Abstract
Robotic prostheses are expected to allow amputees greater freedom and mobility. However, available options to control transhumeral prostheses are reduced with increasing amputation level. In addition, for electromyography-based control of prostheses, the residual muscles alone cannot generate sufficiently different signals for accurate distal arm function. Thus, controlling a multi-degree of freedom (DoF) transhumeral prosthesis is challenging with currently available techniques. In this paper, an electroencephalogram (EEG)-based hierarchical two-stage approach is proposed to achieve multi-DoF control of a transhumeral prosthesis. In the proposed method, the motion intention for arm reaching or hand lifting is identified using classifiers trained with motion-related EEG features. For this purpose, neural network and k-nearest neighbor classifiers are used. Then, elbow motion and hand endpoint motion is estimated using a different set of neural-network-based classifiers, which are trained with motion information recorded using healthy subjects. The predictions from the classifiers are compared with residual limb motion to generate a final prediction of motion intention. This can then be used to realize multi-DoF control of a prosthesis. The experimental results show the feasibility of the proposed method for multi-DoF control of a transhumeral prosthesis. This proof of concept study was performed with healthy subjects.
Highlights
Transhumeral prostheses are worn by upper elbow amputees to substitute for the loss of functions of the upper limb in performing activities of daily living
In this paper, a new approach was proposed to control a multi-degree of freedom (DoF) transhumeral prosthesis taking into account the motion intention of the user based on EEG signals
It consists of three major steps: EEG-based motion intention identification, collection of motion information from healthy subjects to create a database, and estimation of the motion of the prosthesis based on residual limb motion
Summary
Transhumeral prostheses are worn by upper elbow amputees to substitute for the loss of functions of the upper limb in performing activities of daily living. Some studies [11,12,13] report EMG-based motion intention studies for much lower level amputation such as transradial or wrist disarticulation Despite these advances, there is still a gap to be filled in controlling simultaneous movements in multi-DoF transhumeral prostheses. We propose a new hierarchical approach to control a multi-DoF transhumeral prosthesis using EEG signals in combination with residual upper limb motion. TTThhheee mmmaaaiiinnn sssttteeepppsss aaannnddd ttthhheee sssiiigggnnnaaalll flffllooowww ccchhhaaarrrttt ooofff ttthhheeeppprrrooopppooossseeeddd mmmeeettthhhooodddooolllooogggyyy fffooorrr mmmoootttiiiooonnn iiinnnttteeennntttiiiooonnn iiidddeeennntttiiififfiiccaaattiioonn aaarreee ssshhhooowwwnn iiinn FFFiiiggguuurrreee 222. NNeexxtt,, eexxttrraacctteedd ffeeaattuurreess aarree uusseedd tttoo tttrrraaaiiinn ttthhheee mmmoootttiiiooonnn iiinnnttteeennntttiiiooonnn ccclllaaassssssiiififfiieeerrr...FFFiiinnnaaalllllyyy,,,thtthheeeooouuutpttppuuut ttfrfforromommthttheheemmmotooiottiinoonniniitnnettneetnniottiinoonnclcaclslaasssifissiieffriieeirrs iicssoccmoopmmappreaadrreewddiwwthiittthhhetthhmeeommtioottniioosnntasstettaaotteef toohffetthhreesrrieedssuiiddaluulaailml lliibmmabbnaadnntddhetthhfieenffaiinnl aadlleddceeiscciiiossiniooninsiigsseggneeennreearrtaaettdee.ddI..nIInnthtthhisiissstssuttuudddyyythtthheeeeeefffeffeeccctittviivveeennneeessssssooofffaaannnNNNNNN---bbbaaassseeeddd ccclllaaassssssiiififfiieeerrr aaannnddd aaa kkk---nnneeeaaarrreeessstttnnneeeiiiggghhhbbbooorrrccclllaaassssssiiififfiieeerrraaarrreeeeeevvvaaallluuuaaattteeedddfffooorrrttthhheeemmmoootttiioioonnniininnttteeennntttiioioonnnccclallaasssssifiiiffeiieerr.r. FFiiggguuurrreee 222. After CAR correction, the data are ready for feature extraction
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