REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification

Meiyan Xu, Jie Jiao,Duo Chen, Yi Ding, Qingqing Chen, Jipeng Wu,Peipei Gu,Yijie Pan,Xueping Peng,Naian Xiao, Bokai Yang,Qiyuan Li,Jiayang Guo

IEEE journal of biomedical and health informatics（2025）

Cited 0|Views2

Abstract

High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2D representation that focuses on the time domain may loss the spatial information in EEG. In contrast, a 3D representation based on topography may suffer from channel loss and introduce noise through different padding methods. In this paper, we propose a framework called Reference Electrode Standardization Interpolation Network (REI-Net). Through an interpolation of 3D representation, REI-Net retains the temporal information in 2D scalp EEG while improving the spatial resolution within a certain montage. Additionally, to overcome the data variability caused by individual differences, transfer learning is employed to enhance the decoding robustness. Our approach achieves promising performance on two widely-recognized MI datasets, with an accuracy of 77.99% on BCI-C IV-2a and an accuracy of 63.94% on Kaya2018. The proposed algorithm outperforms the SOTAs leading to more accurate and robust results.

Translated text

Key words

Brain computer interface,electroencephalogram,motor imagery,interpolate method,transfer learning

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper

Data Disclaimer

The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn

Chat Paper

【要点】：本文提出了一种名为REI-Net的框架，通过3D CNN和参考电极标准化插值技术，在保留EEG信号时间信息的同时提高空间分辨率，用于更准确的运动想象分类。

【方法】：REI-Net结合了3D卷积神经网络和参考电极标准化插值技术，通过插值3D空间表示来提高空间分辨率，并采用迁移学习来克服个体差异引起的数据变化性。

【实验】：在BCI-C IV-2a和Kaya2018两个公认的MI数据集上进行的实验表明，REI-Net分别实现了77.99%和63.94%的准确率，优于当前最先进的技术（SOTA）。