Intheon Community Projects
The Glass Brain
The world’s first interactive, real-time, high-resolution visualization of an active human brain, designed specifically for Virtual Reality.• Anatomically realistic and navigable high-resolution MRI-based 3D brain model.
• Integrates cortical brain activity and connectivity, computed in real-time from an EEG headset, with structural fiber tracts estimated from diffusion tensor imaging.
• Virtual-reality compatible (Oculus Rift).
• Over 250,000 views of the Glass Brain on YouTube!
Intheon Role: Tim Mullen co-led development of the Glass Brain with Adam Gazzaley (Director, UCSF Neuroscape Lab). The brain mapping software was implemented by Tim Mullen and Christian Kothe using SIFT, BCILAB, and LSL software which they developed at the Swartz Center for Computational Neuroscience, Inst. for Neural Computation, UCSD.
• Integrated into the EEGLAB software environment.
• Modular architecture including modules for data pre-processing, model fitting and validation, connectivity analysis, statistics, visualization, group analysis, and realistic simulations of EEG dynamics.
• Includes a suite of methods for dynamical system identification, including regularized vector autoregressionand linear and non-linear Kalman filtering.
• Over 15 measures of brain connectivity, including multivariate Granger causality, transfer function analysis, and coherence.
• Interactive visualization allowing analysis of source- or sensor-based connectivity across time, frequency, and spatial location.
Intheon Role: Tim Mullen developed SIFT as a graduate fellow at the Swartz Center for Computational Neuroscience, Inst. for Neural Computation, UCSD.
• Available as a plug-in for EEGLAB.
• GUI-driven or scriptable via MATLAB script.
• Facilitates rapid prototyping and evaluation of novel BCI paradigms.
• API support for real-time processing.
• Includes support for custom extensions.
• Supports batch processing for large study analysis.
Intheon Role: Christian Kothe developed BCILAB during his previous position at the Swartz Center for Computational Neuroscience, Inst. for Neural Computation, UCSD.
• Provides robust statistical measures of the spatial consistency of EEG dynamics across datasets.
• Available as a plug-in for EEGLAB.
• Transforms EEG into a 3-D cortical mapping method with near-cm resolution.
• Interactive cluster visualization and processing.
• Probabilistic multi-subject EEG independent component source comparison and inference.
Intheon Role: Nima Bigdely-Shamlo developed MPT during his PhD studies at the Swartz Center for Computational Neuroscience, Inst. for Neural Computation, UCSD.
• Open source MIT License.
• Provides unified collection of measurement time series over a network.
• Widely adopted by neurophysiological measurement device manufacturers.
• Sub-millisecond synchronization capable on typical LANs.
• Extensive range of supported measurement modalities including eye trackers, EEG systems, motion capture devices, keyboards, mice, trackballs, force plates and multimedia hardware.
• Provides a standard interface for data acquisition integration.
Intheon Role: Christian Kothe developed LSL during his previous position at the Swartz Center for Computational Neuroscience, Inst. for Neural Computation, UCSD.
• Built on well-established community supported ontologies for experimental neuroscience.
• Extensible, user friendly event description system.
• Tools available for tagging, visualization, autocompletion and comparison.
• Simplifies data mining over large experimental data sets.
Intheon Role: Intheon is the primary curator of HED standard. Open source HED tools are developed jointly by Prof. Kay Robbins (Univ. Texas, San Antonio) and Intheon.
EEG Study Schema (ESS) makes it easier for researcher in the field of EEG/BCI to package, share and automatize the analysis workflow of their study data.You can think of ESS as a “shipping container” for your EEG study data.• An XML-based specification
• Holds all the information necessary to analyze an EEG study: task and paradigm description, recording parameters, sensor locations, gender, handedness, age and group associations of subjects.
• Contains a table of HED tags for event codes.
• Both human- and machine-readable. The XML file may be readily formatted into a readable description of the EEG study.
