Browsing by Author "Bibián, Carlos"
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Item Design of continuous EMG classification approaches towards the control of a robotic exoskeleton in reaching movements(IEEE Computer Society, 2017-08-11) Irastorza-Landa, Nerea; Sarasola-Sanz, Andrea; López-Larraz, Eduardo; Bibián, Carlos; Shiman, Parid; Birbaumer, Niels; Ramos-Murguialday, Ander; Ajoudani, Arash; Artemiadis, Panagiotis; Beckerle, Philipp; Grioli, Giorgio; Lambercy, Olivier; Mombaur, Katja; Novak, Domen; Rauter, Georg; Rodriguez Guerrero, Carlos; Salvietti, Gionata; Amirabdollahian, Farshid; Balasubramanian, Sivakumar; Castellini, Claudio; Di Pino, Giovanni; Guo, Zhao; Hughes, Charmayne; Iida, Fumiya; Lenzi, Tommaso; Ruffaldi, Emanuele; Sergi, Fabrizio; Soh, Gim Song; Caimmi, Marco; Cappello, Leonardo; Carloni, Raffaella; Carlson, Tom; Casadio, Maura; Coscia, Martina; De Santis, Dalia; Forner-Cordero, Arturo; Howard, Matthew; Piovesan, Davide; Siqueira, Adriano; Sup, Frank; Lorenzo, Masia; Catalano, Manuel Giuseppe; Lee, Hyunglae; Menon, Carlo; Raspopovic, Stanisa; Rastgaar, Mo; Ronsse, Renaud; van Asseldonk, Edwin; Vanderborght, Bram; Venkadesan, Madhusudhan; Bianchi, Matteo; Braun, David; Godfrey, Sasha Blue; Mastrogiovanni, Fulvio; McDaid, Andrew; Rossi, Stefano; Zenzeri, Jacopo; Formica, Domenico; Karavas, Nikolaos; Marchal-Crespo, Laura; Reed, Kyle B.; Tagliamonte, Nevio Luigi; Burdet, Etienne; Basteris, Angelo; Campolo, Domenico; Deshpande, Ashish; Dubey, Venketesh; Hussain, Asif; Sanguineti, Vittorio; Unal, Ramazan; Caurin, Glauco Augusto de Paula; Koike, Yasuharu; Mazzoleni, Stefano; Park, Hyung-Soon; Remy, C. David; Saint-Bauzel, Ludovic; Tsagarakis, Nikos; Veneman, Jan; Zhang, Wenlong; Medical TechnologiesMyoelectric control of rehabilitation devices engages active recruitment of muscles for motor task accomplishment, which has been proven to be essential in motor rehabilitation. Unfortunately, most electromyographic (EMG) activity-based controls are limited to one single degree-of-freedom (DoF), not permitting multi-joint functional tasks. On the other hand, discrete EMG-triggered approaches fail to provide continuous feedback about muscle recruitment during movement. For such purposes, myoelectric interfaces for continuous recognition of functional movements are necessary. Here we recorded EMG activity using 5 bipolar electrodes placed on the upper-arm in 8 healthy participants while they performed reaching movements in 8 different directions. A pseudo on-line system was developed to continuously predict movement intention and attempted arm direction. We evaluated two hierarchical classification approaches. Movement intention detection triggered different movement direction classifiers (4 or 8 classes) that were trained and tested over a 5-fold cross validation. We also investigated the effect of 3 different window lengths to extract EMG features on classification. We obtained classification accuracies above 70% for both hierarchical approaches. These results highlight the viability of classifying online 8 upper-arm different directions using surface EMG activity of 5 muscles and represent a first step towards an online EMG-based control for rehabilitation devices.Item A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients(IEEE Computer Society, 2017-08-11) Sarasola-Sanz, Andrea; Irastorza-Landa, Nerea; López-Larraz, Eduardo; Bibián, Carlos; Helmhold, Florian; Broetz, Doris; Birbaumer, Niels; Ramos-Murguialday, Ander; Ajoudani, Arash; Artemiadis, Panagiotis; Beckerle, Philipp; Grioli, Giorgio; Lambercy, Olivier; Mombaur, Katja; Novak, Domen; Rauter, Georg; Rodriguez Guerrero, Carlos; Salvietti, Gionata; Amirabdollahian, Farshid; Balasubramanian, Sivakumar; Castellini, Claudio; Di Pino, Giovanni; Guo, Zhao; Hughes, Charmayne; Iida, Fumiya; Lenzi, Tommaso; Ruffaldi, Emanuele; Sergi, Fabrizio; Soh, Gim Song; Caimmi, Marco; Cappello, Leonardo; Carloni, Raffaella; Carlson, Tom; Casadio, Maura; Coscia, Martina; De Santis, Dalia; Forner-Cordero, Arturo; Howard, Matthew; Piovesan, Davide; Siqueira, Adriano; Sup, Frank; Lorenzo, Masia; Catalano, Manuel Giuseppe; Lee, Hyunglae; Menon, Carlo; Raspopovic, Stanisa; Rastgaar, Mo; Ronsse, Renaud; van Asseldonk, Edwin; Vanderborght, Bram; Venkadesan, Madhusudhan; Bianchi, Matteo; Braun, David; Godfrey, Sasha Blue; Mastrogiovanni, Fulvio; McDaid, Andrew; Rossi, Stefano; Zenzeri, Jacopo; Formica, Domenico; Karavas, Nikolaos; Marchal-Crespo, Laura; Reed, Kyle B.; Tagliamonte, Nevio Luigi; Burdet, Etienne; Basteris, Angelo; Campolo, Domenico; Deshpande, Ashish; Dubey, Venketesh; Hussain, Asif; Sanguineti, Vittorio; Unal, Ramazan; Caurin, Glauco Augusto de Paula; Koike, Yasuharu; Mazzoleni, Stefano; Park, Hyung-Soon; Remy, C. David; Saint-Bauzel, Ludovic; Tsagarakis, Nikos; Veneman, Jan; Zhang, Wenlong; Medical