Brain Signals Localization by Alternating Projections
We present a novel solution to the problem of localization of brain signals. The solution is sequential and iterative, and is based on minimizing the least-squares (LS) criterion by the alternating projection (AP) algorithm, well known in the context of array signal processing. Unlike existing solut...
| Main Authors: | , , |
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| Format: | Technical Report |
| Published: |
Center for Brains, Minds and Machines (CBMM), arXiv
2019
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| Online Access: | https://hdl.handle.net/1721.1/122034 |
| _version_ | 1826199545665552384 |
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| author | Adler, Amir Wax, Mati Pantazis, Dimitrios |
| author_facet | Adler, Amir Wax, Mati Pantazis, Dimitrios |
| author_sort | Adler, Amir |
| collection | MIT |
| description | We present a novel solution to the problem of localization of brain signals. The solution is sequential and iterative, and is based on minimizing the least-squares (LS) criterion by the alternating projection (AP) algorithm, well known in the context of array signal processing. Unlike existing solutions belonging to the linearly constrained minimum variance (LCMV) and to the multiple-signal classification (MUSIC) families, the algorithm is applicable even in the case of a single sample and in the case of synchronous sources. The performance of the solution is demonstrated via simulations. |
| first_indexed | 2024-09-23T11:21:44Z |
| format | Technical Report |
| id | mit-1721.1/122034 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:21:44Z |
| publishDate | 2019 |
| publisher | Center for Brains, Minds and Machines (CBMM), arXiv |
| record_format | dspace |
| spelling | mit-1721.1/1220342019-09-03T17:26:46Z Brain Signals Localization by Alternating Projections Adler, Amir Wax, Mati Pantazis, Dimitrios We present a novel solution to the problem of localization of brain signals. The solution is sequential and iterative, and is based on minimizing the least-squares (LS) criterion by the alternating projection (AP) algorithm, well known in the context of array signal processing. Unlike existing solutions belonging to the linearly constrained minimum variance (LCMV) and to the multiple-signal classification (MUSIC) families, the algorithm is applicable even in the case of a single sample and in the case of synchronous sources. The performance of the solution is demonstrated via simulations. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. 2019-09-03T17:26:45Z 2019-09-03T17:26:45Z 2019-08-29 Technical Report Working Paper Other https://hdl.handle.net/1721.1/122034 CBMM Memo;099 application/pdf Center for Brains, Minds and Machines (CBMM), arXiv |
| spellingShingle | Adler, Amir Wax, Mati Pantazis, Dimitrios Brain Signals Localization by Alternating Projections |
| title | Brain Signals Localization by Alternating Projections |
| title_full | Brain Signals Localization by Alternating Projections |
| title_fullStr | Brain Signals Localization by Alternating Projections |
| title_full_unstemmed | Brain Signals Localization by Alternating Projections |
| title_short | Brain Signals Localization by Alternating Projections |
| title_sort | brain signals localization by alternating projections |
| url | https://hdl.handle.net/1721.1/122034 |
| work_keys_str_mv | AT adleramir brainsignalslocalizationbyalternatingprojections AT waxmati brainsignalslocalizationbyalternatingprojections AT pantazisdimitrios brainsignalslocalizationbyalternatingprojections |