frequency methods, detection, and validation for serious physiological signal processing.
Self-Supervised Representation Learning for Human Physiological Data
Predictive, contrastive, masked, and multimodal objectives for scalable biosignal pretraining across ECG, EEG, and human physiological sensing.
Multimodal Biosignal Foundation Models
Cross-modal pretraining, modality-robust interfaces, and generalizable health inference across heterogeneous physiological sensing.
Advanced Signal Processing for Physiological Data
Adaptive filtering, source separation, state-space models, nonlinear methods, and sparse inference for physiological signals.