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

Progress in Brain Computer Interface: Challenges and Opportunities. ​

Literature Information ​

DOI10.3389/fnsys.2021.578875
PMID33716680
JournalFrontiers in systems neuroscience
Impact Factor3.5
JCR QuartileQ2
Publication Year2021
Times Cited84
Keywordsbrain computer interface, cognitive rehabilitation, electrical/hemodynamic brain signals, hybrid/multimodal BCI, neuroimaging techniques
Literature TypeJournal Article, Review
ISSN1662-5137
Pages578875
Issue15()
AuthorsSimanto Saha, Khondaker A Mamun, Khawza Ahmed, Raqibul Mostafa, Ganesh R Naik, Sam Darvishi, Ahsan H Khandoker, Mathias Baumert

TL;DR ​

This review discusses the advancements in brain-computer interfaces (BCI), which bridge communication between the brain and external devices, thus enhancing human capabilities in areas like rehabilitation and robotics. It emphasizes the ongoing challenges in standardizing technology and addressing the complexities of brain dynamics, which are crucial for transitioning BCI from research settings to everyday use.

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brain computer interface · cognitive rehabilitation · electrical/hemodynamic brain signals · hybrid/multimodal BCI · neuroimaging techniques

Abstract ​

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

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Primary Questions Addressed ​

  1. What are the specific applications of BCI technology in rehabilitation and how do they compare to traditional methods?
  2. How do current advancements in BCI address the challenges posed by time-variant psycho-neurophysiological fluctuations?
  3. What role does standardization play in the future development and deployment of BCI technologies across different fields?
  4. In what ways can BCI technology enhance human-computer interaction in gaming and robotics beyond current capabilities?
  5. What are the ethical considerations surrounding the use of BCIs in affective computing and how might they impact user privacy?

Key Findings ​

Research Background and Purpose ​

Brain-Computer Interfaces (BCIs) create a direct communication pathway between the brain and external devices, enabling individuals to control devices through brain activity alone. This technology has potential applications in rehabilitation, assistive devices, and cognitive enhancement. The review discusses advancements in BCI technology, the challenges faced, and future opportunities in the field.

Main Methods/Materials/Experimental Design ​

The review synthesizes findings from various studies on BCIs, focusing on signal acquisition methods, such as:

  • EEG (Electroencephalography)
  • ECoG (Electrocorticography)
  • fMRI (Functional Magnetic Resonance Imaging)
  • fNIRS (Functional Near-Infrared Spectroscopy)

The paper categorizes BCIs based on:

  • Signal Modality: Invasive vs. Non-invasive
  • User Interaction: Active, Passive, Reactive

The technological progression of BCIs can be illustrated using the following flowchart:

Mermaid diagram

Key Results and Findings ​

  1. Performance Variability: BCI performance can vary significantly among individuals due to psychological and physiological factors.
  2. Signal Quality: Non-invasive methods like EEG provide good temporal resolution but suffer from spatial limitations, while invasive methods yield higher quality signals.
  3. Neuroplasticity: BCIs can facilitate neuroplastic changes, aiding rehabilitation for motor impairments.
  4. Technological Integration: Hybrid BCIs combining different signal modalities (e.g., EEG and fNIRS) have shown improved classification accuracy.

Main Conclusions/Significance/Innovativeness ​

The review highlights that while significant advancements have been made in BCI technology, challenges such as individual variability, signal noise, and the need for robust signal processing methods remain. Future developments should focus on improving signal acquisition techniques, integrating machine learning for better performance, and addressing ethical considerations in BCI applications.

Research Limitations and Future Directions ​

  1. Limitations:

    • The inherent variability in brain dynamics can lead to inconsistent BCI performance across users.
    • Many BCI systems require extensive calibration, which can be a barrier to widespread adoption.

  2. Future Directions:

    • Develop more generalized BCI models that minimize calibration needs.
    • Enhance the portability and user-friendliness of BCI devices.
    • Investigate the ethical implications of BCI technology, particularly regarding privacy and user consent.

Summary Table of Key BCI Signal Acquisition Methods ​

MethodInvasivenessTemporal ResolutionSpatial ResolutionApplications
EEGNon-invasive~0.05 s~10 mmGeneral BCI, rehabilitation
ECoGInvasive~0.003 s~0.5 mmHigh-precision applications
fMRINon-invasive~1 s~5 mmResearch tool, neuroimaging
fNIRSNon-invasive~1 s~5 mmCognitive assessment, BCI

This structured overview captures the essential insights from the review on the progress of Brain-Computer Interfaces, outlining the current state of technology, challenges, and future research avenues.

