Please wait a minute...

Frontiers of Mechanical Engineering

Front Mech Eng    2011, Vol. 6 Issue (1) : 71-81     https://doi.org/10.1007/s11465-011-0207-1
RESEARCH ARTICLE |
EEG controlled neuromuscular electrical stimulation of the upper limb for stroke patients
Hock Guan TAN1,3, Cheng Yap SHEE1, Keng He KONG2, Cuntai GUAN3, Wei Tech ANG1()
1. School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore, Singapore; 2. Department of Rehabilitation Medicine, Tan Tock Seng Hospital, 17 Ang Mo Kio Avenue 9, Singapore, Singapore; 3. Institute for Infocomm Research, 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore, Singapore
Download: PDF(344 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

This paper describes the Brain Computer Interface (BCI) system and the experiments to allow post-acute (<3 months) stroke patients to use electroencephalogram (EEG) to trigger neuromuscular electrical stimulation (NMES)-assisted extension of the wrist/fingers, which are essential pre-requisites for useful hand function. EEG was recorded while subjects performed motor imagery of their paretic limb, and then analyzed to determine the optimal frequency range within the mu-rhythm, with the greatest attenuation. Aided by visual feedback, subjects then trained to regulate their mu-rhythm EEG to operate the BCI to trigger NMES of the wrist/finger. 6 post-acute stroke patients successfully completed the training, with 4 able to learn to control and use the BCI to initiate NMES. This result is consistent with the reported BCI literacy rate of healthy subjects. Thereafter, without the loss of generality, the controller of the NMES is developed and is based on a model of the upper limb muscle (biceps/triceps) groups to determine the intensity of NMES required to flex or extend the forearm by a specific angle. The muscle model is based on a phenomenological approach, with parameters that are easily measured and conveniently implemented.

