Work

Localization of Brain Signals by Signal Subspace Matching

Year: 2025

Type: conference-paper

Abstract: We present a novel approach for solving the inverse problem in electroencephalography (EEG) using the signal subspace matching (SSM) criterion. The approach is based on an intuitively pleasing metric, aimed at matching the error-contaminated model-based signal subspace to its noisy sample-data counterpart. SSM is formulated using an iterative scheme inspired by the alternating projection (AP) solution to the deterministic maximum likelihood, which involves only 1-dimensional optimization. We compare the SSM algorithm to RAP-MUSIC and AP and show its advantages in various realistic settings. We find that SSM performs particularly well when 1) neural sources are highly correlated, 2) noise at different channels is correlated, and 3) when the number of sources is under-estimated. All these scenarios are commonly encountered with real EEG recordings, therefore the SSM algorithm provides a significant improvement over other similar algorithms such as RAP-MUSIC and AP when applied to real recordings.

Source: IEEE International Symposium on Biomedical Imaging (ISBI)

DOI: 10.1109/isbi60581.2025.10980755

Authors: Lukas Hecker, Amir Adler, Dimitrios Pantazis, Mati Wax, Amita Giri

Institutions: Braude College of Engineering Karmiel, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Technion – Israel Institute of Technology

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funders: United States-Israel Binational Science Foundation, National Institutes of Health

Awards: G8972882399, G9000096426