Curated Papers > High-impact authoritative literature on "brain-machine interfaces"

Brain computer interfaces, a review.

Literature Information

DOI	10.3390/s120201211
PMID	22438708
Journal	Sensors (Basel, Switzerland)
Impact Factor	3.5
JCR Quartile	Q2
Publication Year	2012
Times Cited	363
Keywords	artifact, brain-computer interface (BCI), brain-machine interface, collaborative sensor system, electroencephalography (EEG)
Literature Type	Journal Article, Research Support, Non-U.S. Gov't, Review
ISSN	1424-8220
Pages	1211-79
Issue	12(2)
Authors	Luis Fernando Nicolas-Alonso, Jaime Gomez-Gil

TL;DR

This review paper explores the advancements and methodologies in brain-computer interfaces (BCIs), which enable communication for severely disabled individuals by using cerebral activity to control external devices. It covers the entire BCI process, including signal acquisition, preprocessing, feature extraction, and classification, while highlighting the various technologies employed and their implications for improving the quality of life for those with neuromuscular disorders.

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artifact · brain-computer interface (BCI) · brain-machine interface · collaborative sensor system · electroencephalography (EEG)

Abstract

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

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Primary Questions Addressed

1. What are the latest advancements in neuroimaging modalities for signal acquisition in BCIs?
2. How do different electrophysiological control signals compare in terms of effectiveness for determining user intentions?
3. What are the most common artifacts encountered during the signal enhancement step, and how can they be mitigated?
4. How do various mathematical algorithms impact the accuracy of feature extraction and classification in BCIs?
5. What are some emerging applications of BCIs beyond communication for disabled individuals?

Key Findings

Research Background and Purpose

Brain-Computer Interfaces (BCIs) represent a significant advancement in assistive technology, allowing individuals with severe motor disabilities to communicate and control devices using brain activity alone. This review aims to synthesize the current state-of-the-art in BCI technology, focusing on its components, methodologies, applications, and the challenges that remain in the field.

Main Methods/Materials/Experimental Design

The BCI process consists of several key stages:

1. Signal Acquisition: Various neuroimaging techniques are used, including:

   • Electroencephalography (EEG)
   • Magnetoencephalography (MEG)
   • Electrocorticography (ECoG)
   • Functional Magnetic Resonance Imaging (fMRI)
   • Near Infrared Spectroscopy (NIRS)

2. Preprocessing: Techniques for noise reduction and artifact removal are employed to enhance signal quality.

3. Feature Extraction: Algorithms extract meaningful features from the processed signals to interpret user intentions.

4. Classification: The extracted features are classified into commands using various machine learning algorithms.

5. Control Interface: Translates classified signals into actions that control external devices.

Key Results and Findings

• Neuroimaging Techniques: EEG remains the most commonly used method due to its balance of cost, portability, and temporal resolution. However, invasive techniques like ECoG provide higher signal quality necessary for complex tasks.
• Control Signals: Different types of brain signals (e.g., visual evoked potentials, slow cortical potentials, P300 potentials, and sensorimotor rhythms) have been identified as effective for BCI applications.
• Applications: BCIs have been successfully implemented in communication devices, motor restoration, environmental control, and entertainment, significantly improving the quality of life for users.

Main Conclusions/Significance/Innovation

BCIs represent a transformative technology that enhances communication and independence for individuals with severe disabilities. Despite notable advancements, challenges remain, particularly in improving the information transfer rates and user comfort. The integration of machine learning and neurofeedback has shown promise in refining BCI performance.

Research Limitations and Future Directions

• Limitations: Current BCI systems often require extensive user training and are susceptible to artifacts. The variability of brain signals poses challenges for consistent performance.
• Future Directions: There is a need for research into hybrid systems that combine BCI with other assistive technologies, as well as efforts to enhance the usability and accessibility of BCI applications for a broader audience.

Aspect	Current Status	Future Directions
Signal Acquisition	Primarily EEG, with emerging invasive methods	Explore hybrid systems combining BCI with other technologies
Information Transfer Rate	Generally low for effective interaction	Improve rates through better algorithms and signal processing
User Comfort	Many systems require cumbersome setups	Develop more user-friendly interfaces and technologies
Applications	Communication, motor control, etc.	Expand to everyday use for able-bodied individuals

This review highlights the progress made in BCI technology while underscoring the need for continued research to address existing challenges and enhance user experience.

References

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Literatures Citing This Work

1. Sensors in collaboration increase individual potentialities. - Gonzalo Pajares - Sensors (Basel, Switzerland) (2012)
2. Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces. - Jun Lu;Dennis J McFarland;Jonathan R Wolpaw - Journal of neural engineering (2013)
3. Assisted closed-loop optimization of SSVEP-BCI efficiency. - Jacobo Fernandez-Vargas;Hanns U Pfaff;Francisco B Rodríguez;Pablo Varona - Frontiers in neural circuits (2013)
4. Ectopic eyes outside the head in Xenopus tadpoles provide sensory data for light-mediated learning. - Douglas J Blackiston;Michael Levin - The Journal of experimental biology (2013)
5. New evidence for therapies in stroke rehabilitation. - Bruce H Dobkin;Andrew Dorsch - Current atherosclerosis reports (2013)
6. Electrical stimulation of embryonic neurons for 1 hour improves axon regeneration and the number of reinnervated muscles that function. - Yang Liu;Robert M Grumbles;Christine K Thomas - Journal of neuropathology and experimental neurology (2013)
7. Gyroscope-driven mouse pointer with an EMOTIV® EEG headset and data analysis based on Empirical Mode Decomposition. - Gerardo Rosas-Cholula;Juan Manuel Ramirez-Cortes;Vicente Alarcon-Aquino;Pilar Gomez-Gil;Jose de Jesus Rangel-Magdaleno;Carlos Reyes-Garcia - Sensors (Basel, Switzerland) (2013)
8. Automatic and direct identification of blink components from scalp EEG. - Wanzeng Kong;Zhanpeng Zhou;Sanqing Hu;Jianhai Zhang;Fabio Babiloni;Guojun Dai - Sensors (Basel, Switzerland) (2013)
9. EEG-response consistency across subjects in an active oddball task. - Yvonne Höller;Aljoscha Thomschewski;Jürgen Bergmann;Martin Kronbichler;Julia S Crone;Elisabeth V Schmid;Kevin Butz;Peter Höller;Eugen Trinka - PloS one (2013)
10. EOG artifact correction from EEG recording using stationary subspace analysis and empirical mode decomposition. - Hong Zeng;Aiguo Song;Ruqiang Yan;Hongyun Qin - Sensors (Basel, Switzerland) (2013)

... (353 more literatures)

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