Skip to content
2000
Volume 11, Issue 1
  • ISSN: 2667-3371
  • E-ISSN: 2667-338X

Abstract

Brain-computer interface (BCI) technology has emerged as a groundbreaking innovation with transformative potential in medical devices. BCIs are analyzed for their ability to diagnose, treat, and manage neurological disorders, such as Parkinson's disease, ALS, and stroke.

The study explores the integration of BCI technology into medical devices and examines the challenges and opportunities regulatory authorities face in overseeing this rapidly evolving field.

The study employs a comprehensive literature review with the help of databases like Google Scholar, and PubMed, analyzing case studies and regulatory requirements.

BCI technology enables direct communication between the human brain and external devices, allowing for the control of computers or prosthetic limbs. Additionally, software tools facilitate the analysis of recorded brain signals, aided by advancements in Artificial Intelligence (AI), including Machine Learning (ML) and Deep Learning (DL), for automatic classification of EEG signals. However, the rapid advancement leads to high costs and complexity of BCI systems which can limit their accessibility and scalability, posing a barrier. Moreover, the development of standardized protocols and guidelines for BCI implementation is essential to maintain consistency and reliability across applications.

The ethical considerations surrounding BCI technology are vital and emphasize the need for government regulations to ensure its safe and effective integration into healthcare. BCI's potential for responsible innovation in patient-centric care is advocated, propelling medical technology into a new era of seamless integration and improved patient outcomes.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/adctra/10.2174/0126673371314770241020145227
2024-11-26
2025-09-28

