Specializing in continuous and discrete signals and systems with applications to signal detection and feature extraction for acoustics, sensory perception, speech & language processing, biophysics, and brain-computer interface design
Generating valid predictions from noisy or sparse data begins with a solid understanding of theory & design and access to a variety of analysis tools to facilitate practical application.
Time-series analysis, time-frequency analysis, linear and non-linear decomposition methods including spectral and spatial decomposition
Unsupervised and semi-supervised machine learning, statistical design and analysis, neural networks including shallow and deep learning
Theoretical and applied neuroscience, neurophysiological processing and biophysics, electromagnetic volume conduction including finite and boundary element methods
Time-frequency reconstructions of brainwave activity while listening to speech can be used to extract encoded features of the speech sounds.
Human sentence processing requires accurate and rapid predictions about linguistic information.
Semi-supervised machine learning can be used to identify predictors of speech development from brain activity in infants.
Modulatory brain systems and neural networks employ complex processes for rapid, real-time updating of sensory information.
The analysis of dynamical signals & systems extends further than just brain processing. Get in touch to learn more about custom algorithm development or how these methods may apply to your data.