# Classification of High-Dimensional Motor Imagery Tasks based on An End-to-end role assigned convolutional neural network

arXiv: https://arxiv.org/abs/2002.00210 · HTML: https://mlsanity.com/paper/2002.00210/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2020.

## Built on by (most cited first, 7 of 7)

- [EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification](https://mlsanity.com/paper/2101.10932/) (2021)
- [Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks](https://mlsanity.com/paper/2107.07335/) (2021)
- [Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface](https://mlsanity.com/paper/2206.08494/) (2022)
- [Hybrid Paradigm-based Brain-Computer Interface for Robotic Arm Control](https://mlsanity.com/paper/2212.08122/) (2022)
- [Siamese Sleep Transformer For Robust Sleep Stage Scoring With Self-knowledge Distillation and Selective Batch Sampling](https://mlsanity.com/paper/2212.13919/) (2022)
- [Sample Dominance Aware Framework via Non-Parametric Estimation for Spontaneous Brain-Computer Interface](https://mlsanity.com/paper/2311.07079/) (2023)
- [FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network](https://mlsanity.com/paper/2403.03526/) (2024)

## Abstract (arXiv)

A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. Electroencephalogram (EEG) motor imagery (MI) paradigm is widely used in non-invasive BCI to obtain encoded signals contained user intention of movement execution. However, EEG has intricate and non-stationary properties resulting in insufficient decoding performance. By imagining numerous movements of a single-arm, decoding performance can be improved without artificial command matching. In this study, we collected intuitive EEG data contained the nine different types of movements of a single-arm from 9 subjects. We propose an end-to-end role assigned convolutional neural network (ERA-CNN) which considers discriminative features of each upper limb region by adopting the principle of a hierarchical CNN architecture. The proposed model outperforms previous methods on 3-class, 5-class and two different types of 7-class classification tasks. Hence, we demonstrate the possibility of decoding user intention by using only EEG signals with robust performance using an ERA-CNN.