Combined EEG-fNIRS Decoding of Motor Attempt and Imagery for Brain Switch Control: An Offline Study in Patients With Tetraplegia

被引:49
作者
Blokland, Yvonne [1 ,2 ]
Spyrou, Loukianos [1 ,2 ]
Thijssen, Dick [3 ,4 ]
Eijsvogels, Thijs [3 ]
Colier, Willy [5 ]
Floor-Westerdijk, Marianne [5 ]
Vlek, Rutger [1 ]
Bruhn, Jorgen [1 ,2 ]
Farquhar, Jason [1 ]
机构
[1] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, NL-6500 GL Nijmegen, Netherlands
[2] Radboud Univ Nijmegen, Med Ctr, Dept Anaesthesiol Pain & Palliat Med, NL-6500 HB Nijmegen, Netherlands
[3] Radboud Univ Nijmegen, Med Ctr, Dept Physiol, NL-6500 HB Nijmegen, Netherlands
[4] Liverpool John Moores Univ, Liverpool L3 2AJ, Merseyside, England
[5] Artinis Med Syst BV, NL-6671 AS Zetten, Netherlands
关键词
Brain switch; electroencephalography (EEG); functional near-infrared spectroscopy (fNIRS); tetraplegia; CORTICAL ACTIVITY; SPECTROSCOPY;
D O I
10.1109/TNSRE.2013.2292995
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.
引用
收藏
页码:222 / 229
页数:8
相关论文
共 28 条
  • [1] Approximate is better than "exact" for interval estimation of binomial proportions
    Agresti, A
    Coull, BA
    [J]. AMERICAN STATISTICIAN, 1998, 52 (02) : 119 - 126
  • [2] Bishop C., 2006, PATTERN RECOGN, DOI DOI 10.1117/1.2819119
  • [3] The Berlin brain-computer interface:: EEG-based communication without subject training
    Blankertz, Benjamin
    Dornhege, Guido
    Krauledat, Matthias
    Mueller, Klaus-Robert
    Kunzmann, Volker
    Losch, Florian
    Curio, Gabriel
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2006, 14 (02) : 147 - 152
  • [4] On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces
    Coyle, S
    Ward, T
    Markham, C
    McDarby, G
    [J]. PHYSIOLOGICAL MEASUREMENT, 2004, 25 (04) : 815 - 822
  • [5] Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy
    Duncan, A
    Meek, JH
    Clemence, M
    Elwell, CE
    Fallon, P
    Tyszczuk, L
    Cope, M
    Delpy, DT
    [J]. PEDIATRIC RESEARCH, 1996, 39 (05) : 889 - 894
  • [6] Enhanced performance by a hybrid NIRS-EEG brain computer interface
    Fazli, Siamac
    Mehnert, Jan
    Steinbrink, Jens
    Curio, Gabriel
    Villringer, Arno
    Mueller, Klaus-Robert
    Blankertz, Benjamin
    [J]. NEUROIMAGE, 2012, 59 (01) : 519 - 529
  • [7] Neuronal ensemble control of prosthetic devices by a human with tetraplegia
    Hochberg, Leigh R.
    Serruya, Mijail D.
    Friehs, Gerhard M.
    Mukand, Jon A.
    Saleh, Maryam
    Caplan, Abraham H.
    Branner, Almut
    Chen, David
    Penn, Richard D.
    Donoghue, John P.
    [J]. NATURE, 2006, 442 (7099) : 164 - 171
  • [8] Kato YX, 2011, IEEE ENG MED BIO, P4629, DOI 10.1109/IEMBS.2011.6091146
  • [9] Kauhanen L., 2007, COMPUTAT INTELL NEUR
  • [10] Logothetis NK, 2003, J NEUROSCI, V23, P3963