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Sparse Representation Learning Approach Resolves Deep Sources Underlying MEG and EEG Data.

, , , , , , and . AAAI Spring Symposia, AAAI Press, (2017)

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Sparse spectral estimation from point process observations., , , and . ICASSP, page 841-845. IEEE, (2017)A fast iterative greedy algorithm for MEG source localization., , , , and . EMBC, page 6748-6751. IEEE, (2012)Real-Time Tracking of Magnetoencephalographic Neuromarkers during a Dynamic Attention-Switching Task., , , and . EMBC, page 4148-4151. IEEE, (2019)Fast and Stable Signal Deconvolution via Compressible State-Space Models., , , , , , and . IEEE Trans. Biomed. Eng., 65 (1): 74-86 (2018)Dynamic Analysis of Higher-Order Coordination in Neuronal Assemblies via De-Sparsified Orthogonal Matching Pursuit., and . NeurIPS, page 4120-4133. (2021)Probing the functional circuitry underlying auditory attention via dynamic granger causality analysis., , , , and . ACSSC, page 593-597. IEEE, (2016)Sparse Representation Learning Approach Resolves Deep Sources Underlying MEG and EEG Data., , , , , , and . AAAI Spring Symposia, AAAI Press, (2017)Regularized recursive least squares for anomaly detection in sparse channel tracking applications., and . RACS, page 277-281. ACM, (2011)Random frames from binary linear block codes., and . CISS, page 1-3. IEEE, (2010)Real-time Algorithms for Sparse Neuronal System Identification., and . EMBC, page 3410-3413. IEEE, (2016)