Dr. Eagleman is interested in understanding brain states that exhibit altered states of consciousness. In particular she is interested in how different anesthetic agents interact with different neuromodulatory systems to produce behavioral outcomes such as sedation and unconsciousness. She is currently exploring different computational approaches to quantifying anesthetic depth using EEG.

Dr. Delorme is focused on the statistical analysis of EEG recorded during various cognitive tasks. He developed the free EEGLAB software for advanced analysis of EEG signals in collaboration with Scott Makeig, software which is now amongst the most used in EEG research worldwide.

Dr. Gramfort works on statistical machine learning, signal and image processing, optimization, scientific computing and software engineering with primary applications in brain functional imaging (MEG, EEG, fMRI).

Dr. Pernet focuses on methodological development that allows neuroimaging (MRI, PET, EEG) to be applied in day-to-day clinical settings. His interests are in studying cognition (vision, audition, and language), brain disease (stroke, brain tumour, development disorders). He is also the lead author of the Brain Imaging Data Structure (BIDS) framework for EEG described here.

Dr. Picone leads the Neural Engineering Data Consortium (NEDC) that generates and curates big data resources to support application of state of the art machine learning algorithms to bioengineering problems. NEDC distributes a variety of resources including signal and image data, software to facilitate common evaluations and visualization tools.

Dr. Mullen’s work has focused on the use of machine learning and adaptive system identification techniques applied to non-invasive and intracranial electrophysiological data (e.g. EEG, ECoG), to detect mental states and neuronal pathologies. He has advanced widely used software for neural signal analysis and state decoding and lead high-profile mobile brain imaging projects such as the “Glass Brain”.

Dr. Obeid focuses on recording and decoding signals from neural tissue including applications for brain computer interfaces, brain-in-a-dish studies, and traumatic brain injury. His lab has made extensive use of Field Programmable Gate Array (FPGA) technology to make and decode massively parallel neural recordings as well as methods from speech and audio processing in the analysis of EEG.

Dr. Plenz studies the self-organization of neuronal networks at different scales and across species from rodents to monkey to humans using a combination of electrophysiological and imaging techniques and modeling. Particularly, he has pioneered the statistical description of neuronal avalanche dynamics and their relationship to our understanding of brain states.

Dr. Thiagarajan is interested in the application of EEG at scale to enable the understanding of inter and intra individual variability in brain dynamics across diverse human populations, and their relationship to environment and behavioral outcome.

Dr. Voytek studies the role of neural oscillations and neural noise in cognition, aging, and disease using both EEG and ECoG recordings and large scale data-mining to discover neural features that are informative of cognitive states.

Dr. Bastos studies top-down and bottom-up interactions in the visual and attention systems, their mechanisms, and how they are modulated by exogenous (e.g., salience) and endogenous factors (e.g., attention, expectation) at different scales of measurement from LFP to EEG.

Dr. Newson(formerly Summerfield) has research interests in assessment of cognitive capabilities and mental health symptoms and their signatures in the EEG. She is particularly interested in cross-symptom and disorder paradigms. She has a background in Cognitive Neuroscience.

Dr. Subramaniyam’s interest lies in the development of signal processing algorithms and their application to EEG, MEG and other neural time series.

Dr. Prerau’s interests focus on experimental and computational approaches to understanding the neural correlates of consciousness in humans—specifically how sleep affects the brain. He specializes in developing novel statistical signal processing algorithms for the analysis of neural data, with direct applications to basic science, biomarker discovery, and medical device development.

Dr. Colombo explores analytical approaches to the EEG signal aimed at identifying the presence/absence of conscious experience during anesthesia and distinguishing the specific molecular targets of different anesthetic compounds. He is also interested in the understanding states of insomnia and sleep using EEG.

Dr. Bosl’s primary research focus is on developmental neuroscience and pediatric psychiatry. He is leading a series of projects to discover digital biomarkers in EEG signals for very early detection and monitoring of autism and other neurodevelopmental disorders. This work is being done primarily through Boston Children’s Hospital in the Computational Health Informatics Program.