# EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification

arXiv: https://arxiv.org/abs/2204.06947 · HTML: https://mlsanity.com/paper/2204.06947/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 46 references that are in the map.
- First posted on arXiv 14 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces](https://mlsanity.com/paper/2006.00622/) (2020)
- [Spatial-Temporal Recurrent Neural Network for Emotion Recognition](https://mlsanity.com/paper/1705.04515/) (2017)
- [Machine-Learning-Based Diagnostics of EEG Pathology](https://mlsanity.com/paper/2002.05115/) (2020)
- [Fuzzy temporal convolutional neural networks in P300-based Brain-computer interface for smart home interaction](https://mlsanity.com/paper/2204.04338/) (2022)

## Built on by (most cited first, 11 of 11)

- [WaveMind: Towards a Conversational EEG Foundation Model Aligned to Textual and Visual Modalities](https://mlsanity.com/paper/2510.00032/) (2025)
- [Pseudo-online framework for BCI evaluation: A MOABB perspective](https://mlsanity.com/paper/2308.11656/) (2023)
- [A Strong and Simple Deep Learning Baseline for BCI MI Decoding](https://mlsanity.com/paper/2309.07159/) (2023)
- [Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space](https://mlsanity.com/paper/2312.13033/) (2023)
- [Geometric Neural Network based on Phase Space for BCI-EEG decoding](https://mlsanity.com/paper/2403.05645/) (2024)
- [Towards Unified Neural Decoding of Perceived, Spoken and Imagined Speech from EEG Signals](https://mlsanity.com/paper/2411.09243/) (2024)
- [Motor Imagery EEG Signal Classification Using Minimally Random Convolutional Kernel Transform and Hybrid Deep Learning](https://mlsanity.com/paper/2508.16179/) (2025)
- [Distinct Theta Synchrony across Speech Modes: Perceived, Spoken, Whispered, and Imagined](https://mlsanity.com/paper/2511.07918/) (2025)
- [LI-DSN: A Layer-wise Interactive Dual-Stream Network for EEG Decoding](https://mlsanity.com/paper/2604.01889/) (2026)
- [SUP-MCRL: Subject-aware Unified Pseudo-feature Coded Multimodal Contrastive Representation Learning for EEG Visual Decoding](https://mlsanity.com/paper/2606.16615/) (2026)
- [EEG-Fusion: Failure-Informed Source-Free Expert Routing for Robust Motor Imagery EEG Decoding](https://mlsanity.com/paper/2609.33962/) (2026)

## Abstract (arXiv)

In recent years, neural networks and especially deep architectures have received substantial attention for EEG signal analysis in the field of brain-computer interfaces (BCIs). In this ongoing research area, the end-to-end models are more favoured than traditional approaches requiring signal transformation pre-classification. They can eliminate the need for prior information from experts and the extraction of handcrafted features. However, although several deep learning algorithms have been already proposed in the literature, achieving high accuracies for classifying motor movements or mental tasks, they often face a lack of interpretability and therefore are not quite favoured by the neuroscience community. The reasons behind this issue can be the high number of parameters and the sensitivity of deep neural networks to capture tiny yet unrelated discriminative features. We propose an end-to-end deep learning architecture called EEG-ITNet and a more comprehensible method to visualise the network learned patterns. Using inception modules and causal convolutions with dilation, our model can extract rich spectral, spatial, and temporal information from multi-channel EEG signals with less complexity (in terms of the number of trainable parameters) than other existing end-to-end architectures, such as EEG-Inception and EEG-TCNet. By an exhaustive evaluation on dataset 2a from BCI competition IV and OpenBMI motor imagery dataset, EEG-ITNet shows up to 5.9% improvement in the classification accuracy in different scenarios with statistical significance compared to its competitors. We also comprehensively explain and support the validity of network illustration from a neuroscientific perspective. We have also made our code open at https://github.com/AbbasSalami/EEG-ITNet