  • From This Site

    /content/journals/adctra/10.2174/0126673371314770241020145227
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. ZhuangM. WuQ. WanF. HuY. State-of-the-art non-invasive brain–computer interface for neural rehabilitation: A review.Journal of Neurorestoratology202081122510.26599/JNR.2020.9040001
     [Google Scholar]
  2. ShihJ.J. KrusienskiD.J. WolpawJ.R. Brain-computer interfaces in medicine.Mayo Clin. Proc.201287326827910.1016/j.mayocp.2011.12.00822325364
     [Google Scholar]
  3. Brain-computer interfaces and the governance system: Upstream approaches.2022Available from: https://www.oecd.org/publications/brain-computer-interfaces-and-the-governance-system-18d86753-en.htm(Accessed on: 14 August 2023)
  4. McFarlandD.J. DalyJ. BoulayC. ParvazM.A. Therapeutic applications of BCI technologies.Brain Comput. Interfaces (Abingdon)201741-2375210.1080/2326263X.2017.130762529527538
     [Google Scholar]
  5. VasiljevicGAM. de MirandaLC. Brain–computer interface games based on consumer-grade eeg devices: A systematic literature review.20193610514210.4473/1820191612213
     [Google Scholar]
  6. ByromB. McCarthyM. SchuelerP. MuehlhausenW. Brain Monitoring Devices in Neuroscience Clinical Research: The Potential of Remote Monitoring Using Sensors, Wearables, and Mobile Devices.Clin. Pharmacol. Ther.20181041597110.1002/cpt.107729574776
     [Google Scholar]
  7. NisoG. RomeroE. MoreauJ.T. AraujoA. KrolL.R. Wireless EEG: A survey of systems and studies.Neuroimage202326911977410.1016/j.neuroimage.2022.11977436566924
     [Google Scholar]
  8. BaiZ. FongK.N.K. ZhangJ.J. ChanJ. TingK.H. Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis.J. Neuroeng. Rehabil.20201715710.1186/s12984‑020‑00686‑232334608
     [Google Scholar]
  9. KosmynaN. LécuyerA. A conceptual space for EEG-based brain-computer interfaces.PLoS One2019141e021014510.1371/journal.pone.021014530605482
     [Google Scholar]
  10. ChenW.L. WagnerJ. HeugelN. SugarJ. LeeY.W. ConantL. MalloyM. HeffernanJ. QuirkB. ZinosA. BeardsleyS.A. ProstR. WhelanH.T. Functional Near-Infrared Spectroscopy and Its Clinical Application in the Field of Neuroscience: Advances and Future Directions.Front. Neurosci.20201472410.3389/fnins.2020.0072432742257
     [Google Scholar]
  11. JavaidMA. Brain–computer interface.SSRN201310.2139/ssrn.2386900.
     [Google Scholar]
  12. BurwellS. SampleM. RacineE. Ethical aspects of brain computer interfaces: a scoping review.BMC Med. Ethics20171816010.1186/s12910‑017‑0220‑y29121942
     [Google Scholar]
  13. MoxonK.A. FoffaniG. Brain-machine interfaces beyond neuroprosthetics.Neuron2015861556710.1016/j.neuron.2015.03.03625856486
     [Google Scholar]
  14. Brain-computer interfaces - Microsoft research.2018Available from: https://www.microsoft.com/en-us/research/project/brain-computer-interfaces/(Accessed on: 28 April 2023)
  15. BCI2000 Wiki.2023Available from: https://www.bci2000.org/mediawiki/index.php/Main_Page(Accessed on: 28 July 2023)
  16. Software for brain computer interfaces and real time neurosciences.2015Available from: http://openvibe.inria.fr/(Accessed on: 28 July 2023)
  17. 2023Available from: https://biosig.sourceforge.net/(Accessed on: 14 August 2023)
  18. FryA. ChanH.W. HarelN.Y. SpielmanL.A. EscalonM.X. PutrinoD.F. Evaluating the clinical benefit of brain-computer interfaces for control of a personal computer.J. Neural Eng.202219202100110.1088/1741‑2552/ac60ca35325875
     [Google Scholar]
  19. OostenveldR. FriesP. MarisE. SchoffelenJM. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data.Comput Intell Neurosci2011201115686910.1155/2011/156869
     [Google Scholar]
  20. CianiO. WilcherB. van GiessenA. TaylorR.S. Linking the Regulatory and Reimbursement Processes for Medical Devices: The Need for Integrated Assessments.Health Econ.201726S1132910.1002/hec.347928139087
     [Google Scholar]
  21. Van ErpJ.B.F. LotteF. TangermannM. Brain-computer interfaces: Beyond medical applications.Computer (Long Beach Calif)2012452634
     [Google Scholar]
