1
|
Chen Y, Wang F, Li T, Zhao L, Gong A, Nan W, Ding P, Fu Y. Considerations and discussions on the clear definition and definite scope of brain-computer interfaces. Front Neurosci 2024; 18:1449208. [PMID: 39161655 PMCID: PMC11330831 DOI: 10.3389/fnins.2024.1449208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Accepted: 07/22/2024] [Indexed: 08/21/2024] Open
Abstract
Brain-computer interface (BCI) is a revolutionizing human-computer interaction with potential applications in both medical and non-medical fields, emerging as a cutting-edge and trending research direction. Increasing numbers of groups are engaging in BCI research and development. However, in recent years, there has been some confusion regarding BCI, including misleading and hyped propaganda about BCI, and even non-BCI technologies being labeled as BCI. Therefore, a clear definition and a definite scope for BCI are thoroughly considered and discussed in the paper, based on the existing definitions of BCI, including the six key or essential components of BCI. In the review, different from previous definitions of BCI, BCI paradigms and neural coding are explicitly included in the clear definition of BCI provided, and the BCI user (the brain) is clearly identified as a key component of the BCI system. Different people may have different viewpoints on the definition and scope of BCI, as well as some related issues, which are discussed in the article. This review argues that a clear definition and definite scope of BCI will benefit future research and commercial applications. It is hoped that this review will reduce some of the confusion surrounding BCI and promote sustainable development in this field.
Collapse
Affiliation(s)
- Yanxiao Chen
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Fan Wang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Tianwen Li
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
- Faculty of Science, Kunming University of Science and Technology, Kunming, China
| | - Lei Zhao
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
- Faculty of Science, Kunming University of Science and Technology, Kunming, China
| | - Anmin Gong
- School of Information Engineering, Chinese People’s Armed Police Force Engineering University, Xi’an, China
| | - Wenya Nan
- School of Psychology, Shanghai Normal University, Shanghai, China
| | - Peng Ding
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Yunfa Fu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| |
Collapse
|
2
|
Popa LL, Chira D, Strilciuc Ș, Mureșanu DF. Non-Invasive Systems Application in Traumatic Brain Injury Rehabilitation. Brain Sci 2023; 13:1594. [PMID: 38002552 PMCID: PMC10670234 DOI: 10.3390/brainsci13111594] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 11/10/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
Traumatic brain injury (TBI) is a significant public health concern, often leading to long-lasting impairments in cognitive, motor and sensory functions. The rapid development of non-invasive systems has revolutionized the field of TBI rehabilitation by offering modern and effective interventions. This narrative review explores the application of non-invasive technologies, including electroencephalography (EEG), quantitative electroencephalography (qEEG), brain-computer interface (BCI), eye tracking, near-infrared spectroscopy (NIRS), functional near-infrared spectroscopy (fNIRS), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) in assessing TBI consequences, and repetitive transcranial magnetic stimulation (rTMS), low-level laser therapy (LLLT), neurofeedback, transcranial direct current stimulation (tDCS), transcranial alternative current stimulation (tACS) and virtual reality (VR) as therapeutic approaches for TBI rehabilitation. In pursuit of advancing TBI rehabilitation, this narrative review highlights the promising potential of non-invasive technologies. We emphasize the need for future research and clinical trials to elucidate their mechanisms of action, refine treatment protocols, and ensure their widespread adoption in TBI rehabilitation settings.
Collapse
Affiliation(s)
- Livia Livinț Popa
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania; (L.L.P.); (D.F.M.)
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, 400083 Cluj-Napoca, Romania
| | - Diana Chira
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania; (L.L.P.); (D.F.M.)
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, 400083 Cluj-Napoca, Romania
| | - Ștefan Strilciuc
- Research Center for Functional Genomics, Biomedicine, and Translational Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Dafin F. Mureșanu
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania; (L.L.P.); (D.F.M.)
