# MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification

arXiv: https://arxiv.org/abs/2409.04104 · HTML: https://mlsanity.com/paper/2409.04104/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 61 references that are in the map.
- First posted on arXiv 6 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [What Makes Training Multi-Modal Classification Networks Hard?](https://mlsanity.com/paper/1905.12681/) (2019)
- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017)
- [XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging](https://mlsanity.com/paper/2007.05492/) (2020)
- [MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification](https://mlsanity.com/paper/2102.03814/) (2021)
- [Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals](https://mlsanity.com/paper/1912.12127/) (2019)
- [Affective EEG-Based Person Identification Using the Deep Learning Approach](https://mlsanity.com/paper/1807.03147/) (2018)
- [Improving EEG Decoding via Clustering-based Multi-task Feature Learning](https://mlsanity.com/paper/2012.06813/) (2020)

## Built on by (most cited first, 3 of 3)

- [BiT-MamSleep: Bidirectional Temporal Mamba for EEG Sleep Staging](https://mlsanity.com/paper/2411.01589/) (2024)
- [EEG-to-Text Translation: A Model for Deciphering Human Brain Activity](https://mlsanity.com/paper/2505.13936/) (2025)
- [DELTA: Language Diffusion-based EEG-to-Text Architecture](https://mlsanity.com/paper/2511.21746/) (2025)

## Abstract (arXiv)

Recent advances in deep learning (DL) have significantly impacted motor imagery (MI)-based brain-computer interface (BCI) systems, enhancing the decoding of electroencephalography (EEG) signals. However, most studies struggle to identify discriminative patterns across subjects during MI tasks, limiting MI classification performance. In this article, we propose MixNet, a novel classification framework designed to overcome this limitation by utilizing spectral-spatial signals from MI data, along with a multitask learning architecture named MIN2Net, for classification. Here, the spectral-spatial signals are generated using the filter-bank common spatial patterns (FBCSPs) method on MI data. Since the multitask learning architecture is used for the classification task, the learning in each task may exhibit different generalization rates and potential overfitting across tasks. To address this issue, we implement adaptive gradient blending, simultaneously regulating multiple loss weights and adjusting the learning pace for each task based on its generalization/overfitting tendencies. Experimental results on six benchmark data sets of different data sizes demonstrate that MixNet consistently outperforms all state-of-the-art algorithms in subject-dependent and -independent settings. Finally, the low-density EEG MI classification results show that MixNet outperforms all state-of-the-art algorithms, offering promising implications for Internet of Thing (IoT) applications, such as lightweight and portable EEG wearable devices based on low-density montages.