| Residential College | false |
| Status | 已發表Published |
| Empirical validation of task-related component analysis reformulation for computational complexity reduction | |
Chiang, Kuan Jung1,2; Wong, Chi Man3; Wan, Feng3 ; Jung, Tzyy Ping2; Nakanishi, Masaki2
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| 2023-09-01 | |
| Source Publication | Biomedical Signal Processing and Control
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| ISSN | 1746-8094 |
| Volume | 86 |
| Abstract | The task-related component analysis (TRCA) is a data-driven method for extracting reproducible components across multiple data segments from multivariate data. TRCA has been proven effective in enhancing the signal-to-noise ratio of neuroimaging data in previous studies. However, its original form requires a computational cost of O(N) to compute the sum of cross-covariance matrices, indicating that the computational time increases rapidly as the number of data segments N increases. This study aims to empirically validate that the reformulated form of TRCA can reduce its computational complexity. This study estimated that the reformulation can reduce the computational complexity from O(N) to O(N) without distorting its outputs by reducing the number of matrix multiplications in computing the sum of cross-covariance matrices. A series of computer simulations using computer-generated synthetic, functional near-infrared spectroscopy (fNIRS), and electroencephalogram (EEG) data were conducted to verify the theoretical computation complexity and consistency of the outputs between the original and reformulated TRCA. The computation reduction was validated through an experimental comparison of the original and reformulated TRCA with synthetic and real data of various sizes (e.g., data segments, dimensions, and lengths). In addition, the two algorithms output almost exactly identical results with only floating-point errors. The reformulation would considerably benefit practical applications, especially when applying the TRCA to large-scale computations. The code is available at https://github.com/mnakanishi/TRCA-SSVEP. |
| Keyword | Biomedical data analysis Electroencephalogram Functional near-infrared spectroscopy Generalized eigenvalue problem Task-related component analysis |
| DOI | 10.1016/j.bspc.2023.105220 |
| URL | View the original |
| Language | 英語English |
| Scopus ID | 2-s2.0-85164217672 |
| Fulltext Access | |
| Citation statistics | |
| Document Type | Journal article |
| Collection | DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING |
| Affiliation | 1.Department of Computer Science and Engineering, University of California San Diego, La Jolla, 9500 Gilman Dr, 92093, United States 2.Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, 9500 Gilman Dr, 92093, United States 3.Department of Electrical and Computer Engineering, University of Macau, Taipa, Avenida da Universidade, Macao |
| Recommended Citation GB/T 7714 | Chiang, Kuan Jung,Wong, Chi Man,Wan, Feng,et al. Empirical validation of task-related component analysis reformulation for computational complexity reduction[J]. Biomedical Signal Processing and Control, 2023, 86. |
| APA | Chiang, Kuan Jung., Wong, Chi Man., Wan, Feng., Jung, Tzyy Ping., & Nakanishi, Masaki (2023). Empirical validation of task-related component analysis reformulation for computational complexity reduction. Biomedical Signal Processing and Control, 86. |
| MLA | Chiang, Kuan Jung,et al."Empirical validation of task-related component analysis reformulation for computational complexity reduction".Biomedical Signal Processing and Control 86(2023). |
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