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Multimodal Social Data Analytics on the Design and Implementation of an EEG-Mechatronic System Interface

Published:28 September 2023Publication History
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Abstract

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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      cover image Journal of Data and Information Quality
      Journal of Data and Information Quality  Volume 15, Issue 3
      September 2023
      326 pages
      ISSN:1936-1955
      EISSN:1936-1963
      DOI:10.1145/3611329
      Issue’s Table of Contents

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      Publication History

      • Published: 28 September 2023
      • Online AM: 15 May 2023
      • Accepted: 21 March 2023
      • Revised: 12 March 2023
      • Received: 14 November 2022
      Published in jdiq Volume 15, Issue 3

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