基于eeg -机电系统接口设计与实现的多模态社会数据分析

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Journal of Data and Information Quality Pub Date : 2023-05-15 DOI:10.1145/3597306
Cameron Aume, S. Pal, Alireza Jolfaei, S. Mukhopadhyay
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引用次数: 0

摘要

在脑机接口(bci)中,可以读取脑电图(EEG)信号的设备得到了广泛的应用。近年来,随着一些消费级脑电图设备的发展,脑机接口领域的普及程度有所提高,这些设备可以实时检测人类的认知状态,并提供反馈以提高人类的表现。为了了解脑机接口中脑电图的基本原理和重要方面,已经进行了一些先前的研究。然而,如何使用消费级脑电图设备有效地控制机电系统的重要问题却很少得到关注。在本文中,我们使用OpenBCI Cyton耳机和运行游戏的用户界面设计并实现了一个EEG BCI系统,以探索通过BCI EEG-机电系统界面简化人与机电系统之间交互的概念。大多模态社会数据(BMSD)分析可以应用于高频和大容量的脑电图数据,使我们能够探索数据采集、数据处理和数据验证的各个方面,并评估我们系统的体验质量(QoE)。我们让现实世界的参与者玩一个游戏来收集训练数据,这些数据后来被放入多个机器学习模型中,包括线性判别分析(LDA)、k近邻(KNN)和卷积神经网络(CNN)。在训练机器学习模型之后,进行了实验的验证阶段,参与者试图玩相同的游戏,但没有直接控制,利用机器学习模型的输出来确定游戏的移动方式。我们发现,经过特定用户训练的CNN能够控制游戏,并且在测试的机器学习模型中具有最高的激活精度,以及最高的用户评价QoE,这为我们提供了机电一体化系统未来实施的重要见解。
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Multimodal Social Data Analytics on the Design and Implementation of an EEG-Mechatronic System Interface
The devices that can read Electroencephalography (EEG) signals have been widely used for Brain-Computer Interfaces (BCIs). Popularity in the field of BCIs has increased in recent years with the development of several consumer-grade EEG devices that can detect human cognitive states in real-time and deliver feedback to enhance human performance. Several previous studies have been conducted to understand the fundamentals and essential aspects of EEG in BCIs. However, the significant issue of how consumer-grade EEG devices can be used to control mechatronic systems effectively has been given less attention. In this article, we have designed and implemented an EEG BCI system using the OpenBCI Cyton headset and a user interface running a game to explore the concept of streamlining the interaction between humans and mechatronic systems with a BCI EEG-mechatronic system interface. Big Multimodal Social Data (BMSD) analytics can be applied to the high-frequency and high-volume EEG data, allowing us to explore aspects of data acquisition, data processing, and data validation and evaluate the Quality of Experience (QoE) of our system. We employ real-world participants to play a game to gather training data that was later put into multiple machine learning models, including a linear discriminant analysis (LDA), k-nearest neighbours (KNN), and a convolutional neural network (CNN). After training the machine learning models, a validation phase of the experiment took place where participants tried to play the same game but without direct control, utilising the outputs of the machine learning models to determine how the game moved. We find that a CNN trained to the specific user was able to control the game and performed with the highest activation accuracy from the machine learning models tested, along with the highest user-rated QoE, which gives us significant insight for future implementation with a mechatronic system.
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来源期刊
ACM Journal of Data and Information Quality
ACM Journal of Data and Information Quality COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
4.10
自引率
4.80%
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0
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