Dean Krusienski, Ph.D.

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


Focusing on neural signal processing and analysis for the development of brain-computer interfaces and neuroprosthetic devices.




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.

Areas of Expertise

EEG Analysis
Brain-Computer Interfaces
Signal Processing
Machine Learning


The Pennsylvania State University

Doctor of Philosophy

Electrical Engineering


The Pennsylvania State University

Master of Science

Electrical Engineering


The Pennsylvania State University

Bachelor of Science

Electrical Engineering


minor in Biomedical Engineering

Research Focus

Brain-Computer Interfaces

Focusing on neural signal processing and analysis for the development of brain-computer interfaces and neuroprosthetic devices.

Research Grants

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



The increasing availability of affordable, high-performance virtual reality (VR) headsets creates great potential for applications including education, training, and therapy. In many applications, being able to sense a user's mental state could provide key benefits. For instance, VR environments could use brain signals such as the electroencephalogram (EEG) to infer aspects of the user's mental workload or emotional state; this, in turn, could be used to change the difficulty of a training task to make it better-suited to each user's unique experience. Using such EEG feedback could be valuable not just for training, but in improving people's performance in real applications including aviation, healthcare, defense, and driving. This project's goal is to develop methods and algorithms for integrating EEG sensors into current VR headsets, which provide a logical and unobtrusive framework for mounting these sensors. However, there are important challenges to overcome. For instance, EEG sensors in labs are typically used with a conducting gel, but for VR headsets these sensors will need to work reliably in "dry" conditions without the gel. Further, in lab settings, motion isn't an issue, but algorithms for processing the EEG data will need to account for people's head and body motion when they are using headsets.

To address these challenges, the project team will build on recent advances in dry EEG electrode technologies and motion artifact suppression algorithms, focusing on supporting passive monitoring and cognitive state feedback. Such passive feedback is likely to be more usable in virtual environments than active EEG feedback, both because people will be using other methods to interact with the environment directly and because passive EEG sensing is more robust to slower response times and decoding errors than active control. Prior studies have demonstrated the potential of EEG for cognitive-state decoding in controlled laboratory scenarios, but practical EEG integration for closed-loop neurofeedback in interactive VR environments requires addressing three critical next questions: (1) can more-practical and convenient EEG dry sensors achieve comparable results to wet sensors?, (2) can passive EEG cognitive-state decoding be made robust to movement-related artifacts?, and (3) can these decoding schemes be generalized across a variety of cognitive tasks and to closed-loop paradigms? To address these questions, classical cognitive tasks and more-complex sim

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



The uniquely human capability to produce speech enables swift communication of abstract and substantive information. Currently, nearly two million people in the United States, and far more worldwide, suffer from significant speech production deficits as a result of severe neuromuscular impairments due to injury or disease. In extreme cases, individuals may be unable to speak at all. These individuals would greatly benefit from a device that could alleviate speech deficits and enable them to communicate more naturally and effectively. This project will explore aspects of decoding a user's intended speech directly from the electrical activity of the brain and converting it to synthesized speech that could be played through a loudspeaker in real-time to emulate natural speaking from thought. In particular, this project will uniquely focus on decoding continuous, spontaneous speech processes to achieve more natural and practical communication device for the severely disabled.

The complex dynamics of brain activity and the fundamental processing units of continuous speech production and perception are largely unknown, and such dynamics make it challenging to investigate these speech processes with traditional neuroimaging techniques. Electrocorticography (ECoG) measures electrical activity directly from the brain surface and covers an area large enough to provide insights about widespread networks for speech production and understanding, while simultaneously providing localized information for decoding nuanced aspects of the underlying speech processes. Thus, ECoG is instrumental and unparalleled for investigating the detailed spatiotemporal dynamics of speech. The research team's prior work has shown for the first time the detailed spatiotemporal progression of brain activity during prompted continuous speech, and that the team's Brain-to-text system can model phonemes and decode words. However, in pursuit of the ultimate objective of developing a natural speech neuroprosthetic for the severely disabled, research must move beyond studying prompted and isolated aspects of speech. This project will extend the research team's prior experiments to investigate the neural processes of spontaneous and imagined speech production. In conjunction with in-depth analysis of the recorded neural signals, the researchers will apply customized ECoG-based automatic speech recognition (ASR) techniques to facilitate the analysis of the large amount of phones occurring in contin

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



The project will develop an offline and then a real-time brain computer interface to detect rhythms that are imagined in people's heads, and translate these rhythms into actual sound. The project builds upon research breakthroughs in electrocorticographic (ECoG) recording technology to convert music that is imagined into synthesized sound. The project researchers will recruit from a specialized group of people for this project, specifically patients with intractable epilepsy who are currently undergoing clinical evaluation of their condition at the Mayo Clinic in Jacksonville, Florida, and are thus uniquely prepared to use brain-computer interfaces based on ECoG recording techniques. This is a highly multidisciplinary project that will make progress towards developing a "brain music synthesizer" which could have a significant impact in the neuroscience and musical domains, and lead to creative outlets and alternative communication devices and thus life improvements for people with severe disabilities.