Intheon Role: Intheon is the curator of ESS standard and the developer of open source ESS tools. The standardized EEG processing pipeline (PREP) is developed jointly by Prof. Kay Robbins (UTSA) and Intheon.
Intheon is a founding member of the BigEEG Consortium™ whose goal is to promote and facilitate large-scale analysis (e.g. meta-study) of EEG and other data modalities related to real-world neuroimaging. This is implemented through developing open standards and tools for event tagging and meta-data encapsulation, along with providing publicly available standardized data and workflows.Currently, there are two major standards being promoted by the consortium:
• Hierarchical Event Descriptor (HED) tags. For describing experimental and real-world events. See www.hedtags.org
• EEG Experiment Schema (ESS). For encapsulating EEG study meta-data. See www.eegstudy.org
For a list of studies currently available in ESS format please visit: www.studycatalog.org
In addition to these, we have released a standard EEG pre-processing toolbox for noisy channel detection and robust referencing, called PREP: http://vislab.github.io/EEG-Clean-Tools/
Intheon's Role: Intheon is a leading developer of ESS and HED technologies, and a founding member of the BigEEG Consortium™ along with Prof. Kay Robbins of UTSA.
• A free web service for remote visualisation of real-time sensor data.
• High sample rate, low latency streaming.
• Supports public and private sensor streams.
• Duplex stream transmission capable.
• Serial-to-socket transmission support.
• No server side coding required.
• Proxies, NATs and Firewall safe.
Intheon Role: Prior to joining Syntrogi, Aaron McCoy and Tomas Ward created SensorMonkey.
• Synchronous measurement of brain signals, behavior and environmental events.
• Supports a large and growing number of measurement devices and technologies.
• Native data structures for multi-modal, heterogeneously sampled, multiple data streams.
• Designed for computationally efficient processing of very large multi-modal datasets.
Intheon Role: Alejandro Ojeda developed MobiLab during his previous position at the Swartz Center for Computational Neuroscience, Inst. for Neural Computation, UCSD.
• Extensible plug in architecture facilitates third party toolbox enhancements.
• Integrated independent component analysis processing.
• Multi-format data importing/exporting.
• Interactive data visualization functions.
• Automated artifact rejection capabilities.
• Forward and inverse source modeling.
• GUI-driven or completely scriptable via Matlab.
Intheon Role: Members of Intheon’s founding team contributed substantially to the development and maintenance of the open source EEGLAB project during their previous positions at the Swartz Center for Computational Neuroscience, Inst. for Neural Computation, UCSD. Intheon members are frequent faculty in EEGLAB workshops worldwide.
(A selection of publications authored/co-authored by members of our team.)
Nima Bigdely-Shamlo, Jonathan Touryan, Alejandro Ojeda, Christian Kothe, Tim Mullen, Kay Robbins. Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies. NeuroImage, Volume 207, 2020. https://doi.org/10.1016/j.neuroimage.2019.116361
Nima Bigdely-Shamlo, Jonathan Touryan, Alejandro Ojeda, Christian Kothe, Tim Mullen, Kay Robbins, 2020. Automated EEG mega-analysis II: Cognitive aspects of event related features. NeuroImage, Volume 207, 2020. https://doi.org/10.1016/j.neuroimage.2019.116054
Robbins, K. A., Touryan, J., Mullen, T., Kothe, C., & Bigdely-Shamlo, N. How Sensitive are EEG Results to Preprocessing Methods: A Benchmarking Study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(5), 1081-1090, 2020. https://doi.org/10.1109/tnsre.2020.2980223
Fazel-Rezai, T. Mullen. Online Tracking of Canonical Brain Network Activation and Behavioral Prediction Using Bayesian Filtering. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019, pp. 4415-4420. https://doi.org/10.1109/SMC.2019.8913992.