Keywords brain computer interface      neuromuscular electrical stimulation      stroke      musculoskeletal modeling     
Corresponding Authors: ANG Wei Tech,Email:cyshee@ntu.edu.sg   
Issue Date: 05 March 2011
 Cite this article:   
Hock Guan TAN,Cheng Yap SHEE,Keng He KONG, et al. EEG controlled neuromuscular electrical stimulation of the upper limb for stroke patients[J]. Front Mech Eng, 2011, 6(1): 71-81.
 URL:  
http://journal.hep.com.cn/fme/EN/10.1007/s11465-011-0207-1
http://journal.hep.com.cn/fme/EN/Y2011/V6/I1/71
Fig.1  Components of the BCI-NMES neuroprosthesis and interactions with the user
Fig.2  A post-acute stroke patient with a paretic right wrist using the BCI to trigger NMES on the wrist extensor muscles
Fig.3  EEG electrodes over the motor cortex
Fig.4  Average amplitude of each frequency of the -rhythm over a 5-s interval, while the subject rested or performed hand movement. The 8–12 Hz band displayed a noticeable attenuation of the signal during hand movement
No.Age/SexDays after strokeNature of strokeLocation of strokeAffected hand / MRC scoreUsed BCI?
161/M56InfarctPonsLeft/2N
254/M66InfarctFrontoparietal lobeLeft/3Y
348/M12HemorrhageBasal gangliaLeft/4Y
464/M10InfarctCorona radiataLeft/1N
573/M34InfarctCorona radiata &amp; lentiform nucleusLeft/0Y
648/F12InfarctPonsLeft/4N
769/F26InfarctCorona radiata &amp; lentiform nucleusRight/0N
856/M8InfarctFrontoparietal lobe &amp; corona radiataLeft/0N
951/M59InfarctFrontoparietal lobe &amp; corona radiataRight/2Y
Tab.1  Demographic and clinical characteristics of subjects ( = 9)
Mass &amp; inertial properties? Mass and length of forearm and hand? Mass moment of inertia about elbow joint
Active aharacteristics ? Activation level under isometric muscle contraction? Muscle force and power: moment-angle characteristic, moment-velocity relationship, maximum isometric moment, active muscle moment
Passive characteristics ? Viscous effects: viscous damping coefficient, passive moment? Elastic effects: passive elastic moment
Tab.2  Summary of the muscle model properties to be determined for use in controlling NMES
Fig.5  Centres of mass and radii of gyration of each segment of the forearm
Fig.6  Passive elastic moment vs elbow angle (passive elastic moment is negligible from 20° to 100°)
Fig.7  Setup to measure muscle activation level, force produced at the wrist is measured as stimulation pulse width is increases
Subject #1Subject #2
BicepsTricepsBicepsTriceps
mbody78 kg82 kg
Lforearm25 cm29 cm
Lhand20 cm22 cm
Inertia, J0.0554 kg·m20.0675 kg·m2
Thres120 μs100 μs223 μs183 μs
Sat263 μs178 μs316 μs315 μs
K155°45°81°91°
K249°55°84°79°
Mmax8.1 N·m5.1 N·m5.4 N·m5.0 N·m
Viscous coeff., V1.09 N·ms/rad1.02 N·ms/rad
Tab.3  Parameters for both subjects
Subject #1Subject #2
FlexionExtensionFlexionExtension
4.4°±4.0°3.4°±1.8°4.2°±3.4°5.0°±2.9°
Tab.4  Average angular deviation from target angle over 5 consecutive repetitions
1 Wade D T, Langton-Hewer R, Wood V A, Skilbeck C E, Ismail H M. The hemiplegic arm after stroke: measurement and recovery. Journal of Neurology, Neurosurgery, and Psychiatry , 1983, 46(6): 521–524
doi: 10.1136/jnnp.46.6.521 pmid:6875585
2 Sunderland A, Tinson D J, Bradley L, Hewer R L. Arm function after stroke. An evaluation of grip strength as a measure of recovery and a prognostic indicator. Journal of Neurology, Neurosurgery, and Psychiatry , 1989, 52(11): 1267–1272
doi: 10.1136/jnnp.52.11.1267 pmid:2592969
3 Pfurtscheller G, Neuper C, Guger C, Harkam W, Ramoser H, Schl?gl A, Obermaier B, Pregenzer M. Current trends in Graz Brain-Computer Interface (BCI) research. IEEE Transactions on Rehabilitation Engineering , 2000, 8(2): 216–219
doi: 10.1109/86.847821 pmid:10896192
4 Buch E, Weber C, Cohen L G, Braun C, Dimyan M A, Ard T, Mellinger J, Caria A, Soekadar S, Fourkas A, Birbaumer N. Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke , 2008, 39(3): 910–917
doi: 10.1161/STROKEAHA.107.505313 pmid:18258825
5 Daly J J, Cheng R, Hrovat K, Litinas K, McCabe J P, Rogers J M, Dohring M E.Feasibility and accuracy of EEG-BCI system control during imposed upper limb motor tasks and relax conditions by stroke survivors. Society for Neuroscience, Abstract 712.9
6 Sheffler L R, Chae J. Neuromuscular electrical stimulation in neurorehabilitation. Muscle &amp; Nerve , 2007, 35(5): 562–590
doi: 10.1002/mus.20758 pmid:17299744
7 Jasper H H. The ten-twenty electrode system of the international federation. Electroencephalography and Clinical Neurophysiology , 1958, 10: 371–375
8 Pfurtscheller G, Neuper C, Andrew C, Edlinger G. Foot and hand area mu rhythms. International Journal of Psychophysiology , 1997, 26(1–3): 121–135
doi: 10.1016/S0167-8760(97)00760-5 pmid:9202999
9 Veluvolu K C, Tan U X, Ang W T, Latt W T, Shee C Y. Bandlimited multiple fourier linear combiner for real-time tremor compensation. Proceedings of the 29th IEEE Engineering in Medicine and Biology Conference, Lyon, France , 2007, 2847–2850
