López-Larraz, EduardoBibián, CarlosBirbaumer, NielsRamos-Murguialday, AnderAjoudani, ArashArtemiadis, PanagiotisBeckerle, PhilippGrioli, GiorgioLambercy, OlivierMombaur, KatjaNovak, DomenRauter, GeorgRodriguez Guerrero, CarlosSalvietti, GionataAmirabdollahian, FarshidBalasubramanian, SivakumarCastellini, ClaudioDi Pino, GiovanniGuo, ZhaoHughes, CharmayneIida, FumiyaLenzi, TommasoRuffaldi, EmanueleSergi, FabrizioSoh, Gim SongCaimmi, MarcoCappello, LeonardoCarloni, RaffaellaCarlson, TomCasadio, MauraCoscia, MartinaDe Santis, DaliaForner-Cordero, ArturoHoward, MatthewPiovesan, DavideSiqueira, AdrianoSup, FrankLorenzo, MasiaCatalano, Manuel GiuseppeLee, HyunglaeMenon, CarloRaspopovic, StanisaRastgaar, MoRonsse, Renaudvan Asseldonk, EdwinVanderborght, BramVenkadesan, MadhusudhanBianchi, MatteoBraun, DavidGodfrey, Sasha BlueMastrogiovanni, FulvioMcDaid, AndrewRossi, StefanoZenzeri, JacopoFormica, DomenicoKaravas, NikolaosMarchal-Crespo, LauraReed, Kyle B.Tagliamonte, Nevio LuigiBurdet, EtienneBasteris, AngeloCampolo, DomenicoDeshpande, AshishDubey, VenketeshHussain, AsifSanguineti, VittorioUnal, RamazanCaurin, Glauco Augusto de PaulaKoike, YasuharuMazzoleni, StefanoPark, Hyung-SoonRemy, C. DavidSaint-Bauzel, LudovicTsagarakis, NikosVeneman, JanZhang, Wenlong2024-07-242024-07-242017-08-11López-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.2017.8009363conference97815386229641945-7898https://hdl.handle.net/11556/2184Publisher Copyright: © 2017 IEEE.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.6enginfo:eu-repo/semantics/restrictedAccessconference output10.1109/ICORR.2017.8009363Control and Systems EngineeringRehabilitationElectrical and Electronic EngineeringSDG 3 - Good Health and Well-beinghttp://www.scopus.com/inward/record.url?scp=85032178984&partnerID=8YFLogxK