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Publications

M Angjelichinoski, T Banerjee, J Choi, B Pesaran, V Tarokh, “Minimax-optimal decoding of movement goals from local field potentials using complex spectral features”, arXiv preprint arXiv:1901.10397, 2019 (link)

F Najafi, G F Elsayed, R Cao, E Pnevmatikakis, P E. Latham, J P Cunningham, A K Churchland, “Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning”, (link)

Bighamian R., Wong Y., Pesaran B., Shanechi M. M., “Sparse model-based estimation of functional dependence in high-dimensional field and spike multiscale networks”, Journal of Neural Engineering, May 2019 (link).

Abbaspourazad H., Hsieh H., Shanechi M. M., “A Multiscale Dynamical Modeling and Identification Framework for Spike-Field Activity”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Apr. 2019 (link)

Wang C., Shanechi M. M., “Estimating Multiscale Direct Causality Graphs in Neural Spike-Field Networks”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Mar. 2019 (link)

Song C., Hsieh H., Shanechi M. M., “Decoder for switching state-space models with spike-field observations”, International IEEE EMBS Conference On Neural Engineering (NER), 20–23 Mar. 2019, San Francisco, CA.

G Denevi, C Ciliberto, D Stamos, M Pontil, “Learning to Learn Around A Common Mean”, NIPS, 2018 (link).

Pesaran B, Vinck M, Einevoll GT, Sirota A, Fries P, Siegel M, Truccolo W, Schroeder CE, Srinivasan R., “Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation”, Nat Neurosci, 21(7): 903-919, Jul. 2018 (link).

Hsieh H., Shanechi M. M., “Optimizing the Learning Rate for Adaptive Estimation of Neural Encoding Models”, PLoS Computational Biology 14(5): e1006168, May 2018 (link)

Hsieh H, Wong Y, Pesaran B, Shanechi M.M., “Multiscale Modeling and Decoding Algorithms for Spike-Field Activity”, Journal of Neural Engineering, Oct. 2018 (link)

Rabinovich, M., Ramdas, A., Wainwright, M., & Jordan, M. I., “Optimal rates and tradeoffs in multiple testing”, In press: Statistica Sinica.

Elsayed G.F., Pnevmatikakis E., Cunningham J.P., Churchland A.K., “Inhibitory and excitatory populations in parietal cortex are equally selective for decision outcome in both novices and experts”, bioRxiv, 2018 (link).

Ramdas, A., Chen, J., Wainwright, M., & Jordan, M. I., “DAGGER: a sequential algorithm for FDR control on DAGs”, In press: Biometrika.

Ramdas, A., Zrnic, T., Wainwright, M., & Jordan, M. I. (2018), “SAFFRON: An adaptive algorithm for online control of the false discovery rate”, In J. Dy and A. Krause (Eds.), International Conference on Machine Learning (ICML), New York: ACM Press.

Ramdas, A., Yang, F., Wainwright, M., & Jordan, M. I.  (2018), “Online control of the false discovery rate with decaying memory”, In S. Bengio, R. Fergus, S. Vishwanathan & H. Wallach (Eds.), Advances in Neural Information Processing (NIPS) 30, Red Hook, NY: Curran Associates.

Kanna, S., von Rosenberg, W., Goverdovsky, V., Constantinides, A.G. and Mandic, D.P., “Bringing Wearable Sensors into the Classroom: A Participatory Approach”, [SP Education]. IEEE Signal Processing Magazine, 35(3), pp.110-130, 2018.

Nakamura, T., Goverdovsky, V. and Mandic, D.P., “In-ear EEG biometrics for feasible and readily collectable real-world person authentication”, IEEE Transactions on Information Forensics and Security, 13(3), pp.648-661, 2018.

Valeriani, & R. Poli (2018). Cyborg Groups Enhance Face Recognition in Crowded Environments, bioRxiv 2018  (link)

Abbaspourazad H., Wong Y., Pesaran B., Shanechi M. M., “Identifying Multiscale Hidden States to Decode Behavior”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Honolulu, HI, 2018.