10 Tan H G, Zhang H H, Wang C C, Shee C Y, Ang W T, Guan C T. Arm flexion and extension exercises using a brain-computer interface and functional electrical stimulation. Proceedings of the 6th IASTED International Conference on Biomedical Engineering, Innsbruck, Austria , 2008
11 Tan H G, Zhang H H, Wang C C, Shee C Y, Ang W T, Guan C T. A step towards discretized motion control of the upper limb using brain-computer interface and electrical stimulation. Proceedings of the 13th Annual International FES Society Conference, Freiburg, Germany , 2008
12 Pfurtscheller G, Neuper C. Motor imagery activates primary sensorimotor area in humans. Neuroscience Letters , 1997, 239(2–3): 65–68
doi: 10.1016/S0304-3940(97)00889-6 pmid:9469657
13 Keller T, Popovic M R, Pappas I P I, Müller P Y. Transcutaneous functional electrical stimulator “Compex Motion”. Artificial Organs , 2002, 26(3): 219–223
doi: 10.1046/j.1525-1594.2002.06934.x pmid:11940017
14 Thrasher T A, Zivanovic V, McIlroy W, Popovic M R. Rehabilitation of reaching and grasping function in severe hemiplegic patients using functional electrical stimulation therapy. Neurorehabilitation and Neural Repair , 2008, 22(6): 706–714
doi: 10.1177/1545968308317436 pmid:18971385
15 Shin H K, Cho S H, Jeon H S, Lee Y H, Song J C, Jang S H, Lee C H, Kwon Y H. Cortical effect and functional recovery by the electromyography-triggered neuromuscular stimulation in chronic stroke patients. Neuroscience Letters , 2008, 442(3): 174–179
doi: 10.1016/j.neulet.2008.07.026 pmid:18644424
16 Chae J, Sheffler L, Knutson J. Neuromuscular electrical stimulation for motor restoration in hemiplegia. Topics in Stroke Rehabilitation , 2008, 15(5): 412–426
doi: 10.1310/tsr1505-412 pmid:19008202
17 Nijholt A, Tan D, Pfurtscheller G, Brunner C, Mill J, Allison B, Graimann B, Popescu F, Blankertz B, M K R. Trends &amp; controversies: brain-computer interfacing for intelligent systems. IEEE Intelligent Systems , 2008, 23(3): 72–79
doi: 10.1109/MIS.2008.41
18 Sharma N, Pomeroy V M, Baron J C. Motor imagery: a backdoor to the motor system after stroke? Stroke , 2006, 37(7): 1941–1952
doi: 10.1161/01.STR.0000226902.43357.fc pmid:16741183
19 Müller K, Bütefisch C M, Seitz R J, H?mberg V. Mental practice improves hand function after hemiparetic stroke. Restorative Neurology and Neuroscience , 2007, 25(5–6): 501–511
pmid:18334768
20 Hill A V. The heat of shortening and the dynamic constants of muscle. Proceedings of the Royal Society of London. Series B. Biological Sciences , 1938, 126(843): 136–195
doi: 10.1098/rspb.1938.0050
21 Huxley A F. Muscle structure and theories of contraction. Progress in Biophysics and Biophysical Chemistry , 1957, 7: 255–318
pmid:13485191
22 Winters J M, Stark L. Muscle models: what is gained and what is lost by varying model complexity. Biological Cybernetics , 1987, 55(6): 403–420
doi: 10.1007/BF00318375 pmid:3567243
23 Ferrarin M, Palazzo F, Riener R, Quintern J. Model-based control of FES-induced single joint movements. IEEE Transactions on Neural Systems and Rehabilitation Engineering , 2001, 9(3): 245–257
doi: 10.1109/7333.948452 pmid:11561660
24 Zatsiorsky V M, Seluyanov V. The mass and inertia characteristics of the main segments of human body. In Biomechanics VIII-B, Matsui H &amp; Kobayashi K, (Eds.). Champaign, IL: Human Kinetics, 1983, 1152–1159
25 Hatze H. Myocybernetic control models of skeletal muscle: Characteristics and applications [dissertation]. University of South Africa, Pretoria , 1981
26 Zajac F E. Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Critical Reviews in Biomedical Engineering , 1989, 17(4): 359–411
pmid:2676342
27 Riener R, Ferrarin M, Pavan E E, Frigo C A. Patient-driven control of FES-supported standing up and sitting down: experimental results. IEEE Transactions on Rehabilitation Engineering , 2000, 8(4): 523–529
doi: 10.1109/86.895956 pmid:11204044
28 Ferrarin M, Iacuone P, Mingrino A, Frigo C, Pedotti A. A dynamic model of electrically activated knee muscles in healthy and paraplegics. In Neuroprosthetics from Basic Research to Clinical Applications . Pedotti A, Ferrarin M, Riener R, Quintern J (Eds.), Berlin, Germany: Springer-Verlag, 1996, 81–90
29 Garner B A, Pandy M G. Estimation of musculotendon properties in the human upper limb. Annals of Biomedical Engineering , 2003, 31(2): 207–220
doi: 10.1114/1.1540105 pmid:12627828
30 Stein R B, Zehr E P, Lebiedowska M K, Popovi? D B, Scheiner A, Chizeck H J. Estimating mechanical parameters of leg segments in individuals with and without physical disabilities. IEEE Transactions on Rehabilitation Engineering , 1996, 4(3): 201–211
doi: 10.1109/86.536776 pmid:8800224
31 Riener R, Edrich T. Identification of passive elastic joint moments in the lower extremities. Journal of Biomechanics , 1999, 32(5): 539–544
doi: 10.1016/S0021-9290(99)00009-3 pmid:10327008
32 Edrich T, Riener R, Quintern J. Analysis of passive elastic joint moments in paraplegics. IEEE Transactions on Bio-Medical Engineering , 2000, 47(8): 1058–1065
doi: 10.1109/10.855933 pmid:10943054
Viewed
Full text


Abstract

Cited

  Shared   0
  Discussed