- Tytuł:
- Classification of cognitive states using clustering-split time series framework
- Autorzy:
-
Ramakrishna, J. Siva
Ramasangu, Hariharan - Powiązania:
- https://bibliotekanauki.pl/articles/38708362.pdf
- Data publikacji:
- 2024
- Wydawca:
- Instytut Podstawowych Problemów Techniki PAN
- Tematy:
-
functional MRI data
classification
consensus clustering
SVM classifier
GNB classifier
XGBoost
funkcjonalne dane MRI
klasyfikacja
grupowanie konsensusu
klasyfikator SVM
klasyfikator GNB - Opis:
- Over the last two decades, functional Magnetic Resonance Imaging (fMRI) has provided immense data about the dynamics of the brain. Ongoing developments in machine learning suggest improvements in the performance of fMRI data analysis. Clustering is one of the critical techniques in machine learning. Unsupervised clustering techniques are utilized to partition the data objects into different groups. Supervised classification techniques applied to fMRI data facilitate the decoding of cognitive states while a subject is engaged in a cognitive task. Due to the high dimensional, sparse, and noisy nature of fMRI data, designing a classifier model for estimating cognitive states becomes challenging. Feature selection and feature extraction techniques are critical aspects of fMRI data analysis. In this work, we present one such synergy, a combination of Hierarchical Consensus Clustering (HCC) and the Statistics of Split Timeseries (SST) framework to estimate cognitive states. The proposed HCC-SST model’s performance has been verified on StarPlus fMRI data. The obtained experimental results show that the proposed classifier model achieves 99% classification accuracy with a smaller number of voxels and lower computational cost.
- Źródło:
-
Computer Assisted Methods in Engineering and Science; 2024, 31, 2; 241-260
2299-3649 - Pojawia się w:
- Computer Assisted Methods in Engineering and Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł