Motor learning elicited by voluntary drive.

By Martin Lotze, Christoph Braun, Niels Birbaumer, S. Anders, Leonardo G. Cohen. Brain : a journal of neurology, 2003

688 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Neurology

View Paper: https://www.semanticscholar.org/paper/efda1d95958a2fb2feaf7becb364723185964089

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Christa Neuper
2. Bin He
3. John C. Rothwell
4. David J. Reinkensmeyer
5. Jeremy L. Emken
6. Steven C. Cramer
7. Ying Mao
8. Sunil Kumar Agrawal

In Cognitive Neuroscience

1. Gernot R. Müller-Putz
2. Reinhold Scherer
3. Christa Neuper
4. Bin He
5. Bryan S. Baxter
6. Bradley Jay Edelman
7. Teodoro Solis-Escalante
8. L. Der-Yeghiaian

In Neurology

1. Steven C. Cramer
2. L. Der-Yeghiaian
3. Vu Le
4. Craig D. Takahashi
5. Rehan R Motiwala
6. Karin Rosenkranz
7. John C. Rothwell
8. Christa Neuper

Citations in the corpus

• Design and Evaluation of the LOPES Exoskeleton Robot for Interactive Gait Rehabilitation (2007, background): “At the moment, determinants of successful gait training are largely unknown, although intensity [11], self-initiative [12] and task specificity [13], [14] seem to be important.”
• Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects (2012, background): “For example, Lotze et al. (2003) found that active training improved motor performance and increased corticomotor excitability in comparison with passive training.”
• Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019, background): “BCI learning rates can vary among individuals, but it is generally thought that a user’s motivation and cognitive arousal play significant roles in the process of skill acquisition and eventual task performance (15, 16).”
• Ankle dorsiflexion as an fMRI paradigm to assay motor control for walking during rehabilitation (2004, methodology): “…have used toe, foot, or knee movements during fMRI (Debaere et al., 2001; Kollias et al., 2001; Lafleur et al., 2002; Leherricy et al., 1998; Lotze et al., 2000, 2003b; Luft et al., 2002; Sabbah et al., 2002; Sahyoun et al., 2004) and PET (Johannsen et al., 2001; Roelcke et al., 1997) to…”
• Robotics, motor learning, and neurologic recovery. (2004, result): “consistent with the observation that passive exercise engaged different brain areas and produced less motor learning when compared to active exercise in unimpaired subjects (97).”
• Robot-based hand motor therapy after stroke. (2007, background): “These two modes were chosen, and a passive assist mode in which the subjects relaxes while the robot performs all limb movements excluded, because interventional studies suggest greater gains are achieved when the subject actively exerts an effort (Lotze et al., 2003; Perez et al., 2004).”
• Robot assisted gait training with active leg exoskeleton (ALEX) (2009, methodology): “This training approach is based on previous studies that indicated the efficacy of robotic gait training with various trajectories [10]‐[12], as well as the importance of subjects’ voluntary participation to enhance gait training [13]‐[ 15 ] and learning in general [16], [17].”
• Differential Modulation of Motor Cortical Plasticity and Excitability in Early and Late Phases of Human Motor Learning (2007, background): “…practice enhanced the MEP (Muellbacher et al., 2002; Ziemann et al., 2004; Stefan et al., 2006; Rosenkranz and Rothwell, 2006), increased the steepness of the IO curve (Lotze et al., 2003; Perez et al., 2004; Jensen et al., 2005), and reduced SICI (Liepert et al., 1998, 2004; Perez et al., 2004).”