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, 400083 Cluj-Napoca, Romania
| |
Collapse
|
3
|
Alashram AR, Padua E, Annino G. Effects of Brain-Computer Interface Controlled Functional Electrical Stimulation on Motor Recovery in Stroke Survivors: a Systematic Review. CURRENT PHYSICAL MEDICINE AND REHABILITATION REPORTS 2022. [DOI: 10.1007/s40141-022-00369-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
4
|
A Therapeutic Strategy for Lower Motor Neuron Disease and Injury Integrating Neural Stem Cell Transplantation and Functional Electrical Stimulation in a Rat Model. Int J Mol Sci 2022; 23:ijms23158760. [PMID: 35955890 PMCID: PMC9368824 DOI: 10.3390/ijms23158760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/01/2022] [Accepted: 08/03/2022] [Indexed: 12/20/2022] Open
Abstract
Promising treatments for upper motor neuron disease are emerging in which motor function is restored by brain–computer interfaces and functional electrical stimulation. At present, such technologies and procedures are not applicable to lower motor neuron disease. We propose a novel therapeutic strategy for lower motor neuron disease and injury integrating neural stem cell transplantation with our new functional electrical stimulation control system. In a rat sciatic nerve transection model, we transplanted embryonic spinal neural stem cells into the distal stump of the peripheral nerve to reinnervate denervated muscle, and subsequently demonstrated that highly responsive limb movement similar to that of a healthy limb could be attained with a wirelessly powered two-channel neurostimulator that we developed. This unique technology, which can reinnervate and precisely move previously denervated muscles that were unresponsive to electrical stimulation, contributes to improving the condition of patients suffering from intractable diseases of paralysis and traumatic injury.
Collapse
|
5
|
Chandrasekaran S, Fifer M, Bickel S, Osborn L, Herrero J, Christie B, Xu J, Murphy RKJ, Singh S, Glasser MF, Collinger JL, Gaunt R, Mehta AD, Schwartz A, Bouton CE. Historical perspectives, challenges, and future directions of implantable brain-computer interfaces for sensorimotor applications. Bioelectron Med 2021; 7:14. [PMID: 34548098 PMCID: PMC8456563 DOI: 10.1186/s42234-021-00076-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 08/29/2021] [Indexed: 11/10/2022] Open
Abstract
Almost 100 years ago experiments involving electrically stimulating and recording from the brain and the body launched new discoveries and debates on how electricity, movement, and thoughts are related. Decades later the development of brain-computer interface technology began, which now targets a wide range of applications. Potential uses include augmentative communication for locked-in patients and restoring sensorimotor function in those who are battling disease or have suffered traumatic injury. Technical and surgical challenges still surround the development of brain-computer technology, however, before it can be widely deployed. In this review we explore these challenges, historical perspectives, and the remarkable achievements of clinical study participants who have bravely forged new paths for future beneficiaries.
Collapse
Affiliation(s)
- Santosh Chandrasekaran
- Neural Bypass and Brain Computer Interface Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Matthew Fifer
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Stephan Bickel
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Neurosurgery, Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
| | - Luke Osborn
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Jose Herrero
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Breanne Christie
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Junqian Xu
- Departments of Radiology and Psychiatry, Baylor College of Medicine, Houston, TX, USA
| | - Rory K J Murphy
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - Sandeep Singh
- Good Shepherd Rehabilitation Hospital, Allentown, PA, USA
| | - Matthew F Glasser
- Departments of Radiology and Neuroscience, Washington University in St Louis, Saint Louis, MO, USA
| | - Jennifer L Collinger
- Rehabilitation Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Robert Gaunt
- Rehabilitation Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashesh D Mehta
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Neurosurgery, Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
| | - Andrew Schwartz
- McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chad E Bouton
- Neural Bypass and Brain Computer Interface Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA.
- Department of Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA.
| |
Collapse
|
6
|
Camargo-Vargas D, Callejas-Cuervo M, Mazzoleni S. Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:4312. [PMID: 34202546 PMCID: PMC8271710 DOI: 10.3390/s21134312] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 06/09/2021] [Accepted: 06/18/2021] [Indexed: 12/22/2022]
Abstract
In recent years, various studies have demonstrated the potential of electroencephalographic (EEG) signals for the development of brain-computer interfaces (BCIs) in the rehabilitation of human limbs. This article is a systematic review of the state of the art and opportunities in the development of BCIs for the rehabilitation of upper and lower limbs of the human body. The systematic review was conducted in databases considering using EEG signals, interface proposals to rehabilitate upper/lower limbs using motor intention or movement assistance and utilizing virtual environments in feedback. Studies that did not specify which processing system was used were excluded. Analyses of the design processing or reviews were excluded as well. It was identified that 11 corresponded to applications to rehabilitate upper limbs, six to lower limbs, and one to both. Likewise, six combined visual/auditory feedback, two haptic/visual, and two visual/auditory/haptic. In addition, four had fully immersive virtual reality (VR), three semi-immersive VR, and 11 non-immersive VR. In summary, the studies have demonstrated that using EEG signals, and user feedback offer benefits including cost, effectiveness, better training, user motivation and there is a need to continue developing interfaces that are accessible to users, and that integrate feedback techniques.
