利用混合深度学习模型避免脑机接口偏差的优化工具研究

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2024-04-22 DOI:10.1016/j.irbm.2024.100836
Nabil I. Ajali-Hernández , Carlos M. Travieso-González , Nayara Bermudo-Mora , Patricia Reino-Cacho , Sheila Rodríguez-Saucedo
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引用次数: 0

摘要

本研究通过提出一种新方法,解决了脑机接口(BCI)中用户特定偏差的难题。主要目标是采用混合深度学习模型,结合二维卷积神经网络(CNN)和长短期记忆(LSTM)层,分析脑电信号并对想象任务进行分类。研究的总体目标是创建一个适用于更广泛人群的通用模型,并减少用户特定的偏差。这是因为除 BCI 竞赛外,还需要使用其他数据库。研究人员提出了一个模拟头部电极排列的阵列模型,利用 CNN 捕捉空间信息,并利用 LSTM 算法捕捉时间信息,然后进行信号分类。通过设置误差检测阈值,剔除了任务亲和力低的受试者,从而显著提高了分类准确率,最高可达 21.34%。结论所提出的方法提供了一种在不同用户数据上训练的通用系统,能有效捕捉空间和时间脑电信号特征,为生物识别领域做出了重大贡献。本研究强调了混合模型在推进生物识别(BCI)方面的价值,突出了其在提高人机交互可靠性和准确性方面的潜力。研究还建议探索其他高级层,如变压器,以进一步增强所提出的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Study of an Optimization Tool Avoided Bias for Brain-Computer Interfaces Using a Hybrid Deep Learning Model

Study of an Optimization Tool Avoided Bias for Brain-Computer Interfaces Using a Hybrid Deep Learning Model

Objective

This study addresses the challenge of user-specific bias in Brain-Computer Interfaces (BCIs) by proposing a novel methodology. The primary objective is to employ a hybrid deep learning model, combining 2D Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers, to analyze EEG signals and classify imagined tasks. The overarching goal is to create a generalized model that is applicable to a broader population and mitigates user-specific biases.

Materials and Methods

EEG signals from imagined motor tasks in the public dataset Physionet form the basis of the study. This is due to the need to use other databases in addition to the BCI competition. A model of arrays emulating the electrode arrangement in the head is proposed to capture spatial information using CNN, and LSTM algorithms are used to capture temporal information, followed by signal classification.

Results

The hybrid model is implemented to achieve a high classification rate, reaching up to 90% for specific users and averaging 74.54%. Error detection thresholds are set to eliminate subjects with low task affinity, resulting in a significant improvement in classification accuracy of up to 21.34%.

Conclusion

The proposed methodology makes a significant contribution to the BCI field by providing a generalized system trained on diverse user data that effectively captures spatial and temporal EEG signal features. This study emphasizes the value of the hybrid model in advancing BCIs, highlighting its potential for improved reliability and accuracy in human-computer interaction. It also suggests the exploration of additional advanced layers, such as transformers, to further enhance the proposed methodology.

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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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