{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multivariate-convolutional-sparse-coding-for","title":"Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals","arxiv_id":"1805.09654","date":"2018-05-24","proceeding":"NeurIPS 2018 12","authors":["Tom Dupré La Tour","Thomas Moreau","Mainak Jas","Alexandre Gramfort"],"abstract":"Frequency-specific patterns of neural activity are traditionally interpreted\nas sustained rhythmic oscillations, and related to cognitive mechanisms such as\nattention, high level visual processing or motor control. While alpha waves\n(8-12 Hz) are known to closely resemble short sinusoids, and thus are revealed\nby Fourier analysis or wavelet transforms, there is an evolving debate that\nelectromagnetic neural signals are composed of more complex waveforms that\ncannot be analyzed by linear filters and traditional signal representations. In\nthis paper, we propose to learn dedicated representations of such recordings\nusing a multivariate convolutional sparse coding (CSC) algorithm. Applied to\nelectroencephalography (EEG) or magnetoencephalography (MEG) data, this method\nis able to learn not only prototypical temporal waveforms, but also associated\nspatial patterns so their origin can be localized in the brain. Our algorithm\nis based on alternated minimization and a greedy coordinate descent solver that\nleads to state-of-the-art running time on long time series. To demonstrate the\nimplications of this method, we apply it to MEG data and show that it is able\nto recover biological artifacts. More remarkably, our approach also reveals the\npresence of non-sinusoidal mu-shaped patterns, along with their topographic\nmaps related to the somatosensory cortex.","url_abs":"http://arxiv.org/abs/1805.09654v2","url_pdf":"http://arxiv.org/pdf/1805.09654v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multivariate-convolutional-sparse-coding-for","repo_url":"https://github.com/alphacsc/alphacsc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}