# MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

arXiv: https://arxiv.org/abs/2102.03814 · HTML: https://mlsanity.com/paper/2102.03814/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 63 references that are in the map.
- First posted on arXiv 7 Feb 2021.

## Builds on (most cited first, 16 of 16)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [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)
- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)
- [Deep Metric Learning with Hierarchical Triplet Loss](https://mlsanity.com/paper/1810.06951/) (2018)
- [Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification](https://mlsanity.com/paper/1910.12278/) (2019)
- [Ranked List Loss for Deep Metric Learning](https://mlsanity.com/paper/1903.03238/) (2019)
- [Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals](https://mlsanity.com/paper/1912.12127/) (2019)
- [Universal Joint Feature Extraction for P300 EEG Classification using Multi-task Autoencoder](https://mlsanity.com/paper/1808.06541/) (2018)
- [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)
- [Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting](https://mlsanity.com/paper/2004.04107/) (2020)
- [A Single-Channel Consumer-Grade EEG Device for Brain-Computer Interface: Enhancing Detection of SSVEP and Its Amplitude Modulation](https://mlsanity.com/paper/1809.07356/) (2018)

## Built on by (most cited first, 16 of 16)

- [EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces](https://mlsanity.com/paper/2405.00719/) (2024)
- [EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification](https://mlsanity.com/paper/2407.20254/) (2024)
- [Interpretable and Robust AI in EEG Systems: A Survey](https://mlsanity.com/paper/2304.10755/) (2023)
- [Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training](https://mlsanity.com/paper/2211.11460/) (2022)
- [MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification](https://mlsanity.com/paper/2409.04104/) (2024)
- [Towards Homogeneous Lexical Tone Decoding from Heterogeneous Intracranial Recordings](https://mlsanity.com/paper/2410.12866/) (2024)
- [Subject-Adaptive Transfer Learning Using Resting State EEG Signals for Cross-Subject EEG Motor Imagery Classification](https://mlsanity.com/paper/2405.19346/) (2024)
- [Rethinking Saliency Map: An Context-aware Perturbation Method to Explain EEG-based Deep Learning Model](https://mlsanity.com/paper/2205.14976/) (2022)
- [ANet: Autoencoder-Based Local Field Potential Feature Extractor for Evaluating An Antidepressant Effect in Mice after Administering Kratom Leaf Extracts](https://mlsanity.com/paper/2209.08210/) (2022)
- [End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfaces](https://mlsanity.com/paper/2307.12827/) (2023)
- [A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification](https://mlsanity.com/paper/2309.11714/) (2023)
- [NiSNN-A: Non-iterative Spiking Neural Networks with Attention with Application to Motor Imagery EEG Classification](https://mlsanity.com/paper/2312.05643/) (2023)
- [Mirror contrastive loss based sliding window transformer for subject-independent motor imagery based EEG signal recognition](https://mlsanity.com/paper/2409.00130/) (2024)
- [System Filter-Based Common Components Modeling for Cross-Subject EEG Decoding](https://mlsanity.com/paper/2507.05268/) (2025)
- [DLGE: Dual Local-Global Encoding for Generalizable Cross-BCI-Paradigm](https://mlsanity.com/paper/2509.07991/) (2025)
- [EEG-Based Motor Imagery BCI Algorithms and Technologies: A Review](https://mlsanity.com/paper/2609.32930/) (2026)

## Abstract (arXiv)

Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite great advances in MI-based BCI, EEG rhythms are specific to a subject and various changes over time. These issues point to significant challenges to enhance the classification performance, especially in a subject-independent manner. To overcome these challenges, we propose MIN2Net, a novel end-to-end multi-task learning to tackle this task. We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously. This approach reduces the complexity in pre-processing, results in significant performance improvement on EEG classification. Experimental results in a subject-independent manner show that MIN2Net outperforms the state-of-the-art techniques, achieving an F1-score improvement of 6.72%, and 2.23% on the SMR-BCI, and OpenBMI datasets, respectively. We demonstrate that MIN2Net improves discriminative information in the latent representation. This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without the need for calibration.