Sadras, N., Shanechi M. M., “Decoding Spike Trains from Neurons with Spatio-Temporal Receptive Fields”, in Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC) Conference, Honolulu, HI, 2018.

Taposh Banerjee, John Choi, Bijan Pesaran, Demba Ba, and Vahid Tarokh, “Classification of Local Field Potentials using Gaussian Sequence Model”, 2018 IEEE Workshop on Statistical Signal Processing (SSP)

Taposh Banerjee, John Choi, Bijan Pesaran, Demba Ba, and Vahid Tarokh, “Wavelet Shrinkage and Thresholding Based Robust Classification for Brain-Computer Interface”, 2018 International Conference on Acoustics, Speech and Signal Processing.

Valeriani, S. Bhattacharyya, C. Cinel, L. Citi, & R. Poli (2018). Augmenting group decision making accuracy in a realistic environment using collaborative brain-computer interfaces based on error-related potentials. 7th International BCI Meeting 2018 (Asilomar, CA).

G Luise, A Rudi, M Pontil, C Ciliberto, “Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance”, arXiv preprint arXiv:1805.11897, 2018.

G Denevi, C Ciliberto, D Stamos, M Pontil, “Incremental Learning-to-Learn with Statistical Guarantees.”, Proc. of Uncertainty in Artificial Intelligence (UAI), 2018.

Ramdas, A., Fogel Barber, A., Wainwright, M, and Jordan, M. I.  A unified treatment of multiple testing with prior knowledge.  ArXiv preprint, arXiv:1703.06222, 2017.

Valeriani, C. Cinel, & R. Poli. Group Augmentation in Realistic Visual-Search Decisions via a Hybrid Brain-Computer Interface. Scientific Reports, 2017. (link)

Goverdovsky, V., von Rosenberg, W., Nakamura, T., Looney, D., Sharp, D.J., Papavassiliou, C., Morrell, M.J. and Mandic, D.P., “Hearables: Multimodal physiological in-ear sensing”, Nature Scientific Reports, 7(1), p.6948, 2017.

Normahani, P., Makwana, N., von Rosenberg, W., Syed, S., Mandic, D.P., Goverdovsky, V., Standfield, N.J. and Jaffer, U., “Self-assessment of surgical ward crisis management using video replay augmented with stress biofeedback,” Patient Safety in Surgery, 12(1), p.6, 2018

von Rosenberg, W., Chanwimalueang, T., Adjei, T., Jaffer, U., Goverdovsky, V. and Mandic, D.P., “Resolving ambiguities in the LF/HF ratio: LF-HF scatter plots for the categorization of mental and physical stress from HRV”, Frontiers in Physiology, 8, p.360, 2017.

von Rosenberg, W., Chanwimalueang, T., Goverdovsky, V., Peters, N.S., Papavassiliou, C. and Mandic, D.P., “Hearables: feasibility of recording cardiac rhythms from head and in-ear locations”, Royal Society open science, 4(11), p.171214, 2017.

Kappel, S.L., Looney, D., Mandic, D.P. and Kidmose, P., “Physiological artifacts in scalp EEG and ear-EEG”, Biomedical Engineering Online, 16(1), p.103, 2017.

Hsieh, H., Wong Y.T., Pesaran B., Shanechi M. M., “Multiscale decoding of spike-field activity to improve brain-machine interface robustness and longevity”, Annual Meeting, Society for Neuroscience (SFN), 11–15 Nov. 2017, Washington, DC.

Abbaspourazad H., Shanechi M. M., “Learning the dependencies between spikes and fields in multiscale modeling”, Annual Meeting, Society for Neuroscience (SFN), 11–15 Nov. 2017, Washington, DC.

