RT Conference Proceedings
T1 Influence of artifacts on movement intention decoding from EEG activity in severely paralyzed stroke patients
A1 López-Larraz, Eduardo
A1 Bibián, Carlos
A1 Birbaumer, Niels
A1 Ramos-Murguialday, Ander
A2 Ajoudani, Arash
A2 Artemiadis, Panagiotis
A2 Beckerle, Philipp
A2 Grioli, Giorgio
A2 Lambercy, Olivier
A2 Mombaur, Katja
A2 Novak, Domen
A2 Rauter, Georg
A2 Rodriguez Guerrero, Carlos
A2 Salvietti, Gionata
A2 Amirabdollahian, Farshid
A2 Balasubramanian, Sivakumar
A2 Castellini, Claudio
A2 Di Pino, Giovanni
A2 Guo, Zhao
A2 Hughes, Charmayne
A2 Iida, Fumiya
A2 Lenzi, Tommaso
A2 Ruffaldi, Emanuele
A2 Sergi, Fabrizio
A2 Soh, Gim Song
A2 Caimmi, Marco
A2 Cappello, Leonardo
A2 Carloni, Raffaella
A2 Carlson, Tom
A2 Casadio, Maura
A2 Coscia, Martina
A2 De Santis, Dalia
A2 Forner-Cordero, Arturo
A2 Howard, Matthew
A2 Piovesan, Davide
A2 Siqueira, Adriano
A2 Sup, Frank
A2 Lorenzo, Masia
A2 Catalano, Manuel Giuseppe
A2 Lee, Hyunglae
A2 Menon, Carlo
A2 Raspopovic, Stanisa
A2 Rastgaar, Mo
A2 Ronsse, Renaud
A2 van Asseldonk, Edwin
A2 Vanderborght, Bram
A2 Venkadesan, Madhusudhan
A2 Bianchi, Matteo
A2 Braun, David
A2 Godfrey, Sasha Blue
A2 Mastrogiovanni, Fulvio
A2 McDaid, Andrew
A2 Rossi, Stefano
A2 Zenzeri, Jacopo
A2 Formica, Domenico
A2 Karavas, Nikolaos
A2 Marchal-Crespo, Laura
A2 Reed, Kyle B.
A2 Tagliamonte, Nevio Luigi
A2 Burdet, Etienne
A2 Basteris, Angelo
A2 Campolo, Domenico
A2 Deshpande, Ashish
A2 Dubey, Venketesh
A2 Hussain, Asif
A2 Sanguineti, Vittorio
A2 Unal, Ramazan
A2 Caurin, Glauco Augusto de Paula
A2 Koike, Yasuharu
A2 Mazzoleni, Stefano
A2 Park, Hyung-Soon
A2 Remy, C. David
A2 Saint-Bauzel, Ludovic
A2 Tsagarakis, Nikos
A2 Veneman, Jan
A2 Zhang, Wenlong
AB Brain-machine interfaces (BMI) can be used to control robotic and prosthetic devices for rehabilitation of motor disorders, such as stroke. The calibration of these BMI systems is of paramount importance in order to establish a precise contingent link between the brain activity related to movement intention and the peripheral feedback. However, electroencephalographic (EEG) activity, commonly used to build non-invasive BMIs, can be easily contaminated by artifacts of electrical or physiological origin. The way these interferences can affect the performance of movement intention decoders has not been deeply studied, especially when dealing with severely paralyzed patients, which often generate more artifacts by compensatory movements. This paper evaluates the effects of removing artifacts from the data used to train a BMI decoder on a dataset of 28 severely paralyzed stroke patients. We show that cleaning the training datasets reduces the global BMI performance for decoding attempts of movement. Further, we demonstrate that this performance drop especially affects the test trials contaminated by artifacts (i.e., trials that might not reflect cortical activity but noise), but not the clean test trials (i.e., trials representing correct cortical activity). This paper underlines the importance of cleaning the datasets used to train BMI systems to improve their efficacy for decoding movement intention and maximize their neurorehabilitative potential.
PB IEEE Computer Society
SN 9781538622964
SN 1945-7898
YR 2017
FD 2017-08-11
LK https://hdl.handle.net/11556/2184
UL https://hdl.handle.net/11556/2184
LA eng
NO López-Larraz , E , Bibián , C , Birbaumer , N & Ramos-Murguialday , A 2017 , Influence of artifacts on movement intention decoding from EEG activity in severely paralyzed stroke patients . in A Ajoudani , P Artemiadis , P Beckerle , G Grioli , O Lambercy , K Mombaur , D Novak , G Rauter , C Rodriguez Guerrero , G Salvietti , F Amirabdollahian , S Balasubramanian , C Castellini , G Di Pino , Z Guo , C Hughes , F Iida , T Lenzi , E Ruffaldi , F Sergi , G S Soh , M Caimmi , L Cappello , R Carloni , T Carlson , M Casadio , M Coscia , D De Santis , A Forner-Cordero , M Howard , D Piovesan , A Siqueira , F Sup , M Lorenzo , M G Catalano , H Lee , C Menon , S Raspopovic , M Rastgaar , R Ronsse , E van Asseldonk , B Vanderborght , M Venkadesan , M Bianchi , D Braun , S B Godfrey , F Mastrogiovanni , A McDaid , S Rossi , J Zenzeri , D Formica , N Karavas , L Marchal-Crespo , K B Reed , N L Tagliamonte , E Burdet , A Basteris , D Campolo , A Deshpande , V Dubey , A Hussain , V Sanguineti , R Unal , G A D P Caurin , Y Koike , S Mazzoleni , H-S Park , C D Remy , L Saint-Bauzel , N Tsagarakis , J Veneman & W Zhang (eds) , 2017 International Conference on Rehabilitation Robotics, ICORR 2017 . , 8009363 , IEEE International Conference on Rehabilitation Robotics , IEEE Computer Society , pp. 901-906 , 2017 International Conference on Rehabilitation Robotics, ICORR 2017 , London , United Kingdom , 17/07/17 . https://doi.org/10.1109/ICORR.2017.8009363
NO conference
NO Publisher Copyright: © 2017 IEEE.
NO This study was funded by the Baden-Württemberg Stiftung (GRUENS ROB-1), the Deutsche Forschungsgemeinschaft (DFG, Koselleck), the Fortüne-Program of the University of Tübingen (2422-0-0), and the Bundesministerium für Bil-dung und Forschung BMBF MOTORBIC (FKZ 13GW0053) and AMORSA (FKZ 16SV7754).
DS TECNALIA Publications
RD 26 jul 2024