Publications

2020

H. Abbaspourazad, M. Choudhary, Y. T. Wong, B. Pesaran, and M. M. Shanechi, “Multiscale low-dimensional neural dynamics explain naturalistic 3D movements,” Annual Meeting, COSYNE, 2020.

H. Abbaspourazad, M. Choudhary, Y. T. Wong, B. Pesaran, and M. M. Shanechi, “Multiscale, low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior,” Nature Communications (to appear), 2020.

P. V. Amadori, T. Fischer, and Y. Demiris, “HammerDrive: A Task-Aware DrivingVisual Attention Model,” IEEE Transactions on Intelligent Transportation Systems (in review), 2020.

P. V. Amadori, T. Fischer, R. Wang, and Y. Demiris, “Decision Anticipation for Driving Assistance Systems,” Accepted for 2020 IEEE Intelligent Transportation Systems Conference (ITSC), 2020.

M. Angjelichinoski, J. Choi, T. Banerjee, B. Pesaran, and V. Tarokh, “Cross-subject decoding of eye movement goals from local field potentials,” Journal of Neural Engineering, vol. 17, no. 1, Art. no. 1, Feb. 2020, doi: 10.1088/1741-2552/ab6df3.

M. Angjelichinoski, M. Soltani, J. Choi, B. Pesaran, and V. Tarokh, “Deep James-Stein Neural Networks For Brain-Computer Interfaces,” ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2020, doi: 10.1109/icassp40776.2020.9053694.

A. Chua, M. I. Jordan, and R. Muller, “Unsupervised Online Learning for Long-Term High Sensitivity Seizure Detection,” 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Jul. 2020, doi: 10.1109/embc44109.2020.9176122.

C. Cinel, J. Fernandez-Vargas, L. Citi, and R. Poli, “Impact of Multisensory Cues in a Realistic Target Discrimination Task,” Accepted for 2020 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), 2020.

H. J. Davies, I. Williams, N. S. Peters, and D. P. Mandic, “In-Ear Measurement of Blood Oxygen Saturation: An Ambulatory Tool Needed To Detect The Delayed Life-Threatening Hypoxaemia in COVID-19,” arXiv preprint arXiv:2006.04231, 2020, [Online]. Available: https://arxiv.org/abs/2006.04231.

G. Denevi, M. Pontil, and C. Ciliberto, “The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning,” arXiv preprint arXiv:2008.10857, 2020, [Online]. Available: https://arxiv.org/abs/2008.10857.

G. Denevi, D. Stamos, and M. Pontil, “Online Parameter-Free Learning of Multiple Low Variance Tasks,” The Conference on Uncertainty in Artificial Intelligence (UAI), in The Conference on Uncertainty in Artificial Intelligence (UAI), 2020, vol. 124, [Online]. Available: http://auai.org/uai2020/proceedings/368_main_paper.pdf.

J. Fernandez-Vargas, C. Tremmel, L. Citi, and R. Poli, “Neural Correlates and Prediction of Decision Confidence,” Accepted for International BCI Meeting 2020, 2020.

J. Fernandez-Vargas et al., “Confidence Prediction from EEG Recordings in a Multisensory Environment,” Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology, Jun. 2020, doi: 10.1145/3397391.3397426.

L. Huang et al., “BRICseq Bridges Brain-wide Interregional Connectivity to Neural Activity and Gene Expression in Single Animals,” Cell, vol. 182, no. 1, Art. no. 1, Jul. 2020, doi: 10.1016/j.cell.2020.05.029.

F. Najafi et al., “Excitatory and Inhibitory Subnetworks Are Equally Selective during Decision-Making and Emerge Simultaneously during Learning,” Neuron, vol. 105, no. 1, Art. no. 1, Jan. 2020, doi: 10.1016/j.neuron.2019.09.045.

T. Nakamura, Y. D. Alqurashi, M. J. Morrell, and D. P. Mandic, “Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 1, Art. no. 1, Jan. 2020, doi: 10.1109/tbme.2019.2911423.

