Timezone: »
The synchronous brain activity measured via MEG (or EEG) can be interpreted as arising from a collection (possibly large) of current dipoles or sources located throughout the cortex. Estimating the number, location, and orientation of these sources remains a challenging task, one that is significantly compounded by the effects of source correlations and the presence of interference from spontaneous brain activity, sensor noise, and other artifacts. This paper derives an empirical Bayesian method for addressing each of these issues in a principled fashion. The resulting algorithm guarantees descent of a cost function uniquely designed to handle unknown orientations and arbitrary correlations. Robust interference suppression is also easily incorporated. In a restricted setting, the proposed method is shown to have theoretically zero bias estimating both the location and orientation of multi-component dipoles even in the presence of correlations, unlike a variety of existing Bayesian localization methods or common signal processing techniques such as beamforming and sLORETA. Empirical results on both simulated and real data sets verify the efficacy of this approach.
Author Information
David P Wipf (AWS)
Julia Owen (UCSF)
Hagai Attias (Microsoft Research)
Kensuke Sekihara
Sri Nagarajan (UCSF)
Related Events (a corresponding poster, oral, or spotlight)
-
2008 Poster: Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG »
Thu. Dec 11th through Wed the 10th Room
More from the Same Authors
-
2021 : A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs »
Mucong Ding · Kezhi Kong · Jiuhai Chen · John Kirchenbauer · Micah Goldblum · David P Wipf · Furong Huang · Tom Goldstein -
2022 Poster: Learning Enhanced Representation for Tabular Data via Neighborhood Propagation »
Kounianhua Du · Weinan Zhang · Ruiwen Zhou · Yangkun Wang · Xilong Zhao · Jiarui Jin · Quan Gan · Zheng Zhang · David P Wipf -
2022 Spotlight: Lightning Talks 5B-3 »
Yanze Wu · Jie Xiao · Nianzu Yang · Jieyi Bi · Jian Yao · Yiting Chen · Qizhou Wang · Yangru Huang · Yongqiang Chen · Peixi Peng · Yuxin Hong · Xintao Wang · Feng Liu · Yining Ma · Qibing Ren · Xueyang Fu · Yonggang Zhang · Kaipeng Zeng · Jiahai Wang · GEN LI · Yonggang Zhang · Qitian Wu · Yifan Zhao · Chiyu Wang · Junchi Yan · Feng Wu · Yatao Bian · Xiaosong Jia · Ying Shan · Zhiguang Cao · Zheng-Jun Zha · Guangyao Chen · Tianjun Xiao · Han Yang · Jing Zhang · Jinbiao Chen · MA Kaili · Yonghong Tian · Junchi Yan · Chen Gong · Tong He · Binghui Xie · Yuan Sun · Francesco Locatello · Tongliang Liu · Yeow Meng Chee · David P Wipf · Tongliang Liu · Bo Han · Bo Han · Yanwei Fu · James Cheng · Zheng Zhang -
2022 Spotlight: Self-supervised Amodal Video Object Segmentation »
Jian Yao · Yuxin Hong · Chiyu Wang · Tianjun Xiao · Tong He · Francesco Locatello · David P Wipf · Yanwei Fu · Zheng Zhang -
2022 Spotlight: NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification »
Qitian Wu · Wentao Zhao · Zenan Li · David P Wipf · Junchi Yan -
2022 Spotlight: Lightning Talks 1B-1 »
Qitian Wu · Runlin Lei · Rongqin Chen · Luca Pinchetti · Yangze Zhou · Abhinav Kumar · Hans Hao-Hsun Hsu · Wentao Zhao · Chenhao Tan · Zhen Wang · Shenghui Zhang · Yuesong Shen · Tommaso Salvatori · Gitta Kutyniok · Zenan Li · Amit Sharma · Leong Hou U · Yordan Yordanov · Christian Tomani · Bruno Ribeiro · Yaliang Li · David P Wipf · Daniel Cremers · Bolin Ding · Beren Millidge · Ye Li · Yuhang Song · Junchi Yan · Zhewei Wei · Thomas Lukasiewicz -
2022 Poster: NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification »
Qitian Wu · Wentao Zhao · Zenan Li · David P Wipf · Junchi Yan -
2022 Poster: Transformers from an Optimization Perspective »
Yongyi Yang · zengfeng Huang · David P Wipf -
2022 Poster: Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks »
Hongjoon Ahn · Yongyi Yang · Quan Gan · Taesup Moon · David P Wipf -
2022 Poster: Self-supervised Amodal Video Object Segmentation »
Jian Yao · Yuxin Hong · Chiyu Wang · Tianjun Xiao · Tong He · Francesco Locatello · David P Wipf · Yanwei Fu · Zheng Zhang -
2022 Poster: Learning Manifold Dimensions with Conditional Variational Autoencoders »
Yijia Zheng · Tong He · Yixuan Qiu · David P Wipf -
2021 : A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs »
Mucong Ding · Kezhi Kong · Jiuhai Chen · John Kirchenbauer · Micah Goldblum · David P Wipf · Furong Huang · Tom Goldstein -
2020 Poster: Further Analysis of Outlier Detection with Deep Generative Models »
Ziyu Wang · Bin Dai · David P Wipf · Jun Zhu -
2012 Poster: Dual-Space Analysis of the Sparse Linear Model »
David P Wipf -
2011 Poster: Sparse Estimation with Structured Dictionaries »
David P Wipf -
2011 Spotlight: Sparse Estimation with Structured Dictionaries »
David P Wipf -
2009 Poster: Sparse Estimation Using General Likelihoods and Non-Factorial Priors »
David P Wipf · Sri Nagarajan -
2007 Poster: A New View of Automatic Relevance Determination »
David P Wipf · Srikantan Nagarajan -
2006 Poster: Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization »
David P Wipf · Rey R Ramirez · Jason A Palmer · Scott Makeig · Bhaskar Rao -
2006 Spotlight: Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization »
David P Wipf · Rey R Ramirez · Jason A Palmer · Scott Makeig · Bhaskar Rao -
2006 Poster: A Probabilistic Algorithm Integrating Source Localization and Noise Suppression for MEG and EEG data »
Johanna M Zumer · Hagai Attias · Kensuke Sekihara · Srikantan Nagarajan