# EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification

arXiv: https://arxiv.org/abs/2101.10932 · HTML: https://mlsanity.com/paper/2101.10932/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 58 references that are in the map.
- First posted on arXiv 24 Jan 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Ablation Studies in Artificial Neural Networks](https://mlsanity.com/paper/1901.08644/) (2019)
- [Classification of High-Dimensional Motor Imagery Tasks based on An End-to-end role assigned convolutional neural network](https://mlsanity.com/paper/2002.00210/) (2020)
- [A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis](https://mlsanity.com/paper/2008.11226/) (2020)
- [A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery](https://mlsanity.com/paper/2008.11227/) (2020)

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

- [Neuro-GPT: Towards A Foundation Model for EEG](https://mlsanity.com/paper/2311.03764/) (2023)
- [Interpretable and Robust AI in EEG Systems: A Survey](https://mlsanity.com/paper/2304.10755/) (2023)
- [Attention-based Neural Network for Driving Environment Complexity Perception](https://mlsanity.com/paper/2106.11277/) (2021)
- [LEAD: An EEG Foundation Model for Alzheimer's Disease Detection](https://mlsanity.com/paper/2502.01678/) (2025)
- [EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks](https://mlsanity.com/paper/2306.13109/) (2023)
- [A Strong and Simple Deep Learning Baseline for BCI MI Decoding](https://mlsanity.com/paper/2309.07159/) (2023)
- [SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals](https://mlsanity.com/paper/2404.15353/) (2024)
- [AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification](https://mlsanity.com/paper/2506.21338/) (2025)
- [Enhancing Motor Imagery Decoding in Brain Computer Interfaces using Riemann Tangent Space Mapping and Cross Frequency Coupling](https://mlsanity.com/paper/2310.19198/) (2023)
- [ADformer: A Multi-Granularity Spatial-Temporal Transformer for EEG-Based Alzheimer Detection](https://mlsanity.com/paper/2409.00032/) (2024)
- [The evolution of AI approaches for motor imagery EEG-based BCIs](https://mlsanity.com/paper/2210.06290/) (2022)
- [EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals](https://mlsanity.com/paper/2211.03878/) (2022)
- [EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG](https://mlsanity.com/paper/2212.04951/) (2022)
- [Brain-Language-Action (BLA) Models: Language-Conditioned EEG for Robotics Control](https://mlsanity.com/paper/2608.28967/) (2026)
- [EEG-Fusion: Failure-Informed Source-Free Expert Routing for Robust Motor Imagery EEG Decoding](https://mlsanity.com/paper/2609.33962/) (2026)
- [ThinkNet: Evolutionary Deep Ensembles for Efficient Motor-Imagery EEG Classification](https://mlsanity.com/paper/2609.33967/) (2026)

## Abstract (arXiv)

Classification of EEG-based motor imagery (MI) is a crucial non-invasive application in brain-computer interface (BCI) research. This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based MI classification that outperforms the state-of-the-art methods. The proposed CNN model, namely EEG-Inception, is built on the backbone of the Inception-Time network, which showed to be highly efficient and accurate for time-series classification. Also, the proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing. Furthermore, this paper proposes a novel data augmentation method for EEG signals to enhance the accuracy, at least by 3%, and reduce overfitting with limited BCI datasets. The proposed model outperforms all the state-of-the-art methods by achieving the average accuracy of 88.4% and 88.6% on the 2008 BCI Competition IV 2a (four-classes) and 2b datasets (binary-classes), respectively. Furthermore, it takes less than 0.025 seconds to test a sample suitable for real-time processing. Moreover, the classification standard deviation for nine different subjects achieves the lowest value of 5.5 for the 2b dataset and 7.1 for the 2a dataset, which validates that the proposed method is highly robust. From the experiment results, it can be inferred that the EEG-Inception network exhibits a strong potential as a subject-independent classifier for EEG-based MI tasks.