M. Rabinovich, M. I. Jordan, and M. J. Wainwright, “Lower bounds in multiple testing: A framework based on derandomized proxies,” arXiv preprint arXiv:2005.03725, 2020, [Online]. Available: https://arxiv.org/abs/2005.03725.

O. G. Sani, B. Pesaran, and M. M. Shanechi, “Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification (PSID),” Nature Neuroscience (to appear), Preprint: bioRxiv, Oct. 2020, doi: 10.1101/808154.

O. G. Sani, B. Pesaran, and M. M. Shanechi, “Modeling behaviorally relevant neural dynamics with a novel preferential subspace identification (PSID),” Annual Meeting, COSYNE, 2020.

J. A. Soloff, A. Guntuboyina, and M. I. Jordan, “Covariance estimation with nonnegative partial correlations,” arXiv preprint arXiv:2007.15252, 2020, [Online]. Available: https://arxiv.org/abs/2007.15252.

A. Song, B. Tolooshams, S. Temereanca, and D. Ba, “Convolutional dictionary learning of stimulus from spiking data,” Annual Meeting, COSYNE, Feb. 2020.

C. Y. Song, H.-L. Hsieh, and M. M. Shanechi, “Multiscale Modeling and Inference Methods for Switching Regime-Dependent Dynamical Systems,” IEEE Transactions on Neural Systems and Rehabilitation Engineering (in preparation), 2020.

B. Tolooshams, S. Dey, and D. Ba, “Deep Residual Autoencoders for Expectation Maximization-Inspired Dictionary Learning,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–15, 2020, doi: 10.1109/TNNLS.2020.3005348.

B. Tolooshams, A. H. Song, S. Temereanca, and D. Ba, “Convolutional dictionary learning based auto-encoders for natural exponential-family distributions,” In Proceedings of the 37th International Conference on Machine Learning (ICML),, 2020, [Online]. Available: https://proceedings.icml.cc/static/paper_files/icml/2020/5733-Paper.pdf.

R. Wang, C. Ciliberto, P. V. Amadori, and Y. Demiris, “Support-weighted Adversarial Imitation Learning,” arXiv preprint arXiv:2002.08803, 2020, [Online]. Available: https://arxiv.org/abs/2002.08803.

Y. Yang et al., “Modeling large-scale brain network dynamics in response to electrical stimulation,” Annual Meeting, COSYNE, 2020.

Y. Yang et al., “Model-based prediction of large-scale brain network dynamic response to direct electrical stimulation,” Nature Biomedical Engineering (to appear), 2020.

T. Zrnic, D. Jiang, A. Ramdas, and M. Jordan, “The Power of Batching in Multiple Hypothesis Testing,” 2020, vol. 108, pp. 3806–3815, [Online]. Available: http://proceedings.mlr.press/v108/zrnic20a.html.

T. Zrnic, A. Ramdas, and M. I. Jordan, “Asynchronous online testing of multiple hypotheses,” Journal of Machine Learning Research (submitted), preprint arXiv:1812.05068, 2020, [Online]. Available: https://arxiv.org/abs/1812.05068.

2019

H. Abbaspourazad, H.-L. Hsieh, and M. M. Shanechi, “A Multiscale Dynamical Modeling and Identification Framework for Spike-Field Activity,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 6, Art. no. 6, Jun. 2019, doi: 10.1109/TNSRE.2019.2913218.

H. Abbaspourazad, Y. Wong, B. Pesaran, and M. M. Shanechi, “Dynamical characteristics of simultaneously-recorded spike and LFP activities underlying 3D reach-to-grasp,” Annual Meeting, Society for Neuroscience (SFN), 2019.

P. Ahmadipour, Y. Yang, and M. M. Shanechi, “Adaptive modeling of neural network dynamics with optimized learning rate,” Annual Meeting, Society for Neuroscience (SFN), 2019.

P. Ahmadipour, Y. Yang, and M. M. Shanechi, “Investigating the effect of forgetting factor on tracking non-stationary neural dynamics,” 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), in 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), Mar. 2019, pp. 291–294, doi: 10.1109/NER.2019.8717119.