  22. MansoorA. UsmanMW. JamilN. NaeemMA. Deep learning algorithm for brain-computer interface.Sci. Program.202020201111210.1155/2020/5762149
     [Google Scholar]
  23. Signal processing for brain–computer interfaces.Available from: https://read.nxtbook.com/ieee/signal_processing/signal_processing_july_2023/signal_processing_for_brain_c.html(Accessed 14 August 2023)2023
  24. ZhongD. KirwanM.J. DuanX. Regulatory barriers blocking standardization of interoperability.JMIR Mhealth Uhealth201312e1310.2196/mhealth.265425098204
     [Google Scholar]
  25. WarbrickT. Simultaneous EEG-fMRI: What have we learned and what does the future hold?Sensors2022226226210.3390/s22062262.
     [Google Scholar]
  26. KrusienskiD.J. McFarlandD.J. PrincipeJ.C. BCI Signal Processing: Feature Extraction.Brain-Computer Interfaces: Principles and Practice20122412414510.1093/acprof:oso/9780195388855.003.0007
     [Google Scholar]
  27. Brain-computer interfaces: Privacy and ethical considerations for the connected mind - Future of privacy forum.Available from: https://fpf.org/blog/brain-computer-interfaces-privacy-and-ethical-considerations-for-the-connected-mind/(Accessed 26 July 2023)2021
  28. FabianiG.E. McFarlandD.J. WolpawJ.R. PfurtschellerG. Conversion of EEG activity into cursor movement by a brain-computer interface (BCI).IEEE Trans. Neural Syst. Rehabil. Eng.200412333133810.1109/TNSRE.2004.83462715473195
     [Google Scholar]
  29. Implanted brain-computer interface (BCI) devices for patients with paralysis or amputation - Non-clinical testing and clinical considerations.Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/implanted-brain-computer-interface-bci-devices-patients-paralysis-or-amputation-non-clinical-testing(Accessed on: 28 April 2023)2021
  30. Next-generation medical devices for brain-computer interfaces.Available from: https://www.wevolver.com/article/next-generation-medical-devices-for-brain-computer-interfaces(Accessed on: 28 April 20232022
  31. McFarlandD.J. WolpawJ.R. Brain-computer interfaces for communication and control.Commun. ACM2011545606610.1145/1941487.194150621984822
     [Google Scholar]
  32. ANSI/AAMI ES60601-1:2005/A2:2021 - Medical electrical equipment - Part 1: General requirements for basic safety and essential performance - Amendment 2.Available from: https://webstore.ansi.org/standards/aami/ansiaamies606012005a22021(Accessed on: 14 August 2023)
  33. ANSI/AAMI/IEC 60601-1-2:2014 - Medical electrical equipment - Part 1-2: General requirements for basic safety and essential performance - Collateral Standard: Electromagnetic disturbances - Requirements and tests.Available from: https://webstore.ansi.org/standards/aami/ansiaamiiec606012014
  34. ISO 14708-1:2014 - Implants for surgery — Active implantable medical devices — Part 1: General requirements for safety, marking and for information to be provided by the manufacturer.Available from: https://www.iso.org/standard/52804.html(Accessed on: 14 August 2023)
  35. Recognized consensus standards: Medical devices.Available from: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfStandards/detail.cfm?standard__identification_no=43309(Accessed on: 24 July 2023)2022
  36. MridhaM.F. DasS.C. KabirM.M. LimaA.A. IslamM.R. WatanobeY. Brain-Computer Interface: Advancement and Challenges.Sensors (Basel)20212117574610.3390/s2117574634502636
     [Google Scholar]
  37. DavisK.C. Meschede-KrasaB. CajigasI. PrinsN.W. AlverC. GalloS. BhatiaS. AbelJ.H. NaeemJ.A. FisherL. RazaF. RifaiW.R. MorrisonM. IvanM.E. BrownE.N. JagidJ.R. PrasadA. Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injury.J. Neuroeng. Rehabil.20221915310.1186/s12984‑022‑01026‑235659259
     [Google Scholar]
  38. Investigational device exemptions (IDEs) for early feasibility medical device clinical studies, including certain first in human (FIH) studies.Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/investigational-device-exemptions-ides-early-feasibility-medical-device-clinical-studies-including(Accessed on: 4 July 2023)2013
  39. WilbonTA. Application of risk management principles for medical devices.Available from: https://fda.yorkcast.com/mediasite/Play/2f3dce7e06d140c4a769666418af58e91d