Leuthardt Eric C., Moran Daniel W., Mullen Tim R. Defining Surgical Terminology and Risk for Brain Computer Interface Technologies. Frontiers in Neuroscience, 2021. https://www.frontiersin.org/article/10.3389/fnins.2021.599549.
Dehais F., Somon B., Mullen T., Callan D.E. A Neuroergonomics Approach to Measure Pilot’s Cognitive Incapacitation in the Real World with EEG. In: Ayaz H., Asgher U. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1201. Springer, Cham. https://doi.org/10.1007/978-3-030-51041-1_16
Dehais, I. Rida, R. N. Roy, J. Iversen, T. Mullen and D. Callan. A pBCI to Predict Attentional Error Before it Happens in Real Flight Conditions. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019, pp. 4155-4160. https://doi.org/10.1109/SMC.2019.8914010
Alejandro Ojeda, Kenneth Kreutz-Delgado, Tim Mullen. Fast and robust Block-Sparse Bayesian learning for EEG source imaging. NeuroImage, 2018. https://doi.org/10.1016/j.neuroimage.2018.03.048
Ibagon, C. A. Kothe, N. Bidgely-Shamlo, T. Mullen. Deep Neural Networks for Forecasting Single-Trial Event-Related Neural Activity. IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018, pp. 1070-1075. https://doi.org/10.1109/SMC.2018.00189
Bigdely-Shamlo, G. Ibagon, C. Kothe, T. Mullen. Finding the Optimal Cross-Subject EEG Data Alignment Method for Analysis and BCI. IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018, pp. 1110-1115. https://doi.org/10.1109/SMC.2018.00196
Kothe, T. Mullen, S. Makeig. STRUM: A New Dataset for Neuroergonomics Research. IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018, pp. 77-82, doi: https://doi.org/10.1109/SMC.2018.00023.
Javier O. Garcia, Justin Brooks, Scott Kerick, Tony Johnson, Tim R. Mullen, Jean M. Vettel. Estimating direction in brain-behavior interactions: Proactive and reactive brain states in driving. NeuroImage, 2017. https://doi.org/10.1016/j.neuroimage.2017.02.057
Courellis Hristos, Mullen Tim, Poizner Howard, Cauwenberghs Gert, Iversen John R. EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks. Frontiers in Neuroscience, 2017. https://www.frontiersin.org/article/10.3389/fnins.2017.00180
-H. Hsu, T. R. Mullen, T. -P. Jung, G. Cauwenberghs. Real-Time Adaptive EEG Source Separation Using Online Recursive Independent Component Analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 3, pp. 309-319, March 2016. https://doi.org/10.1109/TNSRE.2015.2508759
Lin, CT., Chuang, CH., Kerick, S. et al. Mind-Wandering Tends to Occur under Low Perceptual Demands during Driving. Sci Rep 6, 21353 (2016). https://doi.org/10.1038/srep21353
Kenneth Ball, Nima Bigdely-Shamlo, Tim Mullen, Kay Robbins. PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG. Computational Intelligence and Neuroscience, vol. 2016, Article ID 9754813, 20 pages, 2016. https://doi.org/10.1155/2016/9754813
V Shih, L Zhang, C Kothe, S Makeig, P Sajda. Predicting Decision Accuracy and Certainty in Complex Brain-Machine Interactions. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 004076-004081. 10.1109/SMC.2016.7844870, 2016. https://doi.org/10.1109/SMC.2016.7844870
Bigdely-Shamlo Nima, Mullen Tim, Kothe Christian, Su Kyung-Min, Robbins Kay A. The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Frontiers in Neuroinformatics, 2015. https://doi.org/10.3389/fninf.2015.00016
TR Mullen, CA Kothe, YM Chi, A Ojeda, T Kerth, S Makeig, TP Jung, G Cauwenberghs. Real-Time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG. IEEE Trans Biomed Eng. 62(11): 2553-67, 2015 https://doi.org/10.1109/TBME.2015.2481482
Hsu, T. Mullen, T. Jung, G. Cauwenberghs. Validating online recursive independent component analysis on EEG data. 7th International IEEE/EMBS Conference on Neural Engineering (NER), 2015, pp. 918-921. https://doi.org/10.1109/NER.2015.7146775
Zao John K., Gan Tchin-Tze, You Chun-Kai, Chung Cheng-En, Wang Yu-Te, Rodríguez Méndez Sergio José, Mullen Tim, Yu Chieh, Kothe Christian, Hsiao Ching-Teng, Chu San-Liang, Shieh Ce-Kuen, Jung Tzyy-Ping. Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology. Frontiers in Human Neuroscience, 2014. https://www.frontiersin.org/article/10.3389/fnhum.2014.00370.
