Visual and haptic feedback in detecting motor imagery within a wearable brain-computer interface

被引:10
|
作者
Arpaia, Pasquale [1 ,2 ,3 ]
Coyle, Damien [4 ]
Donnarumma, Francesco [1 ,5 ]
Esposito, Antonio [1 ,6 ]
Natalizio, Angela [1 ,6 ]
Parvis, Marco [6 ]
机构
[1] Augmented Real Hlth Monitoring Lab ARHeMLab, Naples, Italy
[2] Univ Napoli Federico II, Dept Elect Engn & Informat Technol DIETI, Naples, Italy
[3] Univ Napoli Federico II, Ctr Interdipartimentale Ric Management Sanit & Inn, Naples, Italy
[4] Univ Ulster, Intelligent Syst Res Ctr, Derry, North Ireland
[5] Natl Res Council ISTC CNR, Inst Cognit Sci & Technol, Rome, Italy
[6] Politecn Torino, Dept Elect & Telecommun DET, Turin, Italy
基金
英国工程与自然科学研究理事会;
关键词
Brain-computer interface; Motor imagery; Electroencephalography; Extended reality; Haptic; Neurofeedback;
D O I
10.1016/j.measurement.2022.112304
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper presents a wearable brain-computer interface relying on neurofeedback in extended reality for the enhancement of motor imagery training. Visual and vibrotactile feedback modalities were evaluated when presented either singularly or simultaneously. Only three acquisition channels and state-of-the-art vibrotactile chest-based feedback were employed. Experimental validation was carried out with eight subjects participating in two or three sessions on different days, with 360 trials per subject per session. Neurofeedback led to statistically significant improvement in performance over the two/three sessions, thus demonstrating for the first time functionality of a motor imagery-based instrument even by using an utmost wearable electroencephalograph and a commercial gaming vibrotactile suit. In the best cases, classification accuracy exceeded 80% with more than 20% improvement with respect to the initial performance. No feedback modality was generally preferable across the cohort study, but it is concluded that the best feedback modality may be subject-dependent.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] Multimodal feedback in assisting a wearable brain-computer interface based on motor imagery
    Arpaia, Pasquale
    Coyle, Damien
    Donnarumma, Francesco
    Esposito, Antonio
    Natalizio, Angela
    Parvis, Marco
    Pesola, Marisa
    Vallefuoco, Ersilia
    2022 IEEE INTERNATIONAL CONFERENCE ON METROLOGY FOR EXTENDED REALITY, ARTIFICIAL INTELLIGENCE AND NEURAL ENGINEERING (METROXRAINE), 2022, : 691 - 696
  • [2] Examination of effectiveness of kinaesthetic haptic feedback for motor imagery-based brain-computer interface training
    Sakamaki, Isao
    Tavakoli, Mahdi
    Wiebe, Sandra
    Adams, Kim
    BRAIN-COMPUTER INTERFACES, 2023, 10 (01) : 16 - 37
  • [3] Development of a Wearable Motor-Imagery-Based Brain-Computer Interface
    Lin, Bor-Shing
    Pan, Jeng-Shyang
    Chu, Tso-Yao
    Lin, Bor-Shyh
    JOURNAL OF MEDICAL SYSTEMS, 2016, 40 (03) : 1 - 8
  • [4] The effects of visual distractors on cognitive load in a motor imagery brain-computer interface
    Emami, Zahra
    Chau, Tom
    BEHAVIOURAL BRAIN RESEARCH, 2020, 378
  • [5] Investigating the effects of visual distractors on the performance of a motor imagery brain-computer interface
    Emami, Zahra
    Chau, Tom
    CLINICAL NEUROPHYSIOLOGY, 2018, 129 (06) : 1268 - 1275
  • [6] A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection
    Lo, Chi-Chun
    Chien, Tsung-Yi
    Chen, Yu-Chun
    Tsai, Shang-Ho
    Fang, Wai-Chi
    Lin, Bor-Shyh
    SENSORS, 2016, 16 (02):
  • [7] A Gait Imagery-Based Brain-Computer Interface With Visual Feedback for Spinal Cord Injury Rehabilitation on Lokomat
    Blanco-Diaz, Cristian Felipe
    Serafini, Ericka Raiane da Silva
    Bastos-Filho, Teodiano
    Dantas, Andre Felipe Oliveira de Azevedo
    Santo, Caroline Cunha do Espirito
    Delisle-Rodriguez, Denis
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2025, 72 (01) : 102 - 111
  • [8] A foot motor imagery brain-computer interface with realistic visual feedback: preliminary evaluation in healthy and stroke subjects
    Carrere L.C.
    Escher L.G.
    Gentiletti G.G.
    Tabernig C.B.
    Research on Biomedical Engineering, 2021, 37 (04) : 595 - 604
  • [9] EEG datasets for motor imagery brain-computer interface
    Cho, Hohyun
    Ahn, Minkyu
    Ahn, Sangtae
    Kwon, Moonyoung
    Jun, Sung Chan
    GIGASCIENCE, 2017, 6 (07): : 1 - 8
  • [10] A Motor Imagery Based Brain-Computer Interface Speller
    Xia, Bin
    Yang, Jing
    Cheng, Conghui
    Xie, Hong
    ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT II, 2013, 7903 : 413 - 421