1
|
Eidel M, Pfeiffer M, Ziebell P, Kübler A. Recording the tactile P300 with the cEEGrid for potential use in a brain-computer interface. Front Hum Neurosci 2024; 18:1371631. [PMID: 38957693 PMCID: PMC11218745 DOI: 10.3389/fnhum.2024.1371631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/27/2024] [Indexed: 07/04/2024] Open
Abstract
Brain-computer interfaces (BCIs) are scientifically well established, but they rarely arrive in the daily lives of potential end-users. This could be in part because electroencephalography (EEG), a prevalent method to acquire brain activity for BCI operation, is considered too impractical to be applied in daily life of end-users with physical impairment as an assistive device. Hence, miniaturized EEG systems such as the cEEGrid have been developed. While they promise to be a step toward bridging the gap between BCI development, lab demonstrations, and home use, they still require further validation. Encouragingly, the cEEGrid has already demonstrated its ability to record visually and auditorily evoked event-related potentials (ERP), which are important as input signal for many BCIs. With this study, we aimed at evaluating the cEEGrid in the context of a BCI based on tactually evoked ERPs. To compare the cEEGrid with a conventional scalp EEG, we recorded brain activity with both systems simultaneously. Forty healthy participants were recruited to perform a P300 oddball task based on vibrotactile stimulation at four different positions. This tactile paradigm has been shown to be feasible for BCI repeatedly but has never been tested with the cEEGrid. We found distinct P300 deflections in the cEEGrid data, particularly at vertical bipolar channels. With an average of 63%, the cEEGrid classification accuracy was significantly above the chance level (25%) but significantly lower than the 81% reached with the EEG cap. Likewise, the P300 amplitude was significantly lower (cEEGrid R2-R7: 1.87 μV, Cap Cz: 3.53 μV). These results indicate that a tactile BCI using the cEEGrid could potentially be operated, albeit with lower efficiency. Additionally, participants' somatosensory sensitivity was assessed, but no correlation to the accuracy of either EEG system was shown. Our research contributes to the growing amount of literature comparing the cEEGrid to conventional EEG systems and provides first evidence that the tactile P300 can be recorded behind the ear. A BCI based on a thus simplified EEG system might be more readily accepted by potential end-users, provided the accuracy can be substantially increased, e.g., by training and improved classification.
Collapse
Affiliation(s)
- M. Eidel
- Institute of Psychology, University of Würzburg, Würzburg, Germany
| | | | | | | |
Collapse
|
2
|
Maiseli B, Abdalla AT, Massawe LV, Mbise M, Mkocha K, Nassor NA, Ismail M, Michael J, Kimambo S. Brain-computer interface: trend, challenges, and threats. Brain Inform 2023; 10:20. [PMID: 37540385 PMCID: PMC10403483 DOI: 10.1186/s40708-023-00199-3] [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: 01/05/2023] [Accepted: 07/01/2023] [Indexed: 08/05/2023] Open
Abstract
Brain-computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onwards, exceeding those from the United States that started to decline during the same period. Implications and reasons for this trend are discussed. Furthermore, we have extensively discussed challenges and threats limiting exploitation of BCI capabilities. A typical BCI architecture is hypothesized to address two prominent BCI threats, privacy and security, as an attempt to make the technology commercially viable to the society.
Collapse
Affiliation(s)
- Baraka Maiseli
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania.