M. Angjelichinoski, T. Banerjee, J. Choi, B. Pesaran, and V. Tarokh, “Minimax-optimal decoding of movement goals from local field potentials using complex spectral features,” Journal of Neural Engineering, vol. 16, no. 4, Art. no. 4, May 2019, doi: 10.1088/1741-2552/ab1a1f.

T. Banerjee, S. Allsop, K. M. Tye, D. Ba, and V. Tarokh, “Sequential Detection of Regime Changes in Neural Data,” 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), in 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), Mar. 2019, pp. 139–142, doi: 10.1109/NER.2019.8716926.

S. Bhattacharyya, C. Cinel, L. Citi, D. Valeriani, and R. Poli, “Walking improves the Performance of a Braine Computer Interface for Group Decision Making,” 2nd Neuroadaptive Technology Conference, Liverpool, UK, 2019.

S. Bhattacharyya, D. Valeriani, C. Cinel, L. Citi, and R. Poli, “Collaborative Brain-Computer Interfaces to Enhance Group Decisions in an Outpost Surveillance Task,” 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul. 2019, doi: 10.1109/embc.2019.8856309.

S. Bhattacharyya, D. Valeriani, C. Cinel, L. Citi, and R. Poli, “Target Detection in Video Feeds with Selected Dyads and Groups Assisted by Collaborative Brain-Computer Interfaces,” 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), in 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), Mar. 2019, pp. 159–162, doi: 10.1109/NER.2019.8717146.

R. Bighamian, Y. T. Wong, B. Pesaran, and M. M. Shanechi, “Sparse model-based estimation of functional dependence in high-dimensional field and spike multiscale networks,” Journal of Neural Engineering, vol. 16, no. 5, Art. no. 5, Sep. 2019, doi: 10.1088/1741-2552/ab225b.

J. Choi, E. A. Voinas, A. Orsborn, B. Ferrentino, and B. Pesaran, “A Projector-Scope for Spatiotemporal Control of Macaque Cortex,” 2019 9th International IEEE EMBS Conference on Neural Engineering (NER), 2019.

L. Chua, M. I. Jordan, and R. Muller, “High sensitivity, low power, seizure detection classifier with unsupervised online learning,” IEEE Biomedical Circuits and Systems Conference (BioCAS) (submitted to), 2019.

C. Cinel, D. Valeriani, and R. Poli, “Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects,” Frontiers in Human Neuroscience, vol. 13, Jan. 2019, doi: 10.3389/fnhum.2019.00013.

G. Denevi, C. Ciliberto, R. Grazzi, and M. Pontil, “Learning-to-Learn Stochastic Gradient Descent with Biased Regularization,” arXiv preprint arXiv:1903.10399, 2019, [Online]. Available: https://arxiv.org/abs/1903.10399.

A. Dubey, D. A. Markowitz, and B. Pesaran, “Beta activity (15-30 HZ) modulates the choice probability in a visual selection task,” Annual Meeting, Society for Neuroscience (SFN), 2019.

H.-L. Hsieh, B. Pesaran, and M. M. Shanechi, “The topology and geometry of motor cortical dynamics underlying 3D movements,” Annual Meeting, Society for Neuroscience (SFN), 2019.

G. Luise, D. Stamos, M. Pontil, and C. Ciliberto, “Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction,” arXiv preprint arXiv:1903.00667, 2019, [Online]. Available: https://arxiv.org/abs/1903.00667.

S. Musall, M. T. Kaufman, A. L. Juavinett, S. Gluf, and A. K. Churchland, “Single-trial neural dynamics are dominated by richly varied movements,” Nature Neuroscience, vol. 22, no. 10, Art. no. 10, Sep. 2019, doi: 10.1038/s41593-019-0502-4.

S. Musall, A. E. Urai, D. Sussillo, and A. K. Churchland, “Harnessing behavioral diversity to understand neural computations for cognition,” Current Opinion in Neurobiology, vol. 58, pp. 229–238, Oct. 2019, doi: 10.1016/j.conb.2019.09.011.