Most brain-computer interfaces (BCIs) use surface-recorded electrophysiological measurements such as surface-recorded electroencephalogram (EEG). However, while some useful signals can be extracted from such surface techniques, it is nearly impossible to accurately decode from such signals the intricate brain activity involved in activities such as language with the detail needed to achieve a natural, transparent translation of thought to device control. On the contrary, intracranial electrodes such as ECoG are closer to the source of the desired brain activity, and can produce signals that, compared to surface techniques, have superior spatial and spectral characteristics and signal-to-noise ratios. Research has already shown that intracranial signals can provide superior decoding capabilities for motor and language signals, and for BCI control. Because complex language and auditory signals (both perceived and imagined) have been decoded using intracranial activity, it is conceivable to decode perceived and imagined musical content from intracranial signals. This project will attempt to similarly use ECoG to decode perceived and imagined musical content from intracranial signals as has been done for language and auditory signals.

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EGRB 603. Biomedical Signal Processing

Explores theory and application of discrete-time signal processing techniques in biomedical data processing. Includes discrete-time signals and systems, the Discrete/Fast Fourier Transforms (DFT/FFT), digital filter design and implementation, and an introduction into processing of discrete-time random signals.

EGRB 601. Numerical Methods and Modeling in Biomedical Engineering

The goal of this course is to develop an enhanced proficiency in the use of computational methods and modeling, to solve realistic numerical problems in advanced biomedical engineering courses and research, as well careers. The course will discuss and students will develop advanced technical skills in the context of numerical data analysis and modeling applications in biology and medicine. An important component of this course is developing problem-solving skills and an understanding of the strengths and weaknesses of different numerical approaches applied in biomedical engineering applications.

EGRB 308. Biomedical Signal Processing

Explores the basic theory and application of digital signal processing techniques related to the acquisition and processing of biomedical and physiological signals including signal modeling, AD/DA, Fourier transform, Z transform, digital filter design, continuous and discrete systems.

Selected Articles

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


SA Herff, C Herff, AJ Milne, GD Johnson, JJ Shih and DJ Krusienski


Rhythmic auditory stimuli are known to elicit matching activity patterns in neural populations. Furthermore, recent research has established the particular importance of high-gamma brain activity in auditory processing by showing its involvement in auditory phrase segmentation and envelope tracking. Here, we use electrocorticographic (ECoG) recordings from eight human listeners to see whether periodicities in high-gamma activity track the periodicities in the envelope of musical rhythms during rhythm perception and imagination. Rhythm imagination was elicited by instructing participants to imagine the rhythm to continue during pauses of several repetitions. To identify electrodes whose periodicities in high-gamma activity track the periodicities in the musical rhythms, we compute the correlation between the autocorrelations (ACCs) of both the musical rhythms and the neural signals. A condition in which participants listened to white noise was used to establish a baseline. High-gamma autocorrelations in auditory areas in the superior temporal gyrus and in frontal areas on both hemispheres significantly matched the autocorrelations of the musical rhythms. Overall, numerous significant electrodes are observed on the right hemisphere. Of particular interest is a large cluster of electrodes in the right prefrontal cortex that is active during both rhythm perception and imagination. This indicates conscious processing of the rhythms’ structure as opposed to mere auditory phenomena. The autocorrelation approach clearly highlights that high-gamma activity measured from cortical electrodes tracks both attended and imagined rhythms.

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

Frontiers in Neuroscience, (Neuroprosthetics)

C Herff, DJ Krusienski, P Kubben


Stereotactic electroencephalogaphy (sEEG) utilizes localized, penetrating depth electrodes to measure electrophysiological brain activity. It is most commonly used in the identification of epileptogenic zones in cases of refractory epilepsy. The implanted electrodes generally provide a sparse sampling of a unique set of brain regions including deeper brain structures such as hippocampus, amygdala and insula that cannot be captured by superficial measurement modalities such as electrocorticography (ECoG). Despite the overlapping clinical application and recent progress in decoding of ECoG for Brain-Computer Interfaces (BCIs), sEEG has thus far received comparatively little attention for BCI decoding. Additionally, the success of the related deep-brain stimulation (DBS) implants bodes well for the potential for chronic sEEG applications. This article provides an overview of sEEG technology, BCI-related research, and prospective future directions of sEEG for long-term BCI applications.

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

Front. Neurosci.

C Herff, L Diener, M Angrick, E Mugler, M Tate, M Goldrick, DJ Krusienski, M Slutzky, T Schultz


Neural interfaces that directly produce intelligible speech from brain activity would allow people with severe impairment from neurological disorders to communicate more naturally. Here, we record neural population activity in motor, premotor and inferior frontal cortices during speech production using electrocorticography (ECoG) and show that ECoG signals alone can be used to generate intelligible speech output that can preserve conversational cues. To produce speech directly from neural data, we adapted a method from the field of speech synthesis called unit selection, in which units of speech are concatenated to form audible output. In our approach, which we call Brain-To-Speech, we chose subsequent units of speech based on the measured ECoG activity to generate audio waveforms directly from the neural recordings. Brain-To-Speech employed the user's own voice to generate speech that sounded very natural and included features such as prosody and accentuation. By investigating the brain areas involved in speech production separately, we found that speech motor cortex provided more information for the reconstruction process than the other cortical areas.

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