| | - Abdi T Abdalla
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Libe V Massawe
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Mercy Mbise
- Department of Computer Science and Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Khadija Mkocha
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Nassor Ally Nassor
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Moses Ismail
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - James Michael
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Samwel Kimambo
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| |
Collapse
|
3
|
Grevet E, Forge K, Tadiello S, Izac M, Amadieu F, Brunel L, Pillette L, Py J, Gasq D, Jeunet-Kelway C. Modeling the acceptability of BCIs for motor rehabilitation after stroke: A large scale study on the general public. FRONTIERS IN NEUROERGONOMICS 2023; 3:1082901. [PMID: 38235470 PMCID: PMC10790937 DOI: 10.3389/fnrgo.2022.1082901] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 12/09/2022] [Indexed: 01/19/2024]
Abstract
Introduction Strokes leave around 40% of survivors dependent in their activities of daily living, notably due to severe motor disabilities. Brain-computer interfaces (BCIs) have been shown to be efficiency for improving motor recovery after stroke, but this efficiency is still far from the level required to achieve the clinical breakthrough expected by both clinicians and patients. While technical levers of improvement have been identified (e.g., sensors and signal processing), fully optimized BCIs are pointless if patients and clinicians cannot or do not want to use them. We hypothesize that improving BCI acceptability will reduce patients' anxiety levels, while increasing their motivation and engagement in the procedure, thereby favoring learning, ultimately, and motor recovery. In other terms, acceptability could be used as a lever to improve BCI efficiency. Yet, studies on BCI based on acceptability/acceptance literature are missing. Thus, our goal was to model BCI acceptability in the context of motor rehabilitation after stroke, and to identify its determinants. Methods The main outcomes of this paper are the following: i) we designed the first model of acceptability of BCIs for motor rehabilitation after stroke, ii) we created a questionnaire to assess acceptability based on that model and distributed it on a sample representative of the general public in France (N = 753, this high response rate strengthens the reliability of our results), iii) we validated the structure of this model and iv) quantified the impact of the different factors on this population. Results Results show that BCIs are associated with high levels of acceptability in the context of motor rehabilitation after stroke and that the intention to use them in that context is mainly driven by the perceived usefulness of the system. In addition, providing people with clear information regarding BCI functioning and scientific relevance had a positive influence on acceptability factors and behavioral intention. Discussion With this paper we propose a basis (model) and a methodology that could be adapted in the future in order to study and compare the results obtained with: i) different stakeholders, i.e., patients and caregivers; ii) different populations of different cultures around the world; and iii) different targets, i.e., other clinical and non-clinical BCI applications.
Collapse
Affiliation(s)
- Elise Grevet
- CNRS, EPHE, INCIA, UMR5287, Université de Bordeaux, Bordeaux, France
| | - Killyam Forge
- CLLE, Université de Toulouse, CNRS, Toulouse, France
| | | | - Margaux Izac
- CNRS, EPHE, INCIA, UMR5287, Université de Bordeaux, Bordeaux, France
| | | | - Lionel Brunel
- Université Paul Valéry Montpellier 3, EPSYLON EA 4556, Montpellier, France
| | - Léa Pillette
- CNRS, EPHE, INCIA, UMR5287, Université de Bordeaux, Bordeaux, France
| | - Jacques Py
- CLLE, Université de Toulouse, CNRS, Toulouse, France
| | - David Gasq
- ToNIC, Université de Toulouse, INSERM, Toulouse, France
- Centre Hospitalier Universitaire Toulouse, Toulouse, France
| | | |
Collapse
|
4
|
Pitt KM, McKelvey M, Weissling K. The perspectives of augmentative and alternative communication experts on the clinical integration of non-invasive brain-computer interfaces. BRAIN-COMPUTER INTERFACES 2022. [DOI: 10.1080/2326263x.2022.2057758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Kevin M. Pitt
- Department of Special Education and Communication Disorders, University of Nebraska–Lincoln, Lincoln, NE, USA
| | - Miechelle McKelvey
- Department of Communication Disorders, University of Nebraska Kearney Kearney, NE, USA
| | - Kristy Weissling
- Department of Special Education and Communication Disorders, University of Nebraska–Lincoln, Lincoln, NE, USA
| |
Collapse
|
5
|