R. M. Nair et al., “Decoding human confidence from neural signals,” Annual Meeting, Society for Neuroscience (SFN), 2019.

V. Oliveira et al., “Early Postnatal Heart Rate Variability in Healthy Newborn Infants,” Frontiers in Physiology, vol. 10, Aug. 2019, doi: 10.3389/fphys.2019.00922.

S. Pisupati, L. Chartarifsky-Lynn, A. Khanal, and A. K. Churchland, “Lapses in perceptual decisions reflect exploration,” bioRxiv, Apr. 2019, doi: 10.1101/613828.

A. Ramdas, J. Chen, M. J. Wainwright, and M. I. Jordan, “A sequential algorithm for false discovery rate control on directed acyclic graphs,” Biometrika, vol. 106, no. 1, Art. no. 1, Jan. 2019, doi: 10.1093/biomet/asy066.

A. K. Ramdas, R. F. Barber, M. J. Wainwright, M. I. Jordan, and others, “A unified treatment of multiple testing with prior knowledge using the p-filter,” The Annals of Statistics, vol. 47, no. 5, Art. no. 5, Oct. 2019, doi: 10.1214/18-AOS1765.

W. von Rosenberg, M.-O. Hoting, and D. P. Mandic, “A physiology based model of heart rate variability,” Biomedical Engineering Letters, vol. 9, no. 4, Art. no. 4, Aug. 2019, doi: 10.1007/s13534-019-00124-w.

S. Sabharwal-Siddiqi et al., “Virtual reality system for the immersive display of visual and audiovisual objects,” Annual Meeting, Society for Neuroscience (SFN), 2019.

N. Sadras, B. Pesaran, and M. M. Shanechi, “A point-process matched filter for event detection and decoding from population spike trains,” Journal of Neural Engineering, vol. 16, no. 6, Art. no. 6, Oct. 2019, doi: 10.1088/1741-2552/ab3dbc.

N. Sadras, B. Pesaran, and M. M. Shanechi, “Estimating event times from spike trains with a point process matched filter,” Annual Meeting, Society for Neuroscience (SFN), 2019.

O. G. Sani, B. Pesaran, and M. M. Shanechi, “A new preferential subspace identification (PSID) algorithm for learning dynamic neural encoding models with behavior-related latent states,” Annual Meeting, Society for Neuroscience (SFN), 2019.

M. M. Shanechi, “Brain–machine interfaces from motor to mood,” Nature Neuroscience, vol. 22, no. 10, Art. no. 10, Sep. 2019, doi: 10.1038/s41593-019-0488-y.

R. A. Shewcraft, H. L. Dean, M. A. Hagan, M. M. Fabiszak, Y. T. Wong, and B. Pesaran, “Excitatory-inhibitory windows shape coherent neuronal dynamics driven by optogenetic stimulation in the primate brain,” bioRxiv, p. 437970, Oct. 2019, doi: 10.1101/437970.

C. Y. Song, H.-L. Hsieh, and M. M. Shanechi, “Decoder for switching state space models with spike-field observations,” Annual Meeting, Society for Neuroscience (SFN), 2019.

C. Y. Song and M. M. Shanechi, “Decoder for Switching State-Space Models with Spike-Field Observations,” 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), in 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), Mar. 2019, pp. 199–202, doi: 10.1109/NER.2019.8716970.

B. Tolooshams, A. H. Song, S. Temereanca, and D. Ba, “Deep Exponential-Family Auto-Encoders,” Advances in Neural Information Processing Systems (Submitted), 2019.

D. Valeriani and R. Poli, “Cyborg groups enhance face recognition in crowded environments,” PLOS ONE, vol. 14, no. 3, Art. no. 3, Mar. 2019, doi: 10.1371/journal.pone.0212935.

C. Wang, B. Pesaran, and M. M. Shanechi, “Multiscale spike-field network causality identification during a motor task,” Annual Meeting, Society for Neuroscience (SFN), 2019.

C. Wang and M. M. Shanechi, “Estimating Multiscale Direct Causality Graphs in Neural Spike-Field Networks,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 5, Art. no. 5, May 2019, doi: 10.1109/TNSRE.2019.2908156.