Collapse
Affiliation(s)
- Daniela Camargo-Vargas
- Software Research Group, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150002, Colombia;
| | - Mauro Callejas-Cuervo
- School of Computer Science, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150002, Colombia
| | - Stefano Mazzoleni
- Department of Electrical and Information Engineering, Politecnico di Bari, 70126 Bari, Italy;
| |
Collapse
|
7
|
Development and Application of Medicine-Engineering Integration in the Rehabilitation of Traumatic Brain Injury. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9962905. [PMID: 34235225 PMCID: PMC8216791 DOI: 10.1155/2021/9962905] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 05/21/2021] [Indexed: 02/02/2023]
Abstract
The rapid progress of the combination of medicine and engineering provides better chances for the clinical treatment and healthcare engineering. Traumatic brain injury (TBI) and its related symptoms have become a major global health problem. At present, these techniques has been widely used in the rehabilitation of TBI. In this review article, we summarizes the progress of the combination of medicine and industry in the rehabilitation of traumatic brain injury in recent years, mainly from the following aspects: artificial intelligence (AI), brain-computer interfaces (BCI), noninvasive brain stimulation (NIBS), and wearable-assisted devices. We believe the summary of this article can improve insight into the combination of medicine and industry in the rehabilitation of traumatic brain injury.
Collapse
|
8
|
Chung E, Lee BH, Hwang S. Therapeutic effects of brain-computer interface-controlled functional electrical stimulation training on balance and gait performance for stroke: A pilot randomized controlled trial. Medicine (Baltimore) 2020; 99:e22612. [PMID: 33371056 PMCID: PMC7748200 DOI: 10.1097/md.0000000000022612] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 09/04/2020] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Brain-computer interface-controlled functional electrical stimulation (BCI-FES) approaches as new feedback training is increasingly being investigated for its usefulness in improving the health of adults or partially impaired upper extremity function in individuals with stroke. OBJECTIVE To evaluate the effects of BCI-FES on postural control and gait performance in individuals with chronic hemiparetic stroke. METHODS A total of 25 individuals with chronic hemiparetic stroke (13 individuals received BCI-FES and 12 individuals received functional electrical stimulation [FES]). The BCI-FES group received BCI-FES on the tibialis anterior muscle on the more-affected side for 30 minutes per session, 3 times per week for 5 weeks. The FES group received FES using the same methodology for the same periods. This study used the Mann-Whitney test to compare the two groups before and after training. RESULTS After training, gait velocity (mean value, 29.0 to 42.0 cm/s) (P = .002) and cadence (mean value, 65.2 to 78.9 steps/min) (P = .020) were significantly improved after BCI-FES training compared to those (mean value, 23.6 to 27.7 cm/s, and mean value, 59.4 to 65.5 steps/min, respectively) after FES approach. In the less-affected side, step length was significantly increased after BCI-FES (mean value, from 28.0 cm to 34.7 cm) more than that on FES approach (mean value, from 23.4 to 25.4 cm) (P = .031). CONCLUSION The results of the BCI-FES training shows potential advantages on walking abilities in individuals with chronic hemiparetic stroke.
Collapse
Affiliation(s)
- Eunjung Chung
- Department of Physical Therapy, Andong Science College, Andong-si
| | - Byoung-Hee Lee
- Department of Physical Therapy, College of Health and Welfare, Sahmyook University, Seoul
| | - Sujin Hwang
- Department of Physical Therapy, Division of Health Science, Baekseok University, Cheonan-si, Republic of Korea
| |
Collapse
|