Nagels-Coune L, Riecke L, Benitez-Andonegui A, Klinkhammer S, Goebel R, De Weerd P, Lührs M, Sorger B. See, Hear, or Feel - to Speak: A Versatile Multiple-Choice Functional Near-Infrared Spectroscopy-Brain-Computer Interface Feasible With Visual, Auditory, or Tactile Instructions. Front Hum Neurosci 2021; 15:784522. [PMID: 34899223 PMCID: PMC8656940 DOI: 10.3389/fnhum.2021.784522] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/05/2021] [Indexed: 11/13/2022] Open
Abstract
Severely motor-disabled patients, such as those suffering from the so-called "locked-in" syndrome, cannot communicate naturally. They may benefit from brain-computer interfaces (BCIs) exploiting brain signals for communication and therewith circumventing the muscular system. One BCI technique that has gained attention recently is functional near-infrared spectroscopy (fNIRS). Typically, fNIRS-based BCIs allow for brain-based communication via voluntarily modulation of brain activity through mental task performance guided by visual or auditory instructions. While the development of fNIRS-BCIs has made great progress, the reliability of fNIRS-BCIs across time and environments has rarely been assessed. In the present fNIRS-BCI study, we tested six healthy participants across three consecutive days using a straightforward four-choice fNIRS-BCI communication paradigm that allows answer encoding based on instructions using various sensory modalities. To encode an answer, participants performed a motor imagery task (mental drawing) in one out of four time periods. Answer encoding was guided by either the visual, auditory, or tactile sensory modality. Two participants were tested outside the laboratory in a cafeteria. Answers were decoded from the time course of the most-informative fNIRS channel-by-chromophore combination. Across the three testing days, we obtained mean single- and multi-trial (joint analysis of four consecutive trials) accuracies of 62.5 and 85.19%, respectively. Obtained multi-trial accuracies were 86.11% for visual, 80.56% for auditory, and 88.89% for tactile sensory encoding. The two participants that used the fNIRS-BCI in a cafeteria obtained the best single- (72.22 and 77.78%) and multi-trial accuracies (100 and 94.44%). Communication was reliable over the three recording sessions with multi-trial accuracies of 86.11% on day 1, 86.11% on day 2, and 83.33% on day 3. To gauge the trade-off between number of optodes and decoding accuracy, averaging across two and three promising fNIRS channels was compared to the one-channel approach. Multi-trial accuracy increased from 85.19% (one-channel approach) to 91.67% (two-/three-channel approach). In sum, the presented fNIRS-BCI yielded robust decoding results using three alternative sensory encoding modalities. Further, fNIRS-BCI communication was stable over the course of three consecutive days, even in a natural (social) environment. Therewith, the developed fNIRS-BCI demonstrated high flexibility, reliability and robustness, crucial requirements for future clinical applicability.
Collapse
Affiliation(s)
- Laurien Nagels-Coune
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Maastricht, Netherlands
- Zorggroep Sint-Kamillus, Bierbeek, Belgium
| | - Lars Riecke
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Maastricht, Netherlands
| | - Amaia Benitez-Andonegui
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Maastricht, Netherlands
- MEG Core Facility, National Institutes of Mental Health, Bethesda, MD, United States
| | - Simona Klinkhammer
- Department of Psychiatry and Neuropsychology, Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Maastricht, Netherlands
- Brain Innovation B.V., Maastricht, Netherlands
| | - Peter De Weerd
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Maastricht, Netherlands
- Maastricht Centre for Systems Biology, Maastricht University, Maastricht, Netherlands
| | | | - Bettina Sorger
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Maastricht, Netherlands
| |
Collapse
|
6
|
Zulauf-Czaja A, Al-Taleb MKH, Purcell M, Petric-Gray N, Cloughley J, Vuckovic A. On the way home: a BCI-FES hand therapy self-managed by sub-acute SCI participants and their caregivers: a usability study. J Neuroeng Rehabil 2021; 18:44. [PMID: 33632262 PMCID: PMC7905902 DOI: 10.1186/s12984-021-00838-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 02/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Regaining hand function is the top priority for people with tetraplegia, however access to specialised therapy outwith clinics is limited. Here we present a system for hand therapy based on brain-computer interface (BCI) which uses a consumer grade electroencephalography (EEG) device combined with functional electrical stimulation (FES), and evaluate its usability among occupational therapists (OTs) and people with spinal cord injury (SCI) and their family members. METHODS Users: Eight people with sub-acute SCI (6 