Lorem ipsum dolor sit amet, consectetur adipiscing elit. Maecenas blandit ligula eget tempus pulvinar. Nulla fermentum tortor ac lacinia lobortis. Phasellus bibendum ut felis sit amet eleifend.
Publications
The accuracy, night-to-night variability, and stability of frontopolar sleep EEG biomarkers. Levendowski DJ, Ferini-Strambi L, Gamaldo C, et al. J Clin Sleep Med, 2017; 13(6):791-803.
Comparison of EMG power during sleepfrom the submental and frontalis muscles. Levendowski DJ, St. Louis, E, Ferini-Strambi L, et al. Nat Sci Sleep 2018; 10:431-437.
A comparison between auto-scored apnea-hypopnea index and oxygen desaturation index in the characterization of positional obstructive sleep apnea. Levendowski DJ,Hamilton GS, St. Louis EK, et al. NatSci Sleep 2019; 11;69-78.
Agreement in supine sleep duration when measured from thechest, head and neck. Levendowski D,Veljkovic B, Ramos Angel E, et al. 30th annual meeting of the Australasian Sleep Association, Brisbane Australia, October17-20, 2018.
Interpreting in-home sleep biomarkers based on polysomnographic reference values. Levendowski DJ, Rosenberg R, Lucey BP et al. Sleep,2017;40:A845.
Non-inferiority between the overall and REM-related apnea-hypopnea indexes obtained by polysomnography and a forehead worn,auto-scored system. Levendowski DJ, Henninger D, Smith J, et al. Sleep, 2016;39:A380.
Agreement between polysomnography-derived sleep staging and auto-staging of signals acquired from three frontopolar sites. Levendowski DJ, Henninger D, Ramos E, et al. Sleep, 2016; 39:A103.
Retrospective cross-validation of automated sleep staging using electroocular recordings in patients with and without sleep disordered breathing. Levendowski DJ, Popovic D, Berka C, et al. Int Arch Med, 2012;5(1):21.
Scoring accuracy of automated sleep staging from a bipolar electroocular recording compared to manual scoring by multiple raters. Stepnowsky C, Levendowski D, Popovic D, et al. Sleep Med, 2013;14(11):1199-07
Automatic scoring of sleep stages and cortical arousals using two electrodes on the head: validation in healthy adults. Popovic D, KhooM, Westbrook P. J Sleep Res, 2014:23(2):211-21.
Comparison of a single-channel EEG sleep study to polysomnography. Lucey B, McLeland JS, Toedebusch CD et al. J Sleep Res, 2016;25(6):625-635.
Validation of a wireless, self-application, ambulatory electroencephalographic sleep monitoring device in healthy volunteers. Finan PH, Richards JM, Gamaldo CE, et al. J Clin Sleep Med, 2016;12(1):443-1451.
The need for a reliable Sleep EEG biomarker. Penzel T, Fietze I, Veauthier C. JCSM 2017; 13(6):771-772.
Portable sleep monitoring systems: Broadening the horizons. Covassin N, Somers VK. JCSM 2017; 13(6):773:774.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Maecenas blandit ligula eget tempus pulvinar. Nulla fermentum tortor ac lacinia lobortis. Phasellus bibendum ut felis sit amet eleifend.
Publications
Multi-night EEG recordings during sleep can provide important information in managing insomnia. Australasian Sleep Society Meeting 2015; Melbourne Australia.
Night to night variability in subjective and objective sleep quality metrics in patients evaluated for chronic insomnia. Levendowski DJ, Cetel M, Rosenberg R, et al. Sleep, 2014;37:188.
Night to night variability in sleep architecture and continuity in patients evaluated for chronic insomnia. Westbrook PR, Levendowski D, Cetel M, et al. Sleep, 2014;37:192.
Comparison of objective and subjective measures of awakenings in patients evaluated for chronic insomnia. Cetel M, Rosenberg R, Levendowski D, et al. Sleep, 2014;37:A191.
A systematic comparison of factors that could impact treatment recommendations for patients with Positional Obstructive Sleep Apnea (POSA). Levendowski DJ, Oksenberg A, Vicini C et al. Sleep Med 2018; 50:145-151.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Maecenas blandit ligula eget tempus pulvinar. Nulla fermentum tortor ac lacinia lobortis. Phasellus bibendum ut felis sit amet eleifend.
Publications
Effects of deep sedation on sleep in critically ill medical patients on mechanical ventilation. Jean R, Shah, P, Yudelevich E, et al. J Sleep Res 2019, in press.
Atypical sleep architecture evaluated in septic patients in the intensive care unit; a pilot study. Kotani T, Miyashita, Mori et al. American Thoracic Society, 2019, Dallas Tx
Can disrupted sleep affect mortality in the mechanically ventilated critically ill? Shah PC, Yudelevich E, Genese F, et al. American Thoracic Society, 2016, San Francisco, CA.