  40. DadiaT. GreenbaumD. Neuralink: The Ethical ‘Rithmatic of Reading and Writing to the Brain.AJOB Neurosci.201910418718910.1080/21507740.2019.166512931642761
     [Google Scholar]
  41. CberC. Use of international standard ISO 10993-1, ‘Biological evaluation of medical devices-Part 1: Evaluation and testing within a risk management process’ Guidance for industry and food and drug administration staff preface public comment.Available from: https://www.fda.gov/vaccines-blood-biologics/guidance-compliance-regulatory-information-(Accessed on: 14 August 2023)2020
  42. Sterilization for medical devices.Available from: https://www.fda.gov/medical-devices/general-hospital-devices-and-supplies/sterilization-medical-devices(Accessed on: 25 July 2023)2023
  43. FierensG. StandaertN. PeetersR. GlorieuxC. VerhaertN. Safety of active auditory implants in magnetic resonance imaging.J. Otol.202116318519810.1016/j.joto.2020.12.00534220987
     [Google Scholar]
  44. ClarkGS. Shelf life of medical devices.1991Available from: https://www.fda.gov/media/72487/download
  45. WangH. SuQ. YanZ. LuF. ZhaoQ. LiuZ. ZhouF. Rehabilitation Treatment of Motor Dysfunction Patients Based on Deep Learning Brain–Computer Interface Technology.Front. Neurosci.20201459508410.3389/fnins.2020.59508433192282
     [Google Scholar]
  46. MycoScience - Accelerated aging testing for medical devices.Available from: https://mycoscience.com/accelerated-aging-testing-for-medical-devices/(Accessed on: 14 August 2023)2023
  47. Applying human factors and usability engineering to medical devices guidance for industry and food and drug administration staff preface public comment.Available from: http://www.regulations.gov(Accessed on: 25 July 2023)2000
  48. Gutierrez-MartinezJ. Mercado-GutierrezJ.A. Carvajal-GámezB.E. Rosas-TriguerosJ.L. Contreras-MartinezA.E. Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions.Front. Hum. Neurosci.20211577283710.3389/fnhum.2021.77283734899220
     [Google Scholar]
  49. Electromagnetic compatibility (EMC) of medical devices.Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/electromagnetic-compatibility-emc-medical-devices(Accessed on: 24 July 2023)2022
  50. WolpawJ.R. BirbaumerN. McFarlandD.J. PfurtschellerG. VaughanT.M. Brain–computer interfaces for communication and control.Clin. Neurophysiol.2002113676779110.1016/S1388‑2457(02)00057‑312048038
     [Google Scholar]
  51. Testing and labeling medical devices for safety in the magnetic resonance (MR) environment guidance for industry and food and drug administration staff.Available from: https://www.regulations.gov(Accessed on: 4 July 2023)2014
  52. Radio frequency wireless technology in medical devices - Guidance for industry and FDA staff.Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/radio-frequency-wireless-technology-medical-devices-guidance-industry-and-fda-staff(Accessed on: 4 July 2023)2013
  53. NyenhuisJ.A. Sung-Min Park KamondetdachaR. AmjadA. ShellockF.G. RezaiA.R. MRI and implanted medical devices: basic interactions with an emphasis on heating.IEEE Trans. Device Mater. Reliab.20055346748010.1109/TDMR.2005.859033
     [Google Scholar]
  54. PhanH.P. Implanted Flexible Electronics: Set Device Lifetime with Smart Nanomaterials.Micromachines (Basel)202112215710.3390/mi1202015733562545
     [Google Scholar]
  55. WangH. D’AndreaD. ChoiY.S. BourichaY. WickersonG. AhnH.Y. GuoH. HuangY. SandhuM.S. JordanS.W. RogersJ.A. FranzC.K. Implantation and control of wireless, battery-free systems for peripheral nerve interfacing.J. Vis. Exp.2021117610.3791/63085‑v34747395
     [Google Scholar]
  56. ShepherdR.K. VillalobosJ. BurnsO. NayagamD.A.X. The development of neural stimulators: a review of preclinical safety and efficacy studies.J. Neural Eng.201815404100410.1088/1741‑2552/aac43c29756600
     [Google Scholar]
  57. General considerations for animal studies intended to evaluate medical devices.Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/general-considerations-animal-studies-intended-evaluate-medical-devices(Accessed on: 24 July 2023)2023