Abbaspourazad H., Shanechi M. M., “Multiscale modeling of dependencies between spikes and fields”, Asilomar Conference on Signals, Systems, and Computers, 29–31 Oct. 2017, Pacific Grove, CA.

Shanechi M. M., “Brain–Machine Interface Control Algorithms,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 10, pp. 1725-1734, Oct. 2017.

Hsieh H., Wong Y., Pesaran B., Shanechi M. M., “Multiscale Decoding for Reliable Brain-Machine Interface Performance Over Time,” in Proc. IEEE Conference on Engineering in Medicine and Biology Society (EMBC), Jul 2017, Jeju Island, Korea

Abbaspourazad H., Shanechi M. M., “An Unsupervised Learning Algorithm for Multiscale Neural Activity” in Proc. IEEE Conference on Engineering in Medicine and Biology Society (EMBC), Jul 2017, Jeju Island, Korea

Ramdas, A., Chen, J., Wainwright, M., and Jordan, M. I. (2017). QuTE algorithms for decentralized decision making on networks with false discovery rate control. 56th IEEE Conference on Decision and Control, Melbourne, Australia.

Lei, L. and Jordan, M. I. (2017). Less than a single pass: Stochastically controlled stochastic gradient. In A. Singh and J. Zhu (Eds.), Proceedings of the Nineteenth Conference on Artificial Intelligence and Statistics (AISTATS), Ft. Lauderdale, FL.

D. Valeriani, C. Cinel, & R. Poli (2017a). A Collaborative BCI Trained to Aid Group Decisions in a Visual Search Task Works Well with Similar Tasks. 1st Biannual Neuroadaptive Technology Conference (NAT’17), Berlin 19-21 July 2017.

Yiannis Demiris, IEEE Intelligent Vehicles 2017 workshop on “Cognitively Inspired Intelligent Vehicles”, 11-14 June 2017, Redondo Beach, California, USA (Keynote presentation).

Danilo Mandic, International Workshop on Brain Inspired Computing, “Hearables: In-ear EEG and vital signs monitoring of the state of body of mind”, Cetraro, Italy, June 2017.

D. Valeriani, C. Cinel, & R. Poli (2017b). Augmenting Group Performance in Target-Face Recognition via Collaborative Brain-Computer Interfaces for Surveillance Applications. 8th International IEEE EMBS Conference on Neural Engineering (NER’17), Shanghai 25-28 May 2017

Farzaneh Najafi, Gamaleldin F. Elsayed, Eftychios A. Pnevmatikakis, John P. Cunningham, Anne K. Churchland “Single-trial decision can be predicted from population activity of excitatory and inhibitor neurons” COSYNE 2017

Georgiou and Y. Demiris, “Adaptive user modelling in car racing games using behavioural and physiological data”, User Modeling and User-Adapted Interaction, Volume 27, Issue 2, pp 267–311, June 2017.

Adjei, W. von Rosenberg, V. Goverdovsky, K. Powezka, U. Jaffer, and D. P. Mandic, “Pain prediction from ECG in vascular surgery”, IEEE Journal of Translational Engineering in Health and Medicine, 5: 2800310, Sep. 2017.

Yiannis Demiris, AAAI Fall Symposium on Shared Autonomy in Research and Practice, 17-19 November 2016, Arlington, VA, USA (Keynote presentation)

Hsieh H., Shanechi M. M., “Adaptive multiscale brain-machine interface decoders”, Annual Meeting, Society for Neuroscience (SFN), 12–16 Nov. 2016, San Diego, CA

Abbaspourazad H., Shanechi M. M., “A new modeling framework for multiscale neural activity underlying behavior”, Annual Meeting, Society for Neuroscience (SFN), 12–16 Nov. 2016, San Diego, CA

Hsieh H., Shanechi M. M., “Multiscale Brain Machine Interface Decoders,” in Proc. IEEE Conference on Engineering in Medicine and Biology Society (EMBC), Aug 2016, Orlando, FL

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