# STN: a new tensor network method to identify stimulus category from brain activity pattern

arXiv: https://arxiv.org/abs/2210.16993 · HTML: https://mlsanity.com/paper/2210.16993/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 67 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Interpreting Encoding and Decoding Models](https://mlsanity.com/paper/1812.00278/) (2018)
- [Parallelizable Algorithms for Optimization Problems with Orthogonality Constraints](https://mlsanity.com/paper/1810.03930/) (2018)
- [Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis](https://mlsanity.com/paper/1806.07703/) (2018)
- [Discriminative Feature Selection for Uncertain Graph Classification](https://mlsanity.com/paper/1301.6626/) (2013)
- [A review of heterogeneous data mining for brain disorders](https://mlsanity.com/paper/1508.01023/) (2015)
- [Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification](https://mlsanity.com/paper/1508.04554/) (2015)
- [Visual Motion Imagery Classification with Deep Neural Network based on Functional Connectivity](https://mlsanity.com/paper/2103.02851/) (2021)

## Abstract (arXiv)

Neural decoding is still a challenge and hot topic in neurocomputing science. Recently, many studies have shown that brain network patterns containing rich spatial and temporal structure information, which represents the activation information of brain under external stimuli. %Therefore, the research of decoding stimuli from brain network received extensive more attention. The traditional method extracts brain network features directly from the common machine learning method, then puts these features into the classifier, and realizes to decode external stimuli. However, this method cannot effectively extract the multi-dimensional structural information, which is hidden in the brain network. The tensor researchers show that the tensor decomposition model can fully mine unique spatio-temporal structure characteristics in multi-dimensional structure data. This research proposed a stimulus constrained tensor brain model(STN)which involves the tensor decomposition idea and stimulus category constraint information. The model was verified on the real neuroimaging data sets (MEG and fMRI). The experimental results show that the STN model achieves more than 11.06% and 18.46% on accuracy matrix compared with others methods on two modal data sets. These results imply the superiority of extracting discriminative characteristics about STN model, especially for decoding object stimuli with semantic information.