M, 2F, age 55.4 ± 15.6) and their caregivers (3 M, 5F, age 45.3 ± 14.3); four OTs (4F, age 42.3 ± 9.8). User Activity: Researchers trained OTs; OTs subsequently taught caregivers to set up the system for the people with SCI to perform hand therapy. Hand therapy consisted of attempted movement (AM) of one hand to lower the power of EEG sensory-motor rhythm in the 8-12 Hz band and thereby activate FES which induced wrist flexion and extension. Technology: Consumer grade wearable EEG, multichannel FES, custom made BCI application. LOCATION Research space within hospital. Evaluation: donning times, BCI accuracy, BCI and FES parameter repeatability, questionnaires, focus groups and interviews. RESULTS Effectiveness: The BCI accuracy was 70-90%. Efficiency: Median donning times decreased from 40.5 min for initial session to 27 min during last training session (N = 7), dropping to 14 min on the last self-managed session (N = 3). BCI and FES parameters were stable from session to session. Satisfaction: Mean satisfaction with the system among SCI users and caregivers was 3.68 ± 0.81 (max 5) as measured by QUEST questionnaire. Main facilitators for implementing BCI-FES technology were "seeing hand moving", "doing something useful for the loved ones", good level of computer literacy (people with SCI and caregivers), "active engagement in therapy" (OT), while main barriers were technical complexity of setup (all groups) and "lack of clinical evidence" (OT). CONCLUSION BCI-FES has potential to be used as at home hand therapy by people with SCI or stroke, provided it is easy to use and support is provided. Transfer of knowledge of operating BCI is possible from researchers to therapists to users and caregivers. Trial registration Registered with NHS GG&C on December 6th 2017; clinicaltrials.gov reference number NCT03257982, url: https://clinicaltrials.gov/ct2/show/NCT03257982 .
Collapse
Affiliation(s)
- Anna Zulauf-Czaja
- Biomedical Engineering Research Division, University of Glasgow, Glasgow, UK.
| | - Manaf K H Al-Taleb
- Biomedical Engineering Research Division, University of Glasgow, Glasgow, UK.,Wasit University, Wasit, Iraq
| | - Mariel Purcell
- Queen Elizabeth National Spinal Injuries Unit, Elizabeth University Hospital, Glasgow, Queen, UK
| | - Nina Petric-Gray
- Biomedical Engineering Research Division, University of Glasgow, Glasgow, UK
| | - Jennifer Cloughley
- Queen Elizabeth National Spinal Injuries Unit, Elizabeth University Hospital, Glasgow, Queen, UK
| | - Aleksandra Vuckovic
- Biomedical Engineering Research Division, University of Glasgow, Glasgow, UK
| |
Collapse
|
7
|
Zaer H, Deshmukh A, Orlowski D, Fan W, Prouvot PH, Glud AN, Jensen MB, Worm ES, Lukacova S, Mikkelsen TW, Fitting LM, Adler JR, Schneider MB, Jensen MS, Fu Q, Go V, Morizio J, Sørensen JCH, Stroh A. An Intracortical Implantable Brain-Computer Interface for Telemetric Real-Time Recording and Manipulation of Neuronal Circuits for Closed-Loop Intervention. Front Hum Neurosci 2021; 15:618626. [PMID: 33613212 PMCID: PMC7887289 DOI: 10.3389/fnhum.2021.618626] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 01/14/2021] [Indexed: 11/13/2022] Open
Abstract
Recording and manipulating neuronal ensemble activity is a key requirement in advanced neuromodulatory and behavior studies. Devices capable of both recording and manipulating neuronal activity brain-computer interfaces (BCIs) should ideally operate un-tethered and allow chronic longitudinal manipulations in the freely moving animal. In this study, we designed a new intracortical BCI feasible of telemetric recording and stimulating local gray and white matter of visual neural circuit after irradiation exposure. To increase the translational reliance, we put forward a Göttingen minipig model. The animal was stereotactically irradiated at the level of the visual cortex upon defining the target by a fused cerebral MRI and CT scan. A fully implantable neural telemetry system consisting of a 64 channel intracortical multielectrode array, a telemetry capsule, and an inductive rechargeable battery was then implanted into the visual cortex to record and manipulate local field potentials, and multi-unit activity. We achieved a 3-month stability of the functionality of the un-tethered BCI in terms of telemetric radio-communication, inductive battery charging, and device biocompatibility for 3 months. Finally, we could reliably record the local signature of sub- and suprathreshold neuronal activity in the visual cortex with high bandwidth without complications. The ability to wireless induction charging combined with the entirely implantable design, the rather high recording bandwidth, and the ability to record and stimulate simultaneously put forward a wireless BCI capable of long-term un-tethered real-time communication for causal preclinical circuit-based closed-loop interventions.