  58. NunezP.L. Electric and Magnetic Fields Produced by the Brain.Brain-Computer Interfaces: Principles and Practice201224466410.1093/acprof:oso/9780195388855.003.0003
     [Google Scholar]
  59. FDA guidance on BCI devices: Non-clinical bench testing - RegDesk.Available from: https://www.regdesk.co/fda-guidance-on-bci-devices-non-clinical-bench-testing/(Accessed on: 24 July 2023)2021
  60. Cyborg artist Neil Harbisson uses his Eyeborg to listen to colour.Available from: https://www.dezeen.com/2013/11/20/interview-with-human-cyborg-neil-harbisson/(Accessed on: 24 July 2023)2013
  61. MaslenH. DouglasT. Cohen KadoshR. LevyN. SavulescuJ. The regulation of cognitive enhancement devices: extending the medical model.J. Law Biosci.201411689310.1093/jlb/lst00325243073
     [Google Scholar]
  62. ChromikJ. KirstenK. HerdickA. KappattanavarA.M. ArnrichB. SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies.Sensors (Basel)202222140810.3390/s2201040835009950
     [Google Scholar]
  63. KellmeyerP. Big Brain Data: On the Responsible Use of Brain Data from Clinical and Consumer-Directed Neurotechnological Devices.Neuroethics2021141839810.1007/s12152‑018‑9371‑x
     [Google Scholar]
  64. CoinA. MulderM. DubljevićV. Ethical aspects of BCI technology: What is the state of the art?Philosophies2020543110.3390/philosophies5040031.
     [Google Scholar]
  65. Bioethics of medical devices based on brain computer interfaces (BCI).Available from: https://www.researchgate.net/publication/349637410_Bioethics_of_Medical_Devices_Based_on_Brain_Computer_Interfaces_BCI(Accessed on: 28 April 2023)2021
  66. Recommended content and format of non-clinical bench performance testing information in premarket submissions.Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/recommended-content-and-format-non-clinical-bench-performance-testing-information-premarket(Accessed on: 24 July 2023)2019
  67. ObidinN. TasnimF. DagdevirenC. The Future of Neuroimplantable Devices: A Materials Science and Regulatory Perspective.Adv. Mater.20203215190148210.1002/adma.20190148231206827
     [Google Scholar]
  68. TabotG.A. DammannJ.F. BergJ.A. TenoreF.V. BobackJ.L. VogelsteinR.J. BensmaiaS.J. Restoring the sense of touch with a prosthetic hand through a brain interface.Proc. Natl. Acad. Sci. USA201311045182791828410.1073/pnas.122111311024127595
     [Google Scholar]
  69. The alliance of advanced biomedical engineering.Available from: https://aabme.asme.org/posts/brain-computer-interface-the-most-investigated-areas-in-health-care-hold-a-promising-future(Accessed on: 28 April 2023)2017
  70. Neural networks in speech recognition.Available from: https://www.researchgate.net/publication/2249623_Neural_Networks_In_Speech_Recognition(Accessed on: 14 August 2023)1994
  71. Blackrock and Pitt work on first at-home BCI system for remote trials.Available from: https://www.medicaldesignandoutsourcing.com/blackrock-neurotech-pitt-first-at-home-bci-system-for-remote-trials/(Accessed on: 4 July 2023)2022
  72. HerffC. SchultzT. Automatic speech recognition from neural signals: A focused review.Front. Neurosci.20161042910.3389/fnins.2016.0042927729844
     [Google Scholar]
  73. CowanR.E. FreglyB.J. BoningerM.L. ChanL. RodgersM.M. ReinkensmeyerD.J. Recent trends in assistive technology for mobility.J. Neuroeng. Rehabil.2012912010.1186/1743‑0003‑9‑2022520500
     [Google Scholar]
  74. ShovalA. AdamsC. David-PurM. SheinM. HaneinY. SernagorE. Carbon nanotube electrodes for effective interfacing with retinal tissue.Front. Neuroeng.20092410.3389/neuro.16.004.200919430595
     [Google Scholar]
  75. WeilandJ.D. HumayunM.S. Retinal Prosthesis.IEEE Trans. Biomed. Eng.20146151412142410.1109/TBME.2014.231473324710817
     [Google Scholar]
  76. Health Quality OntarioBilateral cochlear implantation: A health technology assessment.Ont Health Technol Assess Ser2018186113930443278
     [Google Scholar]
  77. PandarinathC. NuyujukianP. BlabeC.H. SoriceB.L. SaabJ. WillettF.R. HochbergL.R. ShenoyK.V. HendersonJ.M. High performance communication by people with paralysis using an intracortical brain-computer interface.eLife20176e1855410.7554/eLife.1855428220753
