MultiView Independent Component Analysis with Delays (2312.00484v1)
Abstract: Linear Independent Component Analysis (ICA) is a blind source separation technique that has been used in various domains to identify independent latent sources from observed signals. In order to obtain a higher signal-to-noise ratio, the presence of multiple views of the same sources can be used. In this work, we present MultiView Independent Component Analysis with Delays (MVICAD). This algorithm builds on the MultiView ICA model by allowing sources to be delayed versions of some shared sources: sources are shared across views up to some unknown latencies that are view- and source-specific. Using simulations, we demonstrate that MVICAD leads to better unmixing of the sources. Moreover, as ICA is often used in neuroscience, we show that latencies are age-related when applied to Cam-CAN, a large-scale magnetoencephalography (MEG) dataset. These results demonstrate that the MVICAD model can reveal rich effects on neural signals without human supervision.
- Faster independent component analysis by preconditioning with hessian approximations. IEEE Transactions on Signal Processing, 66(15):4040–4049.
- A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage, 45(1):S163–S172.
- Comon, P. (1994). Independent component analysis, a new concept? Signal processing, 36(3):287–314.
- Canonical correlation analysis for data fusion and group inferences. IEEE signal processing magazine, 27(4):39–50.
- Hyvarinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE transactions on Neural Networks, 10(3):626–634.
- Independent component analysis: algorithms and applications. Neural networks, 13(4-5):411–430.
- Extended ICA removes artifacts from electroencephalographic recordings. Advances in neural information processing systems, 10.
- Kettenring, J. R. (1971). Canonical analysis of several sets of variables. Biometrika, 58(3):433–451.
- Independent component analysis of electroencephalographic data. Advances in neural information processing systems, 8.
- Self-adaptive source separation. ii. comparison of the direct, feedback, and mixed linear network. IEEE transactions on signal processing, 46(1):39–50.
- Multi-view independent component analysis with shared and individual sources. arXiv preprint arXiv:2210.02083.
- Age-related delay in visual and auditory evoked responses is mediated by white-and grey-matter differences. Nature communications, 8(1):15671.
- Modeling shared responses in neuroimaging studies through multiview ICA. Advances in Neural Information Processing Systems, 33:19149–19162.
- MEG detection of delayed auditory evoked responses in autism spectrum disorders: towards an imaging biomarker for autism. Autism Research, 3(1):8–18.
- The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. neuroimage, 144:262–269.
- Combining PCA and multiset CCA for dimension reduction when group ICA is applied to decompose naturalistic fMRI data. In 2015 International Joint Conference on Neural Networks (IJCNN), pages 1–6. IEEE.
- CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series. arXiv preprint arXiv:0911.4650.
- Independent component approach to the analysis of EEG and MEG recordings. IEEE transactions on biomedical engineering, 47(5):589–593.