| Autor: |
Bişkin OT; Department of Electrical and Electronics Engineering, Burdur Mehmet Akif Ersoy University, Burdur 15030, Turkey., Candemir C; International Computer Institute, Ege University, Izmir 35100, Turkey.; Standardization of Computational Anatomy Techniques, SoCAT Lab, Ege University, Izmir 35100, Turkey., Gonul AS; Standardization of Computational Anatomy Techniques, SoCAT Lab, Ege University, Izmir 35100, Turkey.; Department of Psychiatry, Medical Faculty, Ege University, Izmir 35100, Turkey., Selver MA; Department of Electrical and Electronics Engineering and Izmir Health Technologies Development and Accelerator (BioIzmir), Dokuz Eylul University, Izmir 35160, Turkey. |
| Abstrakt: |
One of the emerging fields in functional magnetic resonance imaging (fMRI) is the decoding of different stimulations. The underlying idea is to reveal the hidden representative signal patterns of various fMRI tasks for achieving high task-classification performance. Unfortunately, when multiple tasks are processed, performance remains limited due to several challenges, which are rarely addressed since the majority of the state-of-the-art studies cover a single neuronal activity task. Accordingly, the first contribution of this study is the collection and release of a rigorously acquired dataset, which contains cognitive, behavioral, and affective fMRI tasks together with resting state. After a comprehensive analysis of the pitfalls of existing systems on this new dataset, we propose an automatic multitask classification (MTC) strategy using a feature fusion module (FFM). FFM aims to create a unique signature for each task by combining deep features with time-frequency representations. We show that FFM creates a feature space that is superior for representing task characteristics compared to their individual use. Finally, for MTC, we test a diverse set of deep-models and analyze their complementarity. Our results reveal higher classification accuracy compared to benchmarks. Both the dataset and the code are accessible to researchers for further developments. |