K. Zao et al.. Augmented Brain Computer Interaction Based on Fog Computing and Linked Data. International Conference on Intelligent Environments, 2014, pp. 374-377, doi: https://doi.org/10.1109/IE.2014.54
Hsu, T. Mullen, T. Jung, G. Cauwenberghs. Online recursive independent component analysis for real-time source separation of high-density EEG. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014, pp. 3845-3848, doi: https://doi.org/10.1109/EMBC.2014.6944462
R. Iversen, A. Ojeda, T. Mullen, M. Plank, J. Snider, G. Gauwengberghs, H. Poizner. Causal analysis of cortical networks involved in reaching to spatial targets. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014, pp. 4399-4402, doi: https://doi.org/10.1109/EMBC.2014.6944599
Broccard, F.D., Mullen, T., Chi, Y.M. et al. Closed-Loop Brain–Machine–Body Interfaces for Noninvasive Rehabilitation of Movement Disorders. Ann Biomed Eng 42, 1573–1593 (2014). https://doi.org/10.1007/s10439-014-1032-6
T Mullen, C Kothe, YM Chi, A Ojeda, T Kerth, S Makeig, G Cauwenberghs, TP Jung. Real-Time Modeling and 3D Visualization of Source Dynamics and Connectivity using Wearable EEG. Prof. IEEE EMBS 2013, 2084-2187, 2013. doi:https://doi.org/10.1109/embc.2013.6609968
Nima Bigdely-Shamlo, Tim Mullen, Kenneth Kreutz-Delgado, Scott Makeig. Measure projection analysis: A probabilistic approach to EEG source comparison and multi-subject inference. NeuroImage, 2013, Volume 72. https://doi.org/10.1016/j.neuroimage.2013.01.040
C Kothe, S Makeig. BCILAB: A Platform for Brain-Computer Interface Development. Journal of Neural Engineering 10 (5), 056014, 2013. https://iopscience.iop.org/article/10.1088/1741-2560/10/5/056014/meta
C Kothe, S Makeig. Emotion Recognition from EEG during Self-Paced Emotional Imagery, Proc. Conf. ACII 2013, 855-585, 2013. https://doi.org/10.1109/ACII.2013.160
N Bigdely-Shamlo, K Kreutz-Delgado, K Robbins, M Miyakoshi, M Westerfield, T Bel-Bahar, C Kothe, J Hsi, S Makeig, Hierarchical Event Descriptor (HED) Tags for Analysis of Event-Related EEG Studies, Proc. IEEE GlobalSIP 2013, 1-4, 2013. https://doi.org/10.1109/GlobalSIP.2013.6736796
N Bigdely-Shamlo, K Kreutz-Delgado, C Kothe, S Makeig, Towards an EEG Search Engine, Proc. IEEE GlobalSIP 2013, 25-28, 2013. https://doi.org/10.1109/GlobalSIP.2013.6736802
N Bigdely-Shamlo, K Kreutz-Delgado, C Kothe, S Makeig, EyeCatch: Data-Mining over Half a Million EEG Independent Components to Construct a Fully Automated Eye-Component Detector, Proc. IEEE EMBS 2013, 5845-5848, 2013. https://doi.org/10.1109/embc.2013.6610881
Miyakoshi, A. Delorme, T. Mullen, K. Kojima, S. Makeig, E. Asano. Automated detection of cross-frequency coupling in the electrocorticogram for clinical inspection. 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013, pp. 3282-3285, https://doi.org/10.1109/EMBC.2013.6610242
T Mullen et al. MindMusic: Playful and Social Installations at the Interface Between Music and the Brain. Gaming Media and Social Effects. 2015. https://link.springer.com/chapter/10.1007/978-981-287-546-4_9
Mullen, G. Worrell, S. Makeig. Multivariate principal oscillation pattern analysis of ICA sources during seizure. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, pp. 2921-2924, https://doi.org/10.1109/EMBC.2012.6346575.
