# Time-space-frequency feature Fusion for 3-channel motor imagery classification

arXiv: https://arxiv.org/abs/2304.01461 · HTML: https://mlsanity.com/paper/2304.01461/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach](https://mlsanity.com/paper/1808.05464/) (2018)
- [Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework](https://mlsanity.com/paper/2202.09559/) (2022)
- [LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability](https://mlsanity.com/paper/2303.16407/) (2023)

## Built on by (most cited first, 2 of 2)

- [Pseudo Channel: Time Embedding for Motor Imagery Decoding](https://mlsanity.com/paper/2405.15812/) (2024)
- [FSCE: A Target-Aware Frequency-Spatial Collaborative Enhancement Framework for Noise-Resilient SAR ATR](https://mlsanity.com/paper/2603.21565/) (2026)

## Abstract (arXiv)

Low-channel EEG devices are crucial for portable and entertainment applications. However, the low spatial resolution of EEG presents challenges in decoding low-channel motor imagery. This study introduces TSFF-Net, a novel network architecture that integrates time-space-frequency features, effectively compensating for the limitations of single-mode feature extraction networks based on time-series or time-frequency modalities. TSFF-Net comprises four main components: time-frequency representation, time-frequency feature extraction, time-space feature extraction, and feature fusion and classification. Time-frequency representation and feature extraction transform raw EEG signals into time-frequency spectrograms and extract relevant features. The time-space network processes time-series EEG trials as input and extracts temporal-spatial features. Feature fusion employs MMD loss to constrain the distribution of time-frequency and time-space features in the Reproducing Kernel Hilbert Space, subsequently combining these features using a weighted fusion approach to obtain effective time-space-frequency features. Moreover, few studies have explored the decoding of three-channel motor imagery based on time-frequency spectrograms. This study proposes a shallow, lightweight decoding architecture (TSFF-img) based on time-frequency spectrograms and compares its classification performance in low-channel motor imagery with other methods using two publicly available datasets. Experimental results demonstrate that TSFF-Net not only compensates for the shortcomings of single-mode feature extraction networks in EEG decoding, but also outperforms other state-of-the-art methods. Overall, TSFF-Net offers considerable advantages in decoding low-channel motor imagery and provides valuable insights for algorithmically enhancing low-channel EEG decoding.