Collapse
Affiliation(s)
- Hamed Zaer
- Department of Neurosurgery, Center for Experimental Neuroscience (CENSE), Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Ashlesha Deshmukh
- Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, NC, United States
- Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC, United States
| | - Dariusz Orlowski
- Department of Neurosurgery, Center for Experimental Neuroscience (CENSE), Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Wei Fan
- Leibniz Institute for Resilience Research, Mainz, Germany
| | | | - Andreas Nørgaard Glud
- Department of Neurosurgery, Center for Experimental Neuroscience (CENSE), Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Morten Bjørn Jensen
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Oncology, Radiation Therapy, and Clinical Medicine, Aarhus University Hospital, Aarhus University, Aarhus, Denmark
| | - Esben Schjødt Worm
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Oncology, Radiation Therapy, and Clinical Medicine, Aarhus University Hospital, Aarhus University, Aarhus, Denmark
| | - Slávka Lukacova
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Oncology, Radiation Therapy, and Clinical Medicine, Aarhus University Hospital, Aarhus University, Aarhus, Denmark
| | - Trine Werenberg Mikkelsen
- Department of Neurosurgery, Center for Experimental Neuroscience (CENSE), Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Lise Moberg Fitting
- Department of Neurosurgery, Center for Experimental Neuroscience (CENSE), Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - John R. Adler
- Zap Surgical Systems, Inc., San Carlos, CA, United States
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
| | - M. Bret Schneider
- Zap Surgical Systems, Inc., San Carlos, CA, United States
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Martin Snejbjerg Jensen
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Nuclear Medicine and PET Center, Institute of Clinical Medicine, Aarhus University and Hospital, Aarhus, Denmark
| | - Quanhai Fu
- Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, NC, United States
| | - Vinson Go
- Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, NC, United States
| | - James Morizio
- Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, NC, United States
| | - Jens Christian Hedemann Sørensen
- Department of Neurosurgery, Center for Experimental Neuroscience (CENSE), Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Albrecht Stroh
- Leibniz Institute for Resilience Research, Mainz, Germany
- Institute of Pathophysiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| |
Collapse
|
8
|
Fontanillo Lopez CA, Li G, Zhang D. Beyond Technologies of Electroencephalography-Based Brain-Computer Interfaces: A Systematic Review From Commercial and Ethical Aspects. Front Neurosci 2020; 14:611130. [PMID: 33390892 PMCID: PMC7773904 DOI: 10.3389/fnins.2020.611130] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 11/13/2020] [Indexed: 01/22/2023] Open
Abstract
The deployment of electroencephalographic techniques for commercial applications has undergone a rapid growth in recent decades. As they continue to expand in the consumer markets as suitable techniques for monitoring the brain activity, their transformative potential necessitates equally significant ethical inquiries. One of the main questions, which arises then when evaluating these kinds of applications, is whether they should be aligned or not with the main ethical concerns reported by scholars and experts. Thus, the present work attempts to unify these disciplines of knowledge by performing a comprehensive scan of the major electroencephalographic market applications as well as their most relevant ethical concerns arising from the existing literature. In this literature review, different databases were consulted, which presented conceptual and empirical discussions and findings about commercial and ethical aspects of electroencephalography. Subsequently, the content was extracted from the articles and the main conclusions were presented. Finally, an external assessment of the outcomes was conducted in consultation with an expert panel in some of the topic areas such as biomedical engineering, biomechatronics, and neuroscience. The ultimate purpose of this review is to provide a genuine insight into the cutting-edge practical attempts at electroencephalography. By the same token, it seeks to highlight the overlap between the market needs and the ethical standards that should govern the deployment of electroencephalographic consumer-grade solutions, providing a practical approach that overcomes the engineering myopia of certain ethical discussions.
