Brain–computer interfaces: communication and restoration of movement in paralysis

By Niels Birbaumer, Leonardo G. Cohen. Journal of Physiology, 2007

789 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/926128a5039fc766807a9d686321dc52f64434ca

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In Cellular and Molecular Neuroscience

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Citations in the corpus

The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology (2010, background): “…the overall aim to help paralyzed patients (Kübler et al., 1999, 2000, 2001, 2005; Birbaumer et al., 2000, 2003; Neuper et al., 2003; Pfurtscheller et al., 2003; Birbaumer and Cohen, 2007; Dobkin, 2007; Müller-Putz et al., 2007; Cincotti et al., 2008; Daly and Wolpaw, 2008; Conradi et al., 2009).”
Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019, neutral): “Previous work has indicated that DT task training can lead to strong learning effects (31); however, some users have required nearly 70 training sessions to do so (18).”
The brain–computer interface cycle (2009, background): “Often, signal feedback is used in combination with the actual control of an output device [81], to allow the user to adapt and learn.”
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces (2011, background): “Such systems allow people to communicate through direct measurements of brain activity, without requiring any movement [1], [2], [3].”
Brain–computer interface systems: progress and prospects (2007, background): “BCI research might also inspire systems to treat disorders that do not directly impair communication, such as epilepsy, anxiety, attentional disorders, psychopathy and stroke [12,53,145,163–166 BCI research can capitalize on advances in basic science and technology.”
Progress in Brain Computer Interface: Challenges and Opportunities (2019, background): “The usefulness of BCI-based motor rehabilitation is limited for locked-in patients because they are unable to interact with the system [103].”
EEG and MEG: relevance to neuroscience. (2013, background): “…EEG/MEG rhythmic activities strongly related to specific movements, e.g., of hands or feet, occur also if the subject just imagines themovement, without executing it, whichmakes these signals good candidates to operate brain-computer interfaces (BCIs) (for review, see Birbaumer and Cohen, 2007).”
Brain-Computer Interfaces: A Gentle Introduction (2009, background): “This refers to the goal of using a BCI to treat disorders such as stroke, ADHD, autism, or emotional disorders [51–53].”
The Hybrid BCI (2010, background): “Non-invasive BCIs utilize changes in the dynamics of brain oscillations such as event-related (de)synchronization (ERD, ERS), steady-state evoked potentials (SSEPs), P300 evoked potentials and related components, real-time fMRI BOLD signals or near-infrared spectroscopy (NIRS)-measured oxyhemoglobin”
Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects (2009, methodology): “in physiological regulation of SCP signals and suggested these areas are important for successful use of this type of BCI [73].”
Functional near-infrared spectroscopy: current status and future prospects. (2007, methodology): “, the efferent nervous system and muscles .(90) BCI has been given much”