Mental workload during brain-computer interface training

被引:38
|
作者
Felton, Elizabeth A. [2 ]
Williams, Justin C. [1 ]
Vanderheiden, Gregg C. [1 ]
Radwin, Robert G. [1 ]
机构
[1] Univ Wisconsin, Dept Biomed Engn, Madison, WI 53706 USA
[2] Johns Hopkins Univ, Sch Med, Dept Neurol, Baltimore, MD 21205 USA
关键词
brain-computer interface (BCI); mental workload; NASA Task Load Index; NASA-TLX; Fitts' law; electroencephalogram (EEG); DASHER;
D O I
10.1080/00140139.2012.662526
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
It is not well understood how people perceive the difficulty of performing brain-computer interface (BCI) tasks, which specific aspects of mental workload contribute the most, and whether there is a difference in perceived workload between participants who are able-bodied and disabled. This study evaluated mental workload using the NASA Task Load Index (TLX), a multi-dimensional rating procedure with six subscales: Mental Demands, Physical Demands, Temporal Demands, Performance, Effort, and Frustration. Able-bodied and motor disabled participants completed the survey after performing EEG-based BCI Fitts' law target acquisition and phrase spelling tasks. The NASA-TLX scores were similar for able-bodied and disabled participants. For example, overall workload scores (range 0-100) for 1D horizontal tasks were 48.5 (SD = 17.7) and 46.6 (SD 10.3), respectively. The TLX can be used to inform the design of BCIs that will have greater usability by evaluating subjective workload between BCI tasks, participant groups, and control modalities. Practitioner Summary: Mental workload of brain-computer interfaces (BCI) can be evaluated with the NASA Task Load Index (TLX). The TLX is an effective tool for comparing subjective workload between BCI tasks, participant groups (able-bodied and disabled), and control modalities. The data can inform the design of BCIs that will have greater usability.
引用
收藏
页码:526 / 537
页数:12
相关论文
共 50 条
  • [1] The effects of varying levels of mental workload on motor imagery based brain-computer interface
    Gu, Bin
    Chen, Long
    Ke, Yufeng
    Zhou, Yijie
    Yu, Haiqing
    Wang, Kun
    Ming, Dong
    INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS, 2020, 12 (03) : 315 - 323
  • [2] Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain-Computer Interface
    Bagheri, Mahsa
    Power, Sarah D.
    SENSORS, 2022, 22 (02)
  • [3] Exploration of User's Mental State Changes during Performing Brain-Computer Interface
    Ko, Li-Wei
    Chikara, Rupesh Kumar
    Lee, Yi-Chieh
    Lin, Wen-Chieh
    SENSORS, 2020, 20 (11) : 1 - 18
  • [4] Phase synchronization for the recognition of mental tasks in a brain-computer interface
    Gysels, E
    Celka, P
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2004, 12 (04) : 406 - 415
  • [5] An Auditory Brain-Computer Interface Using Active Mental Response
    Guo, Jing
    Gao, Shangkai
    Hong, Bo
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2010, 18 (03) : 230 - 235
  • [6] Brain-Computer Interface Based on Explicit and Implicit Mental Process
    Kanoh, Shin'ichiro
    2013 INTERNATIONAL JOINT CONFERENCE ON AWARENESS SCIENCE AND TECHNOLOGY & UBI-MEDIA COMPUTING (ICAST-UMEDIA), 2013, : 308 - 309
  • [7] An EEG-based brain-computer interface for gait training
    Liu, Dong
    Chen, Weihai
    Lee, Kyuhwa
    Pei, Zhongcai
    Millan, Jose del R.
    2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2017, : 6755 - 6760
  • [8] A Brain-Computer Interface Project Applied in Computer Engineering
    Katona, Jozsef
    Kovari, Attila
    IEEE TRANSACTIONS ON EDUCATION, 2016, 59 (04) : 319 - 326
  • [9] A passive brain-computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks
    Arico, P.
    Borghini, G.
    Di Flumeri, G.
    Colosimo, A.
    Pozzi, S.
    Babiloni, F.
    BRAIN-COMPUTER INTERFACES: LAB EXPERIMENTS TO REAL-WORLD APPLICATIONS, 2016, 228 : 295 - 328
  • [10] Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain-Computer Interface
    Asgher, Umer
    Khalil, Khurram
    Khan, Muhammad Jawad
    Ahmad, Riaz
    Butt, Shahid Ikramullah
    Ayaz, Yasar
    Naseer, Noman
    Nazir, Salman
    FRONTIERS IN NEUROSCIENCE, 2020, 14