References ​

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  4. Cerebral, subcortical, and cerebellar activation evoked by selective stimulation of muscle and cutaneous afferents: an fMRI study. - Daniel L Wardman;Simon C Gandevia;James G Colebatch - Physiological reports (2014)
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  6. Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification. - Antonio Maria Chiarelli;Pierpaolo Croce;Arcangelo Merla;Filippo Zappasodi - Journal of neural engineering (2018)
  7. Closed-loop brain training: the science of neurofeedback. - Ranganatha Sitaram;Tomas Ros;Luke Stoeckel;Sven Haller;Frank Scharnowski;Jarrod Lewis-Peacock;Nikolaus Weiskopf;Maria Laura Blefari;Mohit Rana;Ethan Oblak;Niels Birbaumer;James Sulzer - Nature reviews. Neuroscience (2017)
  8. Tools for probing local circuits: high-density silicon probes combined with optogenetics. - György Buzsáki;Eran Stark;Antal Berényi;Dion Khodagholy;Daryl R Kipke;Euisik Yoon;Kensall D Wise - Neuron (2015)
  9. Cortical and subcortical mechanisms of brain-machine interfaces. - Silvia Marchesotti;Roberto Martuzzi;Aaron Schurger;Maria Laura Blefari;José R Del Millán;Hannes Bleuler;Olaf Blanke - Human brain mapping (2017)
  10. An exploration of EEG features during recovery following stroke - implications for BCI-mediated neurorehabilitation therapy. - Darren J Leamy;Juš Kocijan;Katarina Domijan;Joseph Duffin;Richard Ap Roche;Sean Commins;Ronan Collins;Tomas E Ward - Journal of neuroengineering and rehabilitation (2014)

Literatures Citing This Work ​

  1. Progress in Brain Computer Interface: Challenges and Opportunities. - Simanto Saha;Khondaker A Mamun;Khawza Ahmed;Raqibul Mostafa;Ganesh R Naik;Sam Darvishi;Ahsan H Khandoker;Mathias Baumert - Frontiers in systems neuroscience (2021)
  2. Identification of Brain Electrical Activity Related to Head Yaw Rotations. - Enrico Zero;Chiara Bersani;Roberto Sacile - Sensors (Basel, Switzerland) (2021)
  3. Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review. - Daniela Camargo-Vargas;Mauro Callejas-Cuervo;Stefano Mazzoleni - Sensors (Basel, Switzerland) (2021)
  4. Affective Brain-Computer Music Interfaces-Drivers and Implications. - Elisabeth Hildt - Frontiers in human neuroscience (2021)
  5. Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals. - Radek Martinek;Martina Ladrova;Michaela Sidikova;Rene Jaros;Khosrow Behbehani;Radana Kahankova;Aleksandra Kawala-Sterniuk - Sensors (Basel, Switzerland) (2021)
  6. Influence of Implantation Depth on the Performance of Intracortical Probe Recording Sites. - Joshua O Usoro;Komal Dogra;Justin R Abbott;Rahul Radhakrishna;Stuart F Cogan;Joseph J Pancrazio;Sourav S Patnaik - Micromachines (2021)
  7. Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies. - Luca Ascari;Anna Marchenkova;Andrea Bellotti;Stefano Lai;Lucia Moro;Konstantin Koshmak;Alice Mantoan;Michele Barsotti;Raffaello Brondi;Giovanni Avveduto;Davide Sechi;Alberto Compagno;Pietro Avanzini;Jonas Ambeck-Madsen;Giovanni Vecchiato - Sensors (Basel, Switzerland) (2021)
  8. Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain-Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate. - Jisoo Ha;Seonghun Park;Chang-Hwan Im - Frontiers in neuroinformatics (2022)
  9. Identifying potential training factors in a vibrotactile P300-BCI. - M Eidel;A Kübler - Scientific reports (2022)
  10. Frontal alpha asymmetry interaction with an experimental story EEG brain-computer interface. - Claudia Krogmeier;Brandon S Coventry;Christos Mousas - Frontiers in human neuroscience (2022)

... (74 more literatures)

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