当前位置: X-MOL 学术Neuroinformatics › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Signaleeg
Neuroinformatics ( IF 3 ) Pub Date : 2021-01-21 , DOI: 10.1007/s12021-020-09507-2
Joaquim Massana 1 , Òscar Raya 1 , Jaume Gauchola 1 , Beatriz López 1
Affiliation  

Due to the proliferation of brain and neurological disorders (World Health Organization 2006), EEG (Blinowska and Durka 2006) is gaining attention as a support for decision making in the fields of neurology, psychology, and psychiatry. But EEG data are not always easy to understand. Therefore, extracting the desired information from EEG data in different contexts is an important requirement. This article analyses state-of-the-art EEG signal processing tools and proposes a new one: Signaleeg. This addresses the limitations of previous tools. It has been designed with the aim of helping users to build predictive models from EEG signals in a process that is called signal-data mining (DM). Moreover, Signaleeg is user friendly and multi-threaded, with optimisation facilities for finding the best predictive model. It has been implemented and tested in three scenarios: schizophrenia diagnosis, alcoholism detection, and emotion recognition. The tool provided good results in each case, thus demonstrating its versatility.



中文翻译:

信号

由于大脑和神经系统疾病的扩散(世界卫生组织 2006 年),脑电图(Blinowska 和 Durka 2006 年)作为对神经病学、心理学和精神病学领域决策的支持而受到关注。但脑电图数据并不总是很容易理解。因此,从不同上下文的脑电数据中提取所需的信息是一个重要的要求。本文分析了最先进的 EEG 信号处理工具,并提出了一个新工具:Signaleeg。这解决了以前工具的局限性。它旨在帮助用户在称为信号数据挖掘 (DM) 的过程中根据 EEG 信号构建预测模型。此外,Signaleeg 是用户友好和多线程的,具有用于寻找最佳预测模型的优化工具。它已在三个场景中实施和测试:精神分裂症诊断、酒精中毒检测和情绪识别。该工具在每种情况下都提供了良好的结果,从而证明了其多功能性。

更新日期:2021-01-22
down
wechat
bug