East Azerbaijan, New Sahand Town, Sahand University of Technology, Faculty of Biomedical Engineering
10.22034/tjee.2026.68536.5064
Abstract
Functional magnetic resonance imaging (fMRI) is a crucial tool for investigating brain activity. However, analyzing fMRI data presents significant challenges due to the complex temporal nature of the signals and uncertainties in the algorithms used. Classical Independent Component Analysis (ICA) algorithms, such as FastICA, often struggle with high false positive rates and unstable results because they rely on the strict assumption of complete statistical independence. This study aims to comprehensively compare three ICA-based algorithms: FastICA, Entropy Bound Minimization (ERBM), and Semi-Blind Spatial ICA (SBS-ICA). The objective is to assess how different statistical assumptions and prior information affect the quality of component separation in fMRI data and the accurate identification of brain activation regions. Evaluations were conducted using component numbers of 40, 50, 60, and 70. The results revealed that the SBS-ICA algorithm, which benefits from spatial prior information, demonstrated the best performance with an area under the ROC curve (AUC) of 0/999, a high correlation of 0/89, and the lowest number of spatial false positives. The ERBM algorithm, which models temporal correlations, outperformed FastICA, showing a lower mean squared error (MSE = 0/079) and more stable correlation values. In contrast, FastICA exhibited the weakest performance among the three algorithms. These findings highlight the advantages of ICA-based guided methods and emphasize the significance of incorporating task modeling for accurate analysis of fMRI data.
Tajarrod, A. H. , Hossein Khani, T. , Shamsi, M. and Zarei, A. (2026). The role of task-based restriction and statistical models in detecting brain activity with ICA: fMRI time series analysis. Tabriz Journal of Electrical Engineering, (), -. doi: 10.22034/tjee.2026.68536.5064
MLA
Tajarrod, A. H. , , Hossein Khani, T. , , Shamsi, M. , and Zarei, A. . "The role of task-based restriction and statistical models in detecting brain activity with ICA: fMRI time series analysis", Tabriz Journal of Electrical Engineering, , , 2026, -. doi: 10.22034/tjee.2026.68536.5064
HARVARD
Tajarrod, A. H., Hossein Khani, T., Shamsi, M., Zarei, A. (2026). 'The role of task-based restriction and statistical models in detecting brain activity with ICA: fMRI time series analysis', Tabriz Journal of Electrical Engineering, (), pp. -. doi: 10.22034/tjee.2026.68536.5064
CHICAGO
A. H. Tajarrod , T. Hossein Khani , M. Shamsi and A. Zarei, "The role of task-based restriction and statistical models in detecting brain activity with ICA: fMRI time series analysis," Tabriz Journal of Electrical Engineering, (2026): -, doi: 10.22034/tjee.2026.68536.5064
VANCOUVER
Tajarrod, A. H., Hossein Khani, T., Shamsi, M., Zarei, A. The role of task-based restriction and statistical models in detecting brain activity with ICA: fMRI time series analysis. Tabriz Journal of Electrical Engineering, 2026; (): -. doi: 10.22034/tjee.2026.68536.5064
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