R. Wang, C. Ciliberto, P. V. Amadori, and Y. Demiris, “Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation,” arXiv preprint arXiv:1905.06750, 2019, [Online]. Available: https://arxiv.org/abs/1905.06750.

Y. Yang, S. Qiao, B. Pesaran, and M. M. Shanechi, “Accurate prediction of large-scale LFP network dynamics in response to electrical stimulation,” Annual Meeting, Society for Neuroscience (SFN), 2019.

J. Zazo, B. Tolooshams, D. Ba, and H. J. A. Paulson, “Convolutional Dictionary Learning in Hierarchical Networks,” 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Dec. 2019, doi: 10.1109/CAMSAP45676.2019.9022440.

2018

H. Abbaspourazad, Y. Wong, B. Pesaran, and M. M. Shanechi, “Identifying multiscale hidden states to decode behavior,” 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul. 2018, pp. 3778–3781, doi: 10.1109/EMBC.2018.8513242.

H. Abbaspourazad, Y. T. Wong, B. Pesaran, and M. M. Shanechi, “Identifying multiscale hidden neural dynamics to decode movement,” Annual Meeting, Society for Neuroscience (SFN), 2018.

T. Adjei, J. Xue, and D. P. Mandic, “The Female Heart: Sex Differences in the Dynamics of ECG in Response to Stress,” Frontiers in Physiology, vol. 9, Nov. 2018, doi: 10.3389/fphys.2018.01616.

D. Ba, “Deeply-Sparse Signal rePresentations (DS^2P),” arXiv preprint arXiv:1807.01958, 2018, [Online]. Available: https://arxiv.org/abs/1807.01958.

T. Banerjee, J. Choi, B. Pesaran, D. Ba, and V. Tarokh, “Classification of Local Field Potentials using Gaussian Sequence Model,” 2018 IEEE Statistical Signal Processing Workshop (SSP), in 2018 IEEE Statistical Signal Processing Workshop (SSP), Jun. 2018, pp. 683–687, doi: 10.1109/SSP.2018.8450778.

T. Banerjee, J. Choi, B. Pesaran, D. Ba, and V. Tarokh, “Wavelet Shrinkage and Thresholding Based Robust Classification for Brain-Computer Interface,” 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr. 2018, pp. 836–840, doi: 10.1109/ICASSP.2018.8462321.

R. Bighamian and M. M. Shanechi, “Estimation of Functional Dependence in High-Dimensional Spike-Field Activity,” 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul. 2018, pp. 2635–2638, doi: 10.1109/EMBC.2018.8512831.

R. Bighamian and M. M. Shanechi, “Modeling functional dependencies in high-dimensional spike-field activity,” Annual Meeting, Society for Neuroscience (SFN), 2018.

J. Choi, V. Goncharov, J. Kleinbart, A. Orsborn, and B. Pesaran, “Monkey-MIMMS: Towards Automated Cellular Resolution Large- Scale Two-Photon Microscopy In The Awake Macaque Monkey,” 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul. 2018, pp. 3013–3016, doi: 10.1109/EMBC.2018.8512994.

G. Denevi, C. Ciliberto, D. Stamos, and M. Pontil, “Incremental learning-to-learn with statistical guarantees,” arXiv preprint arXiv:1803.08089, 2018, [Online]. Available: https://arxiv.org/abs/1803.08089.

G. Denevi, C. Ciliberto, D. Stamos, and M. Pontil, “Learning To Learn Around A Common Mean,” Advances in Neural Information Processing Systems 31, in Advances in Neural Information Processing Systems 31, 2018, pp. 10169–10179, [Online]. Available: http://papers.nips.cc/paper/8220-learning-to-learn-around-a-common-mean.pdf.

D. J. Hawellek, K. A. Brown, and B. Pesaran, “Deliberation and enaction during adaptive economic choice,” bioRxiv, p. 445346, Oct. 2018, doi: 10.1101/445346.