TechnologiesIncluding supplementary information from the brain or other body parts in the control of brain-machine interfaces (BMIs) has been recently proposed and investigated. Such enriched interfaces are referred to as hybrid BMIs (hBMIs) and have been proven to be more robust and accurate than regular BMIs for assistive and rehabilitative applications. Electromyographic (EMG) activity is one of the most widely utilized biosignals in hBMIs, as it provides a quite direct measurement of the motion intention of the user. Whereas most of the existing non-invasive EEG-EMG-hBMIs have only been subjected to offline testings or are limited to one degree of freedom (DoF), we present an EEG-EMG-hBMI that allows the simultaneous control of 7-DoFs of the upper limb with a robotic exoskeleton. Moreover, it establishes a biologically-inspired hierarchical control flow, requiring the active participation of central and peripheral structures of the nervous system. Contingent visual and proprioceptive feedback about the user's EEG and EMG activity is provided in the form of velocity modulation during functional task training. We believe that training with this closed-loop system may facilitate functional neuroplastic processes and eventually elicit a joint brain and muscle motor rehabilitation. Its usability is validated during a real-time operation session in a healthy participant and a chronic stroke patient, showing encouraging results for its application to a clinical rehabilitation scenario.Item Influence of artifacts on movement intention decoding from EEG activity in severely paralyzed stroke patients(IEEE Computer Society, 2017-08-11) López-Larraz, Eduardo; Bibián, Carlos; Birbaumer, Niels; Ramos-Murguialday, Ander; Ajoudani, Arash; Artemiadis, Panagiotis; Beckerle, Philipp; Grioli, Giorgio; Lambercy, Olivier; Mombaur, Katja; Novak, Domen; Rauter, Georg; Rodriguez Guerrero, Carlos; Salvietti, Gionata; Amirabdollahian, Farshid; Balasubramanian, Sivakumar; Castellini, Claudio; Di Pino, Giovanni; Guo, Zhao; Hughes, Charmayne; Iida, Fumiya; Lenzi, Tommaso; Ruffaldi, Emanuele; Sergi, Fabrizio; Soh, Gim Song; Caimmi, Marco; Cappello, Leonardo; Carloni, Raffaella; Carlson, Tom; Casadio, Maura; Coscia, Martina; De Santis, Dalia; Forner-Cordero, Arturo; Howard, Matthew; Piovesan, Davide; Siqueira, Adriano; Sup, Frank; Lorenzo, Masia; Catalano, Manuel Giuseppe; Lee, Hyunglae; Menon, Carlo; Raspopovic, Stanisa; Rastgaar, Mo; Ronsse, Renaud; van Asseldonk, Edwin; Vanderborght, Bram; Venkadesan, Madhusudhan; Bianchi, Matteo; Braun, David; Godfrey, Sasha Blue; Mastrogiovanni, Fulvio; McDaid, Andrew; Rossi, Stefano; Zenzeri, Jacopo; Formica, Domenico; Karavas, Nikolaos; Marchal-Crespo, Laura; Reed, Kyle B.; Tagliamonte, Nevio Luigi; Burdet, Etienne; Basteris, Angelo; Campolo, Domenico; Deshpande, Ashish; Dubey, Venketesh; Hussain, Asif; Sanguineti, Vittorio; Unal, Ramazan; Caurin, Glauco Augusto de Paula; Koike, Yasuharu; Mazzoleni, Stefano; Park, Hyung-Soon; Remy, C. David; Saint-Bauzel, Ludovic; Tsagarakis, Nikos; Veneman, Jan; Zhang, Wenlong; Medical TechnologiesBrain-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.Item On the extraction of purely motor EEG neural correlates during an upper limb visuomotor task(2022-10-01) Bibián, Carlos; Irastorza-Landa, Nerea; Schönauer, Monika; Birbaumer, Niels; López-Larraz, Eduardo; Ramos-Murguialday, Ander; Medical TechnologiesDeciphering and analyzing the neural correlates of different movements from the same limb using electroencephalography (EEG) would represent a notable breakthrough in the field of sensorimotor neurophysiology. Functional movements involve concurrent posture co-ordination and head and eye movements, which create electrical activity that affects EEG recordings. In this paper, we revisit the identification of brain signatures of different reaching movements using EEG and present, test, and validate a protocol to separate the effect of head and eye movements from a reaching task-related visuomotor brain activity. Ten healthy participants performed reaching movements under two different conditions: avoiding head and eye movements and moving with no constrains. Reaching movements can be identified from EEG with unconstrained eye and head movement, whereas the discriminability of the signals drops to chance level otherwise. These results show that neural patterns associated with different arm movements could only be extracted from EEG if the eye and head movements occurred concurrently with the task, polluting the recordings. Although these findings do not imply that brain correlates of reaching directions cannot be identified from EEG, they show the consequences that ignoring these events can have in any EEG study that includes a visuomotor task.