S Makeig, C Kothe, T Mullen, N Bigdely-Shamlo, Z Zhang, K Kreutz-Delgado, Evolving Signal Processing for Brain-Computer Interfaces, Proceedings of the IEEE 100 (Special Centennial Issue), 1567-1584, 2012, doi: https://doi.org/10.1109/JPROC.2012.2185009
Arnaud Delorme, Tim Mullen, Christian Kothe, Zeynep Akalin Acar, Nima Bigdely-Shamlo, Andrey Vankov, and Scott Makeig. EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing. Intell. Neuroscience 2011, Article 10 (January 2011). DOI:https://doi.org/10.1155/2011/130714
Mullen, Z. A. Acar, G. Worrell, S. Makeig. Modeling cortical source dynamics and interactions during seizure. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011. https://doi.org/10.1109/IEMBS.2011.6090332.
TO Zander, C Kothe. Towards Passive Brain–Computer Interfaces: Applying Brain–Computer Interface Technology to Human–Machine Systems in General, Journal of Neural Engineering 8 (2), 025005, 2011. https://doi.org/10.1088/1741-2560/8/2/025005
S Makeig, G Leslie, T Mullen, D Sarma, N Bigdely-Shamlo, C Kothe. First Demonstration of a Musical Emotion BCI, Affective Computing and Intelligent Interaction, 487-496, 2011. http://dx.doi.org/10.1007/978-3-642-24571-8_61
TO Zander, M Lehne, K Ihme, S Jatzev, J Correia, C Kothe, B Picht, F Nijboer. A Dry EEG System for Scientific Research and Brain-Computer Interfaces, Frontiers in Neuroscience 5, 53, 2011. https://doi.org/10.3389/fnins.2011.00053
A Delorme, T Mullen, C Kothe, ZA Acar, N Bigdely-Shamlo, A Vankov, S Makeig. EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing, Computational Intelligence and Neuroscience, 2011. http://dx.doi.org/10.1155/2011/130714
C Kothe, S Makeig, Estimation of Task Workload from EEG Data: New and Current Tools and Perspectives, Proc. IEEE EMBS 2011, 6547-6551, 2011. https://doi.org/10.1109/iembs.2011.6091615
A Delorme, C Kothe, A Vankov, N Bigdely-Shamlo, R Oostenveld, TO Zander, S Makeig. MATLAB-Based Tools for BCI Research, Brain-Computer Interfaces, 241-259, 2010. http://dx.doi.org/10.1007/978-1-84996-272-8_14
TO Zander, M Gaertner, C Kothe, R Vilimek. Combining Eye Gaze Input with a Brain-Computer Interface for Touchless Human-Computer Interaction, Intl. J. Human-Computer Interaction 27(1), 38-51, 2010. http://dx.doi.org/10.1080/10447318.2011.535752
TO Zander, C Kothe, S Jatzev, M Gaertner, Enhancing Human-Computer Interaction with Input from Active and Passive Brain-Computer Interfaces, Brain-Computer Interfaces, 181-199, 2010. doi: 10.1007/978-1-84996-272-8_11.