ELECTROENCEPHALOGRAPHIC (EEG) CONTROL OF THREE-DIMENSIONAL MOVEMENT

By Dennis J. McFarland, William A. Sarnacki, Jonathan R. Wolpaw. Journal of Neural Engineering, 2010

479 citations recorded by the source corpus.

Brain–computer interfaces (BCIs) can use brain signals from the scalp (EEG), the cortical surface (ECoG), or within the cortex to restore movement control to people who are paralyzed. Like muscle-based skills, BCIs' use requires activity-dependent adaptations in the brain that maintain stable relationships between the person's intent and the signals that convey it. This study shows that humans can learn over a series of training sessions to use EEG for three-dimensional control. The responsible EEG features are focused topographically on the scalp and spectrally in specific frequency bands. People acquire simultaneous control of three independent signals (one for each dimension) and reach targets in a virtual three-dimensional space. Such BCI control in humans has not been reported previously. The results suggest that with further development noninvasive EEG-based BCIs might control the complex movements of robotic arms or neuroprostheses.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/09b11ce53440b9b0600554c168ee7033b4d1929e

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In Cognitive Neuroscience

Citations in the corpus

Prior Knowledge Improves Decoding of Finger Flexion from Electrocorticographic Signals (2011, background): “…from microelectrodes implanted within cortex (Taylor et al., 2002; Hatsopoulos et al., 2004; Carmena et al., 2005; Lebedev et al., 2005; Velliste et al., 2008), but also by ECoG (Wolpaw and McFarland, 2004; McFarland et al., 2008, 2010; Schwartz et al., 2001; Koyama et al., 2010 for review).”
Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects (2009, background): “These studies amply demonstrate that, with training, most people with or without motor disabilities can use SMR amplitudes to select targets by controlling the one, two, or three dimensional movements of a cursor [22,83].”
Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, unknown): “Over the past decades, the SMR-based BCI applications have come a long way from the very beginning of a 1-D “Ping-Pong”like game [25], [80] to the state of the art, a computer cursor [16] or virtual helicopter [17], [18] in the 3-D virtual world, or even an unmanned helicopter in the real world [19]”
Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019, methodology): “These clusters were located andweighted in accordancewith the underlying event-related (de)synchronization generated during the correspondingMI tasks (19) and are similar to those used in other noninvasive cursor control studies, identified through either datadriven (30) or manual (28) selection pro”
EEG-Based Brain-Computer Interfaces (2017, background): “(From Ref. and particularly when users advance to controlling multiple dimensions, imagery tends to disappear [14,15].”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, unknown): “Finally, they expanded their work and controlled a cursor to hit targets located in three-dimensional space [49].”