A. Hemakom, V. Goverdovsky, and D. P. Mandic, “EAR-EEG for Detecting Inter-Brain Synchronisation in Continuous Cooperative Multi-Person Scenarios,” 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr. 2018, pp. 911–915, doi: 10.1109/ICASSP.2018.8462347.

H.-L. Hsieh and M. M. Shanechi, “Optimizing the learning rate for adaptive estimation of neural encoding models,” PLOS Computational Biology, vol. 14, no. 5, Art. no. 5, May 2018, doi: 10.1371/journal.pcbi.1006168.

H.-L. Hsieh, Y. T. Wong, B. Pesaran, and M. M. Shanechi, “Multiscale modeling and decoding algorithms for spike-field activity,” Journal of Neural Engineering, vol. 16, no. 1, Art. no. 1, Dec. 2018, doi: 10.1088/1741-2552/aaeb1a.

H.-L. Hsieh, Y. T. Wong, B. Pesaran, and M. M. Shanechi, “Multiscale modeling and decoding of spike-field activity,” Computational and Systems Neuroscience (Cosyne), 2018.

S. Kanna, W. von Rosenberg, V. Goverdovsky, A. G. Constantinides, and D. P. Mandic, “Bringing Wearable Sensors into the Classroom: A Participatory Approach [SP Education],” IEEE Signal Processing Magazine, vol. 35, no. 3, Art. no. 3, May 2018, doi: 10.1109/MSP.2018.2806418.

G. Luise, A. Rudi, M. Pontil, and C. Ciliberto, “Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance,” Advances in Neural Information Processing Systems 31, in Advances in Neural Information Processing Systems 31, 2018, pp. 5859–5870, [Online]. Available: http://papers.nips.cc/paper/7827-differential-properties-of-sinkhorn-approximation-for-learning-with-wasserstein-distance.pdf.

N. Malem-Shinitski et al., “A separable two-dimensional random field model of binary response data from multi-day behavioral experiments,” Journal of Neuroscience Methods, vol. 307, pp. 175–187, Sep. 2018, doi: 10.1016/j.jneumeth.2018.04.006.

F. Najafi and A. K. Churchland, “Perceptual Decision-Making: A Field in the Midst of a Transformation,” Neuron, vol. 100, no. 2, Art. no. 2, Oct. 2018, doi: 10.1016/j.neuron.2018.10.017.

T. Nakamura, Y. D. Alqurashi, M. J. Morrell, and D. P. Mandic, “Automatic detection of drowsiness using in-ear EEG,” 2018 International Joint Conference on Neural Networks (IJCNN), in 2018 International Joint Conference on Neural Networks (IJCNN), Jul. 2018, pp. 1–6, doi: 10.1109/IJCNN.2018.8489723.

T. Nakamura, V. Goverdovsky, and D. P. Mandic, “In-Ear EEG Biometrics for Feasible and Readily Collectable Real-World Person Authentication,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 3, Art. no. 3, Mar. 2018, doi: 10.1109/TIFS.2017.2763124.

P. Normahani et al., “Self-assessment of surgical ward crisis management using video replay augmented with stress biofeedback,” Patient Safety in Surgery, vol. 12, no. 1, Art. no. 1, Apr. 2018, doi: 10.1186/s13037-018-0153-5.

B. Pesaran et al., “Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation,” Nature Neuroscience, vol. 21, no. 7, Art. no. 7, Jun. 2018, doi: 10.1038/s41593-018-0171-8.

A. Ramdas, T. Zrnic, M. Wainwright, and M. Jordan, “SAFFRON: an adaptive algorithm for online control of the false discovery rate,” arXiv preprint arXiv:1802.09098, 2018, [Online]. Available: https://arxiv.org/abs/1802.09098.

N. Sadras and M. M. Shanechi, “Decoding Spike Trains from Neurons with Spatio-Temporal Receptive Fields,” 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul. 2018, pp. 2012–2015, doi: 10.1109/EMBC.2018.8512598.

O. G. Sani and M. M. Shanechi, “Learning dynamic neural encoding models with behaviorally-relevant latent states,” Annual Meeting, Society for Neuroscience (SFN), 2018.