Collapse
Affiliation(s)
| | - Guangye Li
- The Robotics Institute, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Dingguo Zhang
- The Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| |
Collapse
|
9
|
Roc A, Pillette L, Mladenovic J, Benaroch C, N'Kaoua B, Jeunet C, Lotte F. A review of user training methods in brain computer interfaces based on mental tasks. J Neural Eng 2020; 18. [PMID: 33181488 DOI: 10.1088/1741-2552/abca17] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 11/12/2020] [Indexed: 12/12/2022]
Abstract
Mental-Tasks based Brain-Computer Interfaces (MT-BCIs) allow their users to interact with an external device solely by using brain signals produced through mental tasks. While MT-BCIs are promising for many applications, they are still barely used outside laboratories due to their lack of reliability. MT-BCIs require their users to develop the ability to self-regulate specific brain signals. However, the human learning process to control a BCI is still relatively poorly understood and how to optimally train this ability is currently under investigation. Despite their promises and achievements, traditional training programs have been shown to be sub-optimal and could be further improved. In order to optimize user training and improve BCI performance, human factors should be taken into account. An interdisciplinary approach should be adopted to provide learners with appropriate and/or adaptive training. In this article, we provide an overview of existing methods for MT-BCI user training - notably in terms of environment, instructions, feedback and exercises. We present a categorization and taxonomy of these training approaches, provide guidelines on how to choose the best methods and identify open challenges and perspectives to further improve MT-BCI user training.
Collapse
Affiliation(s)
| | | | | | - Camille Benaroch
- Inria Centre de recherche Bordeaux Sud-Ouest, Talence, 33405, FRANCE
| | - Bernard N'Kaoua
- Handicap, Activity, Cognition, Health, Inserm / University of Bordeaux, Talence, FRANCE
| | | | | |
Collapse
|
10
|
Benitez-Andonegui A, Burden R, Benning R, Möckel R, Lührs M, Sorger B. An Augmented-Reality fNIRS-Based Brain-Computer Interface: A Proof-of-Concept Study. Front Neurosci 2020; 14:346. [PMID: 32410938 PMCID: PMC7199634 DOI: 10.3389/fnins.2020.00346] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 03/23/2020] [Indexed: 02/04/2023] Open
Abstract
Augmented reality (AR) enhances the user's environment by projecting virtual objects into the real world in real-time. Brain-computer interfaces (BCIs) are systems that enable users to control external devices with their brain signals. BCIs can exploit AR technology to interact with the physical and virtual world and to explore new ways of displaying feedback. This is important for users to perceive and regulate their brain activity or shape their communication intentions while operating in the physical world. In this study, twelve healthy participants were introduced to and asked to choose between two motor-imagery tasks: mental drawing and interacting with a virtual cube. Participants first performed a functional localizer run, which was used to select a single fNIRS channel for decoding their intentions in eight subsequent choice-encoding runs. In each run participants were asked to select one choice of a six-item list. A rotating AR cube was displayed on a computer screen as the main stimulus, where each face of the cube was presented for 6 s and represented one choice of the six-item list. For five consecutive trials, participants were instructed to perform the motor-imagery task when the face of the cube that represented their choice was facing them (therewith temporally encoding the selected choice). In the end of each run, participants were provided with the decoded choice based on a joint analysis of all five trials. If the decoded choice was incorrect, an active error-correction procedure was applied by the participant. The choice list provided in each run was based on the decoded choice of the previous run. The experimental design allowed participants to navigate twice through a virtual menu that consisted of four levels if all choices were correctly decoded. Here we demonstrate for the first time that by using AR feedback and flexible choice encoding in form of search trees, we can increase the degrees of freedom of a BCI system. We also show that participants can successfully navigate through a nested menu and achieve a mean accuracy of 74% using a single motor-imagery task and a single fNIRS channel.
