# EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces

arXiv: https://arxiv.org/abs/2006.00622 · HTML: https://mlsanity.com/paper/2006.00622/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 30 of 63)

- [MAtt: A Manifold Attention Network for EEG Decoding](https://mlsanity.com/paper/2210.01986/) (2022)
- [The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark](https://mlsanity.com/paper/2404.15319/) (2024)
- [Physics-Informed Computer Vision: A Review and Perspectives](https://mlsanity.com/paper/2305.18035/) (2023)
- [ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Network](https://mlsanity.com/paper/2103.13740/) (2021)
- [Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design](https://mlsanity.com/paper/2103.15750/) (2021)
- [EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification](https://mlsanity.com/paper/2204.06947/) (2022)
- [EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms](https://mlsanity.com/paper/2310.11198/) (2023)
- [Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring](https://mlsanity.com/paper/2203.04396/) (2022)
- [Learning to Normalize on the SPD Manifold under Bures-Wasserstein Geometry](https://mlsanity.com/paper/2504.00660/) (2025)
- [Neural Networks on Symmetric Spaces of Noncompact Type](https://mlsanity.com/paper/2601.01097/) (2026)
- [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)
- [Geometric Neural Network based on Phase Space for BCI-EEG decoding](https://mlsanity.com/paper/2403.05645/) (2024)
- [Fuzzy temporal convolutional neural networks in P300-based Brain-computer interface for smart home interaction](https://mlsanity.com/paper/2204.04338/) (2022)
- [Robust Hyperbolic Learning with Curvature-Aware Optimization](https://mlsanity.com/paper/2405.13979/) (2024)
- [Neural Spelling: A Spell-Based BCI System for Language Neural Decoding](https://mlsanity.com/paper/2501.17489/) (2025)
- [Pretraining Large Brain Language Model for Active BCI: Silent Speech](https://mlsanity.com/paper/2504.21214/) (2025)
- [Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training](https://mlsanity.com/paper/2404.06966/) (2024)
- [Cross Task Neural Architecture Search for EEG Signal Classifications](https://mlsanity.com/paper/2210.06298/) (2022)
- [Fusing Pretrained ViTs with TCNet for Enhanced EEG Regression](https://mlsanity.com/paper/2404.15311/) (2024)
- [MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning](https://mlsanity.com/paper/2407.05550/) (2024)
- [EEG-EMG FAConformer: Frequency Aware Conv-Transformer for the fusion of EEG and EMG](https://mlsanity.com/paper/2409.18973/) (2024)
- [AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification](https://mlsanity.com/paper/2506.21338/) (2025)
- [Investigating the Impact of Rational Dilated Wavelet Transform on Motor Imagery EEG Decoding with Deep Learning Models](https://mlsanity.com/paper/2510.09242/) (2025)
- [EEG-FM-Audit: A Systematic Evaluation and Analysis Pipeline for EEG Foundation Models](https://mlsanity.com/paper/2605.26910/) (2026)
- [DGAFF: Deep Genetic Algorithm Fitness Formation for EEG Bio-Signal Channel Selection](https://mlsanity.com/paper/2202.10034/) (2022)
- [EEGEncoder: Advancing BCI with Transformer-Based Motor Imagery Classification](https://mlsanity.com/paper/2404.14869/) (2024)
- [Masked EEG Modeling for Driving Intention Prediction](https://mlsanity.com/paper/2408.07083/) (2024)
- [CoSupFormer : A Contrastive Supervised learning approach for EEG signal Classification](https://mlsanity.com/paper/2509.20489/) (2025)
- [Bidirectional Time-Frequency Pyramid Network for Enhanced Robust EEG Classification](https://mlsanity.com/paper/2510.10004/) (2025)

## Abstract (arXiv)

In recent years, deep learning (DL) has contributed significantly to the improvement of motor-imagery brain-machine interfaces (MI-BMIs) based on electroencephalography(EEG). While achieving high classification accuracy, DL models have also grown in size, requiring a vast amount of memory and computational resources. This poses a major challenge to an embedded BMI solution that guarantees user privacy, reduced latency, and low power consumption by processing the data locally. In this paper, we propose EEG-TCNet, a novel temporal convolutional network (TCN) that achieves outstanding accuracy while requiring few trainable parameters. Its low memory footprint and low computational complexity for inference make it suitable for embedded classification on resource-limited devices at the edge. Experimental results on the BCI Competition IV-2a dataset show that EEG-TCNet achieves 77.35% classification accuracy in 4-class MI. By finding the optimal network hyperparameters per subject, we further improve the accuracy to 83.84%. Finally, we demonstrate the versatility of EEG-TCNet on the Mother of All BCI Benchmarks (MOABB), a large scale test benchmark containing 12 different EEG datasets with MI experiments. The results indicate that EEG-TCNet successfully generalizes beyond one single dataset, outperforming the current state-of-the-art (SoA) on MOABB by a meta-effect of 0.25.