B. Tolooshams, S. Dey, and D. Ba, “Scalable convolutional dictionary learning with constrained recurrent sparse auto-encoders,” 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), in 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), Sep. 2018, pp. 1–6, doi: 10.1109/MLSP.2018.8516996.

D. Valeriani, S. Bhattacharyya, C. Cinel, L. Citi, and R. Poli, “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), 2018.

C. Wang and M. M. Shanechi, “An Information-Theoretic Measure of Multiscale Causality for Spike-Field Activity,” 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul. 2018, pp. 2631–2634, doi: 10.1109/EMBC.2018.8512823.

C. Wang and M. M. Shanechi, “Learning causal graphs in spike-field multiscale network encoding models,” Annual Meeting, Society for Neuroscience (SFN), 2018.

Y. Zhang, N. Malem-Shinitski, S. A. Allsop, K. M. Tye, and D. Ba, “Estimating a Separably Markov Random Field from Binary Observations,” Neural Computation, vol. 30, no. 4, Art. no. 4, Apr. 2018, doi: 10.1162/neco_a_01059.

 

2017

H. Abbaspourazad, H.-L. Hsieh, and M. M. Shanechi, “Multiscale modeling of dependencies between spikes and fields,” 2017 51st Asilomar Conference on Signals, Systems, and Computers, in 2017 51st Asilomar Conference on Signals, Systems, and Computers, Oct. 2017, pp. 719–723, doi: 10.1109/acssc.2017.8335438.

H. Abbaspourazad, H.-L. Hsieh, and M. M. Shanechi, “Multiscale Modeling of High-dimensional Neural Activity,” Asilomar conference on signals, systems and computers, 2017.

H. Abbaspourazad and M. M. Shanechi, “An unsupervised learning algorithm for multiscale neural activity,” 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul. 2017, pp. 201–204, doi: 10.1109/EMBC.2017.8036797.

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

T. Adjei, W. V. 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, vol. 5, pp. 1–10, 2017, doi: 10.1109/JTEHM.2017.2734647.

C. Ciliberto, A. Rudi, L. Rosasco, and M. Pontil, “Consistent multitask learning with nonlinear output relations,” Advances in Neural Information Processing Systems, in Advances in Neural Information Processing Systems, 2017, pp. 1986–1996, [Online]. Available: http://papers.nips.cc/paper/6794-consistent-multitask-learning-with-nonlinear-output-relations.pdf.

C. Ciliberto, D. Stamos, and M. Pontil, “Reexamining low rank matrix factorization for trace norm regularization,” arXiv preprint arXiv:1706.08934, 2017, [Online]. Available: http://arxiv.org/abs/1706.08934.

T. Georgiou and Y. Demiris, “Adaptive user modelling in car racing games using behavioural and physiological data,” User Modeling and User-Adapted Interaction, vol. 27, no. 2, Art. no. 2, May 2017, doi: 10.1007/s11257-017-9192-3.

V. Goverdovsky et al., “Hearables: Multimodal physiological in-ear sensing,” Scientific Reports, vol. 7, no. 1, Art. no. 1, Jul. 2017, doi: 10.1038/s41598-017-06925-2.

A. Hemakom, K. Powezka, V. Goverdovsky, U. Jaffer, and D. P. Mandic, “Data from: Quantifying team cooperation through intrinsic multi-scale measures: respiratory and cardiac synchronisation in choir singers and surgical teams,” Royal Society open science, vol. 4, no. 12, Art. no. 12, 2017, doi: 10.5061/dryad.80cv0.

H.-L. Hsieh, Y. T. Wong, B. Pesaran, and M. M. Shanechi, “Multiscale decoding for reliable brain-machine interface performance over time,” 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul. 2017, pp. 197–200, doi: 10.1109/EMBC.2017.8036796.

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

S. L. Kappel, D. Looney, D. P. Mandic, and P. Kidmose, “Physiological artifacts in scalp EEG and ear-EEG,” BioMedical Engineering OnLine, vol. 16, no. 1, Art. no. 1, Aug. 2017, doi: 10.1186/s12938-017-0391-2.