Collapse
Affiliation(s)
- Amaia Benitez-Andonegui
- Department Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
- Laboratory for Cognitive Robotics and Complex Self-Organizing Systems, Department of Data Science and Knowledge Engineering, Faculty of Science and Engineering, Maastricht University, Maastricht, Netherlands
| | - Rodion Burden
- Department Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
| | - Richard Benning
- Instrumentation Engineering, Dean and Directors Office, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Rico Möckel
- Laboratory for Cognitive Robotics and Complex Self-Organizing Systems, Department of Data Science and Knowledge Engineering, Faculty of Science and Engineering, Maastricht University, Maastricht, Netherlands
| | - Michael Lührs
- Department Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
- Research Department, Brain Innovation B.V., Maastricht, Netherlands
| | - Bettina Sorger
- Department Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
| |
Collapse
|
11
|
Allison BZ, Kübler A, Jin J. 30+ years of P300 brain-computer interfaces. Psychophysiology 2020; 57:e13569. [PMID: 32301143 DOI: 10.1111/psyp.13569] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 01/07/2020] [Accepted: 01/20/2020] [Indexed: 11/28/2022]
Abstract
Brain-computer interfaces (BCIs) directly measure brain activity with no physical movement and translate the neural signals into messages. BCIs that employ the P300 event-related brain potential often have used the visual modality. The end user is presented with flashing stimuli that indicate selections for communication, control, or both. Counting each flash that corresponds to a specific target selection while ignoring other flashes will elicit P300s to only the target selection. P300 BCIs also have been implemented using auditory or tactile stimuli. P300 BCIs have been used with a variety of applications for severely disabled end users in their homes without frequent expert support. P300 BCI research and development has made substantial progress, but challenges remain before these tools can become practical devices for impaired patients and perhaps healthy people.
Collapse
Affiliation(s)
- Brendan Z Allison
- Cognitive Science Department, University of California at San Diego, La Jolla, CA, USA
| | - Andrea Kübler
- Psychology Department, University of Würzburg, Würzburg, Germany
| | - Jing Jin
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, P.R. China
| |
Collapse
|
12
|
Abstract
Locked-in syndrome (LIS) is characterized by an inability to move or speak in the presence of intact cognition and can be caused by brainstem trauma or neuromuscular disease. Quality of life (QoL) in LIS is strongly impaired by the inability to communicate, which cannot always be remedied by traditional augmentative and alternative communication (AAC) solutions if residual muscle activity is insufficient to control the AAC device. Brain-computer interfaces (BCIs) may offer a solution by employing the person's neural signals instead of relying on muscle activity. Here, we review the latest communication BCI research using noninvasive signal acquisition approaches (electroencephalography, functional magnetic resonance imaging, functional near-infrared spectroscopy) and subdural and intracortical implanted electrodes, and we discuss current efforts to translate research knowledge into usable BCI-enabled communication solutions that aim to improve the QoL of individuals with LIS.
Collapse
|
13
|
Huggins JE, Guger C, Aarnoutse E, Allison B, Anderson CW, Bedrick S, Besio W, Chavarriaga R, Collinger JL, Do AH, Herff C, Hohmann M, Kinsella M, Lee K, Lotte F, Müller-Putz G, Nijholt A, Pels E, Peters B, Putze F, Rupp R, Schalk G, Scott S, Tangermann M, Tubig P, Zander T. Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation. BRAIN-COMPUTER INTERFACES 2019; 6:71-101. [PMID: 33033729 PMCID: PMC7539697 DOI: 10.1080/2326263x.2019.1697163] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 10/30/2019] [Indexed: 12/11/2022]
Abstract
The Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics in BCI (also called brain-machine interface) research. Workshops covered foundational topics such as hardware development and signal analysis algorithms, new and imaginative topics such as BCI for virtual reality and multi-brain BCIs, and translational topics such as clinical applications and ethical assumptions of BCI development. BCI research is expanding in the diversity of applications and populations for whom those applications are being developed. BCI applications are moving toward clinical readiness as researchers struggle with the practical considerations to make sure that BCI translational efforts will be successful. This paper summarizes each workshop, providing an overview of the topic of discussion, references for additional information, and identifying future issues for research and development that resulted from the interactions and discussion at the workshop.
