Brain Source Localization in Head Harmonics Domain

• Published in: IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 10
• Main Authors: Giri, Amita, Kumar, Lalan, Gandhi, Tapan Kumar
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Basis functions; Brain; Brain modeling; Brain Source Localization (BSL); Domains; Electroencephalography; forward problem; Harmonic analysis; Head; Head Harmonics (H²); inverse problem; Localization; Magnetic heads; Multiple signal classification; MUltiple SIgnal Classification (MUSIC); Music; Performance evaluation; Recursive MUSIC (R-MUSIC); Recursively Applied and Projected MUSIC (RAP-MUSIC); Root-mean-square errors; Seizures; Shape; Signal classification; Spherical harmonics; Spherical Harmonics (SH); Visual tasks
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2020.3026511

Brain source localization (BSL) using Electro- EncephaloGram (EEG) has been an active area of research because of its cost-effective and noninvasive nature of the operation. As the EEG data are spatially sampled over the head, the subsequent localization performance is limited by the head-shape assumption for efficient data representation. In the literature, the human head is approximated by spherical shape. Hence, spherical harmonics, the corresponding basis functions, have been the natural choice for EEG source reconstruction and localization. In this article, a new set of basis functions called Head Harmonics (H 2 ) is developed to accurately represent the data sampled over head. The basis functions are formulated based on a more realistic head dimension. In addition, the forward model for source localization is presented in the H 2 domain. Subsequently, H 2 MUltiple SIgnal Classification (H 2 -MUSIC), H 2 Recursive MUSIC (H 2 R-MUSIC), and H 2 Recursively Applied and Projected MUSIC (H 2 RAP-MUSIC) methods are presented for BSL. For simulation, Root Mean Square Error (RMSE), computation time, and STandard Deviation (STD) ratio measures are utilized to evaluate the performance of the proposed algorithms. Real EEG data corresponding to visual stimulation, arithmetic task, and seizure are also utilized to validate the correctness of the proposed H 2 -based algorithms. The proposed model can be used with EEG instrumentation to localize neural sources for various real-time applications.