     [Google Scholar]
  78. Biased algorithms are everywhere, and no one seems to care.Available from: https://www.technologyreview.com/2017/07/12/150510/biased-algorithms-are-everywhere-and-no-one-seems-to-care/(Accessed on: 4 July 2023)2017
  79. ClantonS.T. Brain-computer interface control of an anthropomorphic robotic arm.Epub ahead of print201110.1184/R1/6715016.V1
     [Google Scholar]
  80. Scientific AmericanElon Musk’s secretive brain tech company debuts a sophisticated neural implant.Available from: https://www.scientificamerican.com/article/elon-musks-secretive-brain-tech-company-debuts-a-sophisticated-neural-implant1/(Accessed on: 25 July 2023)2019
  81. How neuralink is shaping the medical device industry in 2022.Available from: https://www.mantellassociates.com/blog/2022/03/how-neuralink-is-shaping-the-medical-device-industry-in-2022?source=google.com(Accessed on: 4 July 2023)2022
  82. The technology- Synchron.Available from: https://synchron.com/technology(Accessed 14 August 2023)2023
  83. Brain computer interface market size & share report, 2030.Available from: https://www.grandviewresearch.com/industry-analysis/brain-computer-interfaces-market(Accessed on: 24 July 2024)
  84. PeksaJ. MamchurD. State-of-the-Art on Brain-Computer Interface Technology.Sensors (Basel)20232313600110.3390/s2313600137447849
     [Google Scholar]
  85. LebedevM.A. NicolelisM.A.L. Brain–machine interfaces: past, present and future.Trends Neurosci.200629953654610.1016/j.tins.2006.07.00416859758
     [Google Scholar]
  86. SemenovY.R. YehS.T. SeshamaniM. WangN.Y. TobeyE.A. EisenbergL.S. QuittnerA.L. FrickK.D. NiparkoJ.K. Age-dependent cost-utility of pediatric cochlear implantation.Ear Hear.201334440241210.1097/AUD.0b013e3182772c6623558665
     [Google Scholar]
  87. NeveO.M. BoermanJ.A. van den HoutW.B. BriaireJ.J. van BenthemP.P.G. FrijnsJ.H.M. Cost-benefit Analysis of Cochlear Implants: A Societal Perspective.Ear Hear.20214251338135010.1097/AUD.000000000000102133675588
     [Google Scholar]
  88. Pérez-MartínJ. ArtasoM.A. DíezF.J. Cost‐effectiveness of pediatric bilateral cochlear implantation in Spain.Laryngoscope2017127122866287210.1002/lary.2676528776715
     [Google Scholar]
  89. Prosthesis - Wikipedia.Available from: https://en.wikipedia.org/wiki/Prosthesis#Low-cost_prosthetics_for_children(Accessed on: 23 July 2024)
  90. How artificial limb is made – Material, manufacture, making, used, parts, components, structure, procedure.Available from: www.madehow.com, http://www.madehow.com/Volume-1/Artificial-Limb.html(Accessed on: 23 July 2024)
  91. McCrimmonC.M. FuJ.L. WangM. LopesL.S. WangP.T. Karimi-BidhendiA. LiuC.Y. HeydariP. NenadicZ. DoA.H. Performance Assessment of a Custom, Portable, and Low-Cost Brain–Computer Interface Platform.IEEE Trans. Biomed. Eng.201764102313232010.1109/TBME.2017.266757928207382
     [Google Scholar]
  92. BeyrouthyT. Al KorkS. KorbaneJ.A. EEG Mind Controlled Smart Prosthetic Arm – A Comprehensive Study. Advances in Science.Technology and Engineering Systems Journal20172891899
     [Google Scholar]
  93. JochumsenM. JanjuaT.A.M. ArceoJ.C. LauberJ. BuessingerE.S. KæselerR.L. Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton.Sensors (Basel)202121257210.3390/s2102057233467420
     [Google Scholar]
/content/journals/adctra/10.2174/0126673371314770241020145227
Loading
Brain-computer Interface on Medical Devices: A Promising Technology with Limitless Possibilities
Appl. Drug Resea., Clin. Trials and Regu. Affa. 11, E26673371314770 (2025); https://doi.org/10.2174/0126673371314770241020145227
/content/journals/adctra/10.2174/0126673371314770241020145227
/content/journals/adctra/10.2174/0126673371314770241020145227
Loading

Data & Media loading...


  • Article Type:     Review Article
Keyword(s): Brain-computer interface; deep learning; electroencephalogram; food and drug administration; machine learning; signal

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test