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Prefrontal High Gamma in ECoG Tags Periodicity of Musical Rhythms in Perception and Imagination

The Potential of Stereotactic-EEG for Brain-Computer Interfaces: Current Progress and Future Directions

Generating Natural, Intelligible Speech From Brain Activity in Motor, Premotor, and Inferior Frontal Cortices

Estimating Cognitive Workload in an Interactive Virtual Reality Environment Using EEG

Speech synthesis from ECoG using densely connected 3D convolutional neural networks

Interpretation of convolutional neural networks for speech spectrogram regression from intracranial recordings

Biosignal-based Spoken Communication: A Survey

Optimization of Checkerboard Spatial Frequencies for Steady-State Visual Evoked Potential Brain-Computer Interfaces

Spatio-Temporal Progression of Cortical Activity Related to Continuous Overt and Covert Speech Production in a Reading Task

Brain-Computer Interfaces

EGRB 603. Biomedical Signal Processing

EGRB 601. Numerical Methods and Modeling in Biomedical Engineering

EGRB 308. Biomedical Signal Processing

Dean J. Krusienski received the B.S., M.S., and Ph.D. degrees in electrical engineering from The Pennsylvania State University, University Park, PA. He completed his postdoctoral research at the New York State Department of Health’s Wadsworth Center Brain-Computer Interface (BCI) Laboratory in Albany, NY. His primary research focus is on the application of advanced signal processing and pattern recognition techniques to brain-computer interfaces, which allow individuals with severe neuromuscular disabilities to communicate and interact with their environments using their brainwaves. His research interests include decoding and translation of neural signals, digital signal and image processing, machine learning, evolutionary algorithms, artificial neural networks, and biomedical and musical applications. His research is supported by the National Science Foundation (NSF), the National Institutes of Health (NIH), and the National Institute of Aerospace (NIA)/NASA.

EEG Analysis

Signal Processing

Machine Learning

Neuroprosthetics

ASPEN Lab

Google Scholar

EAGER: EEG-based Cognitive-state Decoding for Interactive Virtual Reality

US-German Data Sharing Proposal: CRCNS Data Sharing: REvealing SPONtaneous Speech Processes in Electrocorticography (RESPONSE)

EAGER: Investigating the Neural Correlates of Musical Rhythms from Intracranial Recordings

CHS: Small: A Hybrid Brain-Computer Interface for Behaviorally Non-Responsive Patients

Classifier Development forSteady-State Visually Evoked Potential Data

MRI Consortium: Acquisition of an Integrated System of Instruments for Multichannel Biopotential Recording of In-vitro and In-vivo Experiments

HCC: Medium: RUI: Control of a Robotic Manipulator via a Brain-Computer Interface

Professor and Graduate Program Director, Department of Biomedical Engineering | B.S., M.S., Ph.D., The Pennsylvania State University

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