Sleep in the ICU: an analysis of sleep quality and quantity in mechanically ventilated patients. Yudelevich E, Fuhrman K, Ventura I, et al. Society of Critical Care Medicine, 2016, Orlando FL.
The influence of sepsis on sleep architecture in the intensive care unit. Genese F, Martillo M, Ventura I, et al. Society of Critical Care Medicine, 2016, Orlando FL.
Challenges of sleep in the ICU: The significance of sedatives on sleep architecture. Fuhrmann KA, Martillo M, Genese F, et al.: American Thoracic Society, 2016, San Francisco, CA.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Maecenas blandit ligula eget tempus pulvinar. Nulla fermentum tortor ac lacinia lobortis. Phasellus bibendum ut felis sit amet eleifend.
Publications
Objective measures of sleep and subjective symptoms in patients evaluated for chronic insomnia, grouped by three different depression criteria. Cunnington D, Levendowski D, Westbrook P, etal. Australasian Sleep Society Meeting, 2014, Perth Australia.
Differences in heart rate variability during REM and NREM sleep, a biomarker for depression? Levendowski DJ, Cetel M, Rosenberg R, et al. Sleep, 2014;37:187.
The effect of sleep deprivation on emotional memory consolidation in participants reporting depressive symptoms. Harrington MO, Nedberge KM, Durrant SJ. Neurobiol Learning Memory 2018; 152:10-19.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Maecenas blandit ligula eget tempus pulvinar. Nulla fermentum tortor ac lacinia lobortis. Phasellus bibendum ut felis sit amet eleifend.
Publications
Reduced non-rapid eye movement sleep is associated with tau pathology in early Alzheimer's disease. Lucey B, McCullough A, Landsness EC, et al. Sci Transl Med 2019; 11:(474): DOI 10.1126/scitranslmed.aau6550
Sleep and sleep position: potential implications for patients with neurodegenerative disease. Levendowski DJ, Gamaldo C, St. Louis E, et al. J Alzheimers Dis 2019; 67(2):631-638.
Characterization of candidate sleep biomarkers in neurodegenerative disease using frontopolar EEG. Levendowski DJ, Gamaldo C, St. Louis E, etal. JCSM 2019; submitted.
Assessment of sleep abnormalities in patients with neurodegenerative disease using an in-home sleep profiling system. Levendowski D, Berka C, Meghdadi A et al. Somnologie Suppl 1 2018; 16-17.
Characterization of sleep abnormalities in Alzheimer’s disease and mild cognitive impairment using an in-home sleep profiling system. Berka C, Levendowski D, Meghdadi A et al. AD/PD Focus Meeting, 2018, Torino Italy
Neurophysiological biomarkers for Alzheimer’s disease and mild cognitive impairment acquiredduring sleep and wake. Berka C, MeghdadiA, Rupp G, et al. AD/PD Focus Meeting, 2018, Torino Italy
A growing body of evidence suggests that EEG analyses, including both resting state and event-related stimulation protocols, may be useful in the development of Biomarkers for a number of neurological and psychiatric disorders.
Exploratory resting state and event related EEG biomarkers for MCI. Cross-sectional comparisons resting state EEG/fMRI/PET biomarkers for AD & MCI. Cross-sectional comparisons resting state EEG/fMRI/PET biomarkers for PD. Exploratory resting state EEG biomarkers for FTD behavioral and aphasia variants. Evaluation of investigational therapy using resting state EEG biomarker. Evaluation of investigational therapy using event related EEG biomarker
Publications
Electroencephalography to Assess Motor Control During Balance Tasks in People with Diabetes.
Petrofsky, J. et al. (2012) Diabetes Technology & Therapeutics, 14, 1–9.
Postural Sway and Rhythmic Electroencephalography Analysis of Cortical Activation During Eight Balance Training Tasks.
Tse, Y. et al. (2013). Medical Science Monitor: International Medical Journal of Experimental and Clinical Research, 19, 175–186.
Event-Related Potential Assessment of Cognitive Tasks in PTSD.
Tan, V. et al. (2012). Poster. Presented at the Society For Neuroscience, Chicago, IL.
Heart-Rate Variability and Power Spectral Densities as Neurophysiological Indices of Post-Traumatic Stress Disorder.
Smith, S. et al. (2015). Poster. Presented at the Society For Neuroscience, Chicago, IL.
Development of EEG Biomarkers for Alzheimer’s Disease.
Berka, C. et al. (2014). Poster. Presented at the GTC Biomarker Summit, San Diego, CA.
A Novel Portable Neurocognitive Biomarker Assessment for Parkinson’s Disease.
Korszen, S. et al. (2015). Poster. Presented at the GTC Biomarker Summit, San Diego, CA.
