A brain-actuated wheelchair: asynchronous and non-invasive Brain-computer interfaces for continuous control of robots.

By F. Galán, M. Nuttin, Eileen Lew, P. Ferrez, G. Vanacker, Johan Philips, J. D. Millán. Clinical Neurophysiology, 2008

702 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/4f751de81cd438a56f18a9b153acaddd464f1100

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In Cognitive Neuroscience

In Experimental and Cognitive Psychology

Jonathan R. Wolpaw

In Human-Computer Interaction

Citations in the corpus

Brain Computer Interfaces, a Review (2012, background): “Adaptation to non-stationary signals is particularly necessary in asynchronous and non-invasive BCIs [215,216].”
Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges (2010, background): “These BCI games are based on different BCI protocols, from spontaneous EEG [116, 88, 184] to evoked EEG potentials [93, 52], where the user delivers (as usual for a BCI) mental commands to control some aspect of the game.”
Brain-computer interfaces in medicine. (2012, methodology): “Sensorimotor rhythms have been used to control cursors in 1, 2,(10,23,24) and 3(25,26) dimensions, a spelling device,(27) conventional assistive devices,(28) a hand orthosis,(29) functional electrical stimulation (FES) of a patient’s hand,(30) robotic and prosthetic devices,(31,32) and a wheelchair.”
Event-related (De)synchronization (ERD/ERS) during motor imagery tasks: Implications for brain–computer interfaces (2011, background): “…Nijboer et al., 2008; Kübler and Birbaumer, 2008), move a cursor on the computer screen (Wolpaw et al., 2002), play an electronic pingpong game (Babiloni et al., 2003), control an orthosis that provides hand grasp (Obermaier et al., 2001), or operate a brainactuated wheelchair (Galán et al., 2008).”
An end-to-end deep learning approach to MI-EEG signal classification for BCIs (2018, background): “R Controlling an electric wheelchair (Gal´an et al., 2008), text input (Guan et al., 2004), or neural bypasses to control paretic limbs (Young et al., 2014) are some of the use cases that demonstrate their possibilities.”
EEG-Based Brain-Controlled Mobile Robots: A Survey (2013, positive): “Since then, Mill´an et al. have continued to develop brain-controlled mobile robots using ERD/ERS BCIs [34], [54]–[56], [145], [158].”
Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects (2009, methodology): “based on the EEG activity associated with various mental tasks and a shared control system [86], [124].”
Quadcopter control in three-dimensional space using a noninvasive motor imagery based brain-computer interface (2013, methodology): “…been developed using a motor imagery paradigm (Pfurtscheller et al 1993, Wolpaw et al 1998, Wolpaw and McFarland 2004, Wang and He 2004, Wang et al 2004, Qin et al 2004, Kamousi et al 2005, Qin and He 2005, Gal´an et al 2008, Yuan et al 2008, 2010a, 2010b, McFarland et al 2010, Doud et al 2012).”
Brain-Computer Interfaces for Communication and Control (2011, neutral): “BCI systems have also been developed for control applications; for example, several groups have shown that human subjects can use their EEG activity to drive a simulated wheelchair.(10,14) Bell et al.”
Progress in Brain Computer Interface: Challenges and Opportunities (2019, background): “Brain signals can be translated to drive wheelchairs [107], [108].”