000225393 001__ 225393
000225393 005__ 20190317000630.0
000225393 0247_ $$2doi$$a10.1186/s12984-017-0219-0
000225393 022__ $$a1743-0003
000225393 02470 $$2ISI$$a000394720300001
000225393 037__ $$aARTICLE
000225393 245__ $$aClassification of upper limb center-out reaching tasks by means of EEG-based continuous decoding techniques
000225393 269__ $$a2017
000225393 260__ $$bBioMed Central$$c2017$$aLondon
000225393 300__ $$a14
000225393 336__ $$aJournal Articles
000225393 520__ $$aBackground: One of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device. Recent research work states that it is possible to do so based on low frequency EEG components. However, the validity of these results is still a matter of discussion. In this paper, we assess the feasibility of decoding upper limb kinematics from EEG signals in center-out reaching tasks during passive and active movements. Methods: The decoding of arm movement was performed using a multidimensional linear regression. Passive movements were analyzed using the same methodology to study the influence of proprioceptive sensory feedback in the decoding. Finally, we evaluated the possible advantages of classifying reaching targets, instead of continuous trajectories. Results: The results showed that arm movement decoding was significantly above chance levels. The results also indicated that EEG slow cortical potentials carry significant information to decode active center-out movements. The classification of reached targets allowed obtaining the same conclusions with a very high accuracy. Additionally, the low decoding performance obtained from passive movements suggests that discriminant modulations of low-frequency neural activity are mainly related to the execution of movement while proprioceptive feedback is not sufficient to decode upper limb kinematics. Conclusions: This paper contributes to the assessment of feasibility of using linear regression methods to decode upper limb kinematics from EEG signals. From our findings, it can be concluded that low frequency bands concentrate most of the information extracted from upper limb kinematics decoding and that decoding performance of active movements is above chance levels and mainly related to the activation of cortical motor areas. We also show that the classification of reached targets from decoding approaches may be a more suitable real-time methodology than a direct decoding of hand position.
000225393 6531_ $$aBrain-computer interface
000225393 6531_ $$aElectroencephalography
000225393 6531_ $$aLinear decoding
000225393 6531_ $$aUpper limb movements
000225393 6531_ $$aCenter-out reaching tasks
000225393 700__ $$aUbeda, Andrés
000225393 700__ $$aAzorín, José M.
000225393 700__ $$0241256$$g137762$$aChavarriaga, Ricardo
000225393 700__ $$aMillán, José del R.$$0240030$$g149175
000225393 773__ $$j14$$tJournal of NeuroEngineering and Rehabilitation$$k9
000225393 8564_ $$uhttp://rdcu.be/oW22$$zURL
000225393 8564_ $$uhttps://infoscience.epfl.ch/record/225393/files/UbedaAzChMi17.pdf$$zn/a$$s2700279$$yn/a
000225393 909C0 $$xU12367$$0252409$$pNCCR-ROBOTICS
000225393 909C0 $$pCNBI$$xU12103$$0252018
000225393 909CO $$qGLOBAL_SET$$pSTI$$particle$$ooai:infoscience.tind.io:225393
000225393 917Z8 $$x137762
000225393 917Z8 $$x137762
000225393 917Z8 $$x137762
000225393 937__ $$aEPFL-ARTICLE-225393
000225393 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000225393 980__ $$aARTICLE