Combined Neurocognitive and EEG Biomarkers to Assess Effects of CNS Depressants & Stimulants.
Waninger, S. et al. (2015) Poster. Presented at the American Society of Clinical Psychopharmacology Annual Meeting, Miami, FL.
Multi-Modal Prediction of PTSD and Stress Indicators.
Rozgic, V. et al. (2014). IEEE International Conference on Acoustics, Speech and Signal Processing, 3636–3640.
Detection and Computations Analysis of Psychological Signals (DCAPS).
Ragsdale, C. (2014). Poster. Presented at the DARPA I2O Demo Day.
Event-Related Potentials During Sustained Attention and Memory Tasks: Utility as Biomarkers for Mild Cognitive Impairment.
Waninger, S. et al. (2018). Alzheimer’s & Dementia : Diagnosis, Assessment & Disease Monitoring, 10, 452–460
Neurophysiological Indices of Cannabis and Impairment.
Smith, S. et al. (2018). Presented at the Society For Neuroscience, San Diego, CA.
EEG Biomarkers For Frontotemporal Dementia Differentiate Between Healthy Control And Alzheimer’s Disease.
Waninger, S. (2018). Presented at the Society For Neuroscience, San Diego, CA
The Relationship between Real-Time EEG Engagement, Distraction and Workload Estimates and Simulator-Based Driving Performance.
Marcotte,T. et al. (2017). Proceedings of the 7th International Driving Symposium on Human Factors in Driver Assessment, 411–417.
With B-Alert BCI development tools, developers are provided rapid prototyping tools to fit the right approach with the right task. Within clinical environments, the results are recovery of lost function and accelerated healing. In other applications, BCIs facilitate more efficient interactions between man and machine.
Publications
An Adaptive Brain Actuated System for Augmenting Rehabilitation.
Roset, Scott, et al. (2014). Frontiers in Neuroscience, 8, 415.
Changes in cortical activation during BCI use in chronic spinal cord injury.
Xie, Ziqian, et al. (2014).6th International Brain-Computer Interface Conference, Graz, Austria.
Cognitive Skills Assessment during Robot-Assisted Surgery: Separating Wheat from Chaff.
Guru, Khurshid, et al. (2014). BJU international, 115(1).
In a Blink of an Eye and a Switch of a Transistor: Cortically Coupled Computer Vision.
Sajda, Paul, et al. (2010). Proceedings of the IEEE, 98, 462 - 478.
Non-invasive EEG-based motor and language mapping while playing a Kinetic based computer game.
Scherer, Reinhold, et al. (2013). IEEE Transactions on Computational Intelligence and AI in Games, 5, 155-163.
Dynamic Feature Selection in a Reinforcement Learning Brain Controlled FES.
Roset, S. (2014) Open Access Dissertations, Paper 1240.
Implementation of a Closed-Loop Real-Time EEG-Based Drowsiness Detection System: Effects of Feedback Alarms on Performance in a Driving Simulator.
Berka, Chris, et al. (2005). 1st International Conference on Augmented Cognition, Las Vegas, NV.
Effects of User Mental State on EEG-BCI Performance.
Myrden, A. et al. (2015). Frontiers in Human Neuroscience, 9(308), 1–11.
A Brain–Computer Interface (BCI) for the Detection of Mine-Like Objects in Sidescan Sonar Imagery.
Barngrover, C. et al. (2016). IEEE Journal of Oceanic Engineering, 41(1), 123–138.
The Team NeuroDynamics platform provides biometric measures that reveal the underlying reasons as to why teams, groups, or individuals within them perform the way they do. The synchronization of up to six B-Alert mobile EEG systems delivers quantitative, real-time and objective psychophysiological metrics for understanding social interactions and team metacognitve states.
Publications
Emergent Leadership and Team Engagement: An Application of Neuroscience Technology and Methods.
Waldman, D. et al. (2013). Academy of Management Proceedings, Orlando, FL.
Assessing Neural Synchrony in Tutoring Dyads.
Stone, B. et al. (2014). International Conference on Augmented Cognition, 167-178.
Modeling the Neurodynamic Complexity of Submarine Navigation Teams.
Stevens, R. et al. (2013). Computational & Mathematical Organization Theory, 19(3), 346–369.
Cognitive Neurophysiologic Synchronies: What Can They Contribute to the Study of Teamwork?
Stevens, R. et al. (2012). Human Factors, 54(4), 489-502.
Neurophysiological Estimation of Team Psychological Metrics.
Stikic, M. et al. (2013) Foundations of Augmented Cognition, 8027.
The Organizational Neurodynamics of Teams.
Stevens, R. et al. (2013). Nonlinear Dynamics, Psychology, and Life Sciences, 17, 67–86.
Neuroscience and Team Processes.
