脑机接口:趋势、挑战和威胁。

Q1 Computer Science Brain Informatics Pub Date : 2023-08-04 DOI:10.1186/s40708-023-00199-3
Baraka Maiseli, Abdi T Abdalla, Libe V Massawe, Mercy Mbise, Khadija Mkocha, Nassor Ally Nassor, Moses Ismail, James Michael, Samwel Kimambo
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引用次数: 1

摘要

脑机接口(BCI)是一种促进脑机通信的新兴技术,近年来引起了人们的广泛研究。研究人员提供的实验结果表明,脑机接口可以恢复残疾人的能力,从而提高他们的生活质量。脑机接口已经彻底改变并积极影响了许多行业,包括娱乐和游戏、自动化和控制、教育、神经营销和神经人体工程学。尽管脑机接口的应用范围很广,但在文献中对其全球趋势的讨论仍然很少。了解这一趋势可以告诉研究人员和实践者该领域的方向,以及他们应该在哪里投入更多的努力。注意到这一意义,我们分析了来自Scopus的25,336篇BCI出版物的元数据,以确定该领域的进展。分析显示,从2019年起,中国的BCI出版物呈指数级增长,超过了同期开始下降的美国。讨论了这一趋势的含义和原因。此外,我们还广泛讨论了限制BCI功能开发的挑战和威胁。假设一个典型的BCI架构来解决两个突出的BCI威胁,隐私和安全,作为使该技术在社会上具有商业可行性的尝试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Brain-computer interface: trend, challenges, and threats.

Brain-computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onwards, exceeding those from the United States that started to decline during the same period. Implications and reasons for this trend are discussed. Furthermore, we have extensively discussed challenges and threats limiting exploitation of BCI capabilities. A typical BCI architecture is hypothesized to address two prominent BCI threats, privacy and security, as an attempt to make the technology commercially viable to the society.

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来源期刊
Brain Informatics
Brain Informatics Computer Science-Computer Science Applications
CiteScore
9.50
自引率
0.00%
发文量
27
审稿时长
13 weeks
期刊介绍: Brain Informatics is an international, peer-reviewed, interdisciplinary open-access journal published under the brand SpringerOpen, which provides a unique platform for researchers and practitioners to disseminate original research on computational and informatics technologies related to brain. This journal addresses the computational, cognitive, physiological, biological, physical, ecological and social perspectives of brain informatics. It also welcomes emerging information technologies and advanced neuro-imaging technologies, such as big data analytics and interactive knowledge discovery related to various large-scale brain studies and their applications. This journal will publish high-quality original research papers, brief reports and critical reviews in all theoretical, technological, clinical and interdisciplinary studies that make up the field of brain informatics and its applications in brain-machine intelligence, brain-inspired intelligent systems, mental health and brain disorders, etc. The scope of papers includes the following five tracks: Track 1: Cognitive and Computational Foundations of Brain Science Track 2: Human Information Processing Systems Track 3: Brain Big Data Analytics, Curation and Management Track 4: Informatics Paradigms for Brain and Mental Health Research Track 5: Brain-Machine Intelligence and Brain-Inspired Computing
期刊最新文献
Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline. Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer's disease. CalciumZero: a toolbox for fluorescence calcium imaging on iPSC derived brain organoids. Blockchain-enabled digital twin system for brain stroke prediction. A temporal-spectral graph convolutional neural network model for EEG emotion recognition within and across subjects.
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