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Brain-computer interfaces in neurological rehabilitation.
Literature Information
| DOI | 10.1016/S1474-4422(08)70223-0 |
|---|---|
| PMID | 18835541 |
| Journal | The Lancet. Neurology |
| Impact Factor | 45.5 |
| JCR Quartile | Q1 |
| Publication Year | 2008 |
| Times Cited | 293 |
| Keywords | Brain-computer interface, Neurological rehabilitation, Electroencephalogram, Motor control, Brain plasticity |
| Literature Type | Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S., Review |
| ISSN | 1474-4422 |
| Pages | 1032-43 |
| Issue | 7(11) |
| Authors | Janis J Daly, Jonathan R Wolpaw |
TL;DR
Recent advancements in brain signal analysis and non-invasive EEG-based brain-computer interface (BCI) technologies have enabled individuals with severe motor disabilities to communicate and control their environment, significantly enhancing their independence and quality of life. This research highlights the potential of BCIs not only to assist those with neurological disorders like amyotrophic lateral sclerosis but also to facilitate rehabilitation and improve motor control in stroke patients by promoting brain plasticity and supplementing impaired muscle function.
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Brain-computer interface · Neurological rehabilitation · Electroencephalogram · Motor control · Brain plasticity
Abstract
Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient.
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Primary Questions Addressed
- What are the specific mechanisms by which BCI technologies enhance neuroplasticity in stroke rehabilitation?
- How do different types of brain signals (e.g., EEG vs. fNIRS) compare in effectiveness for BCI applications in neurological rehabilitation?
- What are the potential long-term impacts of using BCIs on the quality of life for patients with severe motor disabilities?
- How can BCI technologies be integrated with existing rehabilitation protocols to optimize recovery outcomes for patients with neurological disorders?
- What are the challenges and limitations in the current implementation of BCI systems for neurological rehabilitation, and how might these be addressed in future research?
Key Findings
Key Insights
Research Background and Objective: The study addresses the challenges faced by individuals with severe motor disabilities due to neurological disorders, such as advanced amyotrophic lateral sclerosis (ALS) and post-stroke conditions. These patients often struggle with communication and mobility, which significantly impacts their quality of life. The primary objective of the research is to explore the potential of brain-computer interface (BCI) technologies, particularly non-invasive electroencephalogram (EEG)-based systems, in facilitating communication and control of external devices, thereby enhancing the independence of these individuals.
Main Methods and Findings: Recent advancements in signal analysis, patient training, and computational capabilities have led to the development of BCIs that can interpret brain signals to control devices like computer cursors, limb orthoses, and environmental controls. These systems use EEG to capture brain activity and translate it into commands for external devices. The findings indicate that BCIs can significantly aid in communication and control tasks, allowing individuals to perform activities such as word processing and internet access. Furthermore, BCIs can support motor rehabilitation by guiding activity-dependent brain plasticity through real-time feedback on brain activity, enabling patients to reduce abnormal brain activity and improve motor control.
Core Conclusion: The study concludes that BCI technologies represent a transformative approach in neurological rehabilitation. By allowing users to control devices through their brain signals, BCIs not only enhance independence but also have the potential to restore motor function post-stroke or after traumatic brain injuries. The dual role of BCIs in facilitating communication and enhancing rehabilitation protocols underscores their significance in therapeutic contexts.
Research Significance and Impact: The implications of this research are profound, as BCI technologies could revolutionize rehabilitation strategies for individuals with severe motor impairments. By bridging the gap between impaired neuromuscular systems and the ability to interact with the environment, BCIs can lead to improved quality of life and greater autonomy for patients. Additionally, the ability of BCIs to facilitate brain plasticity presents new avenues for rehabilitation programs, potentially accelerating recovery and enhancing muscle control. Overall, this research highlights the importance of integrating advanced technology in therapeutic practices, paving the way for more innovative and effective rehabilitation solutions in neurology.
Literatures Citing This Work
- Review of control strategies for robotic movement training after neurologic injury. - Laura Marchal-Crespo;David J Reinkensmeyer - Journal of neuroengineering and rehabilitation (2009)
- Decoding movement-related cortical potentials from electrocorticography. - Chandan G Reddy;Goutam G Reddy;Hiroto Kawasaki;Hiroyuki Oya;Lee E Miller;Matthew A Howard - Neurosurgical focus (2009)
- Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity. - Wei Wang;Jennifer L Collinger;Monica A Perez;Elizabeth C Tyler-Kabara;Leonardo G Cohen;Niels Birbaumer;Steven W Brose;Andrew B Schwartz;Michael L Boninger;Douglas J Weber - Physical medicine and rehabilitation clinics of North America (2010)
- Towards a user-friendly brain-computer interface: initial tests in ALS and PLS patients. - Ou Bai;Peter Lin;Dandan Huang;Ding-Yu Fei;Mary Kay Floeter - Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology (2010)
- Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects. - Joseph N Mak;Jonathan R Wolpaw - IEEE reviews in biomedical engineering (2009)
- Decoding spoken words using local field potentials recorded from the cortical surface. - Spencer Kellis;Kai Miller;Kyle Thomson;Richard Brown;Paul House;Bradley Greger - Journal of neural engineering (2010)
- Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges. - J D R Millán;R Rupp;G R Müller-Putz;R Murray-Smith;C Giugliemma;M Tangermann;C Vidaurre;F Cincotti;A Kübler;R Leeb;C Neuper;K-R Müller;D Mattia - Frontiers in neuroscience (2010)
- The neural substrate of predictive motor timing in spinocerebellar ataxia. - Martin Bares;Ovidiu V Lungu;Tao Liu;Tobias Waechter;Christopher M Gomez;James Ashe - Cerebellum (London, England) (2011)
- The Berlin Brain-Computer Interface: Non-Medical Uses of BCI Technology. - Benjamin Blankertz;Michael Tangermann;Carmen Vidaurre;Siamac Fazli;Claudia Sannelli;Stefan Haufe;Cecilia Maeder;Lenny Ramsey;Irene Sturm;Gabriel Curio;Klaus-Robert Müller - Frontiers in neuroscience (2010)
- Neuroplasticity in the context of motor rehabilitation after stroke. - Michael A Dimyan;Leonardo G Cohen - Nature reviews. Neurology (2011)
... (283 more literatures)
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