Waldman, D. et al. (2015). Organizational Neuroscience, 7, 277–294.
Neuroenhancement in Tasks, Roles, and Occupations.
Stikic, M. et al. (2015). In Organizational Neuroscience, Vol. 7, pp. 169–186.
On the Road to Autonomy: Evaluating and Optimizing Hybrid Team Dynamics.
Berka, C. (2017). In Autonomy and Artificial Intelligence: A Threat or Savior?, 245–262.
The ‘mental game’ of sports, performance, and learning reaches new levels through EEG-based brain mapping techniques. By understanding the biological differences between expert and novice cognitive states during performance, teachers can teach better and students can learn faster.
Publications
Accelerating Training Using Interactive Neuro- Educational Technologies: Applications to Archery, Golf and Rifle Marksmanship.
Berka, C. et al. (2010). International Journal of Sport and Society, 1, 87–104.
Zen and the Art of Genius.
Adee, S. (2012). New Scientist, (213), 32–35.
Characterizing the Psychophysiological Profile of Expert and Novice Marksmen.Pojman, N. et al. (2009).
Presented at the 13th International Conference on Human-Computer Interaction, San Diego, CA.
Neurotechnology to Accelerate Learning.
Behneman, A. et al. (2012). IEEE Pulse, 3(1), 60–63.
Cognitive Skills Assessment During Robot-Assisted Surgery: Separating Wheat from Chaff.
Guru, K. et al. (2014). BJU International, 115, 166–174.
Characterizing the Expertise and Proficiency of Surgeons Using EEG-based Metrics.
Korszen, S. et al. (2014) Poster.
Modeling Temporal Sequences of Cognitive State Changes Based on a Combination of EEG-Engagement, EEG-Workload, and Heart Rate Metrics.
Stikic, M. et al. (2014). Frontiers in Neuroscience, 8, 1-14.
Identifying Psychophysiological Indices of Expert Vs. Novice Performance in Deadly Force Judgment and Decision Making. Johnson, R. et al. (2014). Frontiers in Human Neuroscience, 8, 1-13.
Assessing a Learning Process with Functional ANOVA Estimators of EEG Power Spectral Densities.
Gutiérrez,D., & Ramírez Moreno, M. A. (2015). Cognitive Neurodynamics, 10, 175–183.
Curriculum for Accelerated Learning Through Mindfulness (CALM).
Skinner, A. et al. (2018).
Market researchers require verifiable, accurate, and consistent biometric signals to ensure valid interpretation of their target audience’s responses. In addition to being mobile, ease-to-use, and comfortable for hours, B-Alert EEG technologies have an established track record for high quality, reliable signals. Combined with the suite of validated metrics and analysis tools, they provide a means for obtaining authentic insights into consumer decision making.
Publications
Ericsson Mobility Report: On the Pulse of the Networked Society.
Cerwall, P. (2016).
Aligning Brain Activity and Sketch in Multi-Modal CAD Interface.
Besharat, S., & Tarkesh, E. (2014). ASME 2014 International Design Engineering Technical Conference, 1–7.
Reducing Cognitive Workload During 3D Geometry Problem Solving with an App on iPad.
Bertolo, D., Dinet, J., & Vivian, R. (2014). Science and Information Conference, 896–900.
Consumers’ Cognitive Lock-in on Websites: Evidence from a Neurophysiological Study.
Sénécal, S. et al. (2015). Journal of Internet Commerce, 14, 277–293.
How Virtual Reality Facilitates Social Connection [Facebook for Business].
Neurons Inc. (2017). Facebook IQ. - B-Alert and VR
A growing body of evidence suggests that EEG analyses, including both resting state and event-related stimulation protocols, may be useful in the development of Biomarkers for a number of neurological and psychiatric disorders.
Publications
Behavioral and Neurophysiological Signatures of Benzodiazepine-Related Driving Impairments.
Stone, B. et al. (2015). Frontiers in Psychology, 6, 1799.
Drowsiness/Alertness Algorithm Development and Validation Using Synchronized EEG and Cognitive Performance to Individualize a Generalized Model.
Johnson, R. et al. (2011). Biological Psychology, 87(2), 241–250.
EEG Correlates of Task Engagement and Mental Workload in Vigilance, Learning, and Memory Tasks.
Berka, C. et al. (2007). Aviation, Space, and Environmental Medicine, 78(5 Suppl), B231-244.
EEG-Derived Estimators of Present and Future Cognitive Performance.
Stikic, M. et al. (2011). Frontiers in Human Neuroscience, Volume 5; 1-13.
Real-Time Analysis of EEG Indexes of Alertness, Cognition, and Memory Acquired with a Wireless EEG Headset.
Berka, C. et al. (2004). International Journal of Human–Computer Interaction, 17(2), 151–170.