L. Lei and M. Jordan, “Less than a Single Pass: Stochastically Controlled Stochastic Gradient,” 2017, vol. 54, pp. 148–156, [Online]. Available: http://proceedings.mlr.press/v54/lei17a.html.

F. Najafi, G. F. Elsayed, E. A. Pnevmatikakis, J. P. Cunningham, and A. K. Churchland, “Single-trial decision can be predicted from population activity of excitatory and inhibitor neurons,” Annual Meeting, COSYNE, 2017.

M. Rabinovich, A. Ramdas, M. I. Jordan, and M. J. Wainwright, “Optimal rates and tradeoffs in multiple testing,” arXiv preprint arXiv:1705.05391, 2017, [Online]. Available: https://arxiv.org/abs/1705.05391.

A. Ramdas, J. Chen, M. J. Wainwright, and M. I. Jordan, “QuTE: Decentralized multiple testing on sensor networks with false discovery rate control,” 2017 IEEE 56th Annual Conference on Decision and Control (CDC), in 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017, pp. 6415–6421, doi: 10.1109/CDC.2017.8264627.

A. Ramdas, F. Yang, M. J. Wainwright, and M. I. Jordan, “Online control of the false discovery rate with decaying memory,” Advances in Neural Information Processing Systems 30, in Advances in Neural Information Processing Systems 30, 2017, pp. 5650–5659, [Online]. Available: http://papers.nips.cc/paper/7148-online-control-of-the-false-discovery-rate-with-decaying-memory.pdf.

W. von Rosenberg, T. Chanwimalueang, T. Adjei, U. Jaffer, V. Goverdovsky, and D. P. Mandic, “Resolving ambiguities in the LF/HF ratio: LF-HF scatter plots for the categorization of mental and physical stress from HRV,” Frontiers in physiology, vol. 8, p. 360, Jun. 2017, doi: 10.3389/fphys.2017.00360.

W. von Rosenberg, T. Chanwimalueang, V. Goverdovsky, N. S. Peters, C. Papavassiliou, and D. P. Mandic, “Hearables: Feasibility of recording cardiac rhythms from head and in-ear locations,” Royal Society Open Science, vol. 4, no. 11, Art. no. 11, Nov. 2017, doi: 10.1098/rsos.171214.

M. M. Shanechi, “Brain–Machine Interface Control Algorithms,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 10, Art. no. 10, Oct. 2017, doi: 10.1109/TNSRE.2016.2639501.

Y. Tonoyan, T. Chanwimalueang, D. P. Mandic, and M. M. Van Hulle, “Discrimination of emotional states from scalp- and intracranial EEG using multiscale Rényi entropy,” PloS one, vol. 12, no. 11, Art. no. 11, Nov. 2017, doi: 10.1371/journal.pone.0186916.

D. Valeriani, C. Cinel, and R. Poli, “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), in 1st Biannual Neuroadaptive Technology Conference (NAT’17), 2017, pp. 77–78.

D. Valeriani, C. Cinel, and R. Poli, “Augmenting group performance in target-face recognition via collaborative brain-computer interfaces for surveillance applications,” 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), in 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), May 2017, pp. 415–418, doi: 10.1109/NER.2017.8008378.

D. Valeriani, C. Cinel, and R. Poli, “Group Augmentation in Realistic Visual-Search Decisions via a Hybrid Brain-Computer Interface,” Scientific Reports, vol. 7, no. 1, Art. no. 1, Aug. 2017, doi: 10.1038/s41598-017-08265-7.

2016

H. Abbaspourazad and M. M. Shanechi, “A new modeling framework for multiscale neural activity underlying behavior,” Annual Meeting, Society for Neuroscience (SFN), 2016.

H.-L. Hsieh and M. M. Shanechi, “Adaptive multiscale brain-machine interface decoders,” Annual Meeting, Society for Neuroscience (SFN), 2016.

H.-L. Hsieh and M. M. Shanechi, “Multiscale brain-machine interface decoders,” 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Aug. 2016, pp. 6361–6364, doi: 10.1109/EMBC.2016.7592183.