Collapse
Affiliation(s)
- Jane E Huggins
- Department of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan, United States, 325 East Eisenhower, Room 3017; Ann Arbor, Michigan 48108-5744
| | - Christoph Guger
- g.tec medical engineering GmbH/Guger Technologies OG, Austria, Sierningstrasse 14, 4521 Schiedlberg, Austria
| | - Erik Aarnoutse
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Brendan Allison
- Dept. of Cognitive Science, Mail Code 0515, University of California at San Diego, La Jolla, United States
| | - Charles W Anderson
- Department of Computer Science, Molecular, Cellular and Integrative Neurosience Program, Colorado State University, Fort Collins, CO 80523
| | - Steven Bedrick
- Center for Spoken Language Understanding, Oregon Health & Science University, Portland, OR 97239
| | - Walter Besio
- Department of Electrical, Computer, & Biomedical Engineering and Interdisciplinary Neuroscience Program, University of Rhode Island, Kingston, Rhode Island, USA, CREmedical Corp. Kingston, Rhode Island, USA
| | - Ricardo Chavarriaga
- Defitech Chair in Brain-Machine Interface (CNBI), Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne - EPFL, Switzerland
| | - Jennifer L Collinger
- University of Pittsburgh, Department of Physical Medicine and Rehabilitation, VA Pittsburgh Healthcare System, Department of Veterans Affairs, 3520 5th Ave, Pittsburgh, PA, 15213
| | - An H Do
- UC Irvine Brain Computer Interface Lab, Department of Neurology, University of California, Irvine
| | - Christian Herff
- School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Matthias Hohmann
- Max Planck Institute for Intelligent Systems, Department for Empirical Inference, Max-Planck-Ring 4, 72074 Tübingen, Germany
| | - Michelle Kinsella
- Oregon Health & Science University, Institute on Development & Disability, 707 SW Gaines St, #1290, Portland, OR 97239
| | - Kyuhwa Lee
- Swiss Federal Institute of Technology in Lausanne-EPFL
| | - Fabien Lotte
- Inria Bordeaux Sud-Ouest, LaBRI (Univ. Bordeaux/CNRS/Bordeaux INP), 200 avenue de la vieille tour, 33405, Talence Cedex, France
| | | | - Anton Nijholt
- Faculty EEMCS, University of Twente, Enschede, The Netherlands
| | - Elmar Pels
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Betts Peters
- Oregon Health & Science University, Institute on Development & Disability, 707 SW Gaines St, #1290, Portland, OR 97239
| | - Felix Putze
- University of Bremen, Germany, Cognitive Systems Lab, University of Bremen, Enrique-Schmidt-Straße 5 (Cartesium), 28359 Bremen
| | - Rüdiger Rupp
- Spinal Cord Injury Center, Heidelberg University Hospital
| | - Gerwin Schalk
- National Center for Adaptive Neurotechnologies, Wadsworth Center, NYS Dept. of Health, Dept. of Neurology, Albany Medical College, Dept. of Biomed. Sci., State Univ. of New York at Albany, Center for Medical Sciences 2003, 150 New Scotland Avenue, Albany, New York 12208
| | - Stephanie Scott
- Department of Media Communications, Colorado State University, Fort Collins, CO 80523
| | - Michael Tangermann
- Brain State Decoding Lab, Cluster of Excellence BrainLinks-BrainTools, Computer Science Dept., University of Freiburg, Germany, Autonomous Intelligent Systems Lab, Computer Science Dept., University of Freiburg, Germany
| | - Paul Tubig
- Department of Philosophy, Center for Neurotechnology, University of Washington, Savery Hall, Room 361, Seattle, WA 98195
| | - Thorsten Zander
- Team PhyPA, Biological Psychology and Neuroergonomics, Technische Universität Berlin, Berlin, Germany, 7 Zander Laboratories B.V., Amsterdam, The Netherlands
| |
Collapse
|
14
|
Salazar-Ramirez A, Martin JI, Martinez R, Arruti A, Muguerza J, Sierra B. A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface. PLoS One 2019; 14:e0218181. [PMID: 31211812 PMCID: PMC6581259 DOI: 10.1371/journal.pone.0218181] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 05/28/2019] [Indexed: 11/19/2022] Open
Abstract
A brain-computer interface (BCI), based on motor imagery EEG, uses information extracted from the electroencephalography signals generated by a person who intends to perform any action. One of the most important issues of current research is how to detect automatically whether the user intends to send some message to a certain device. This study presents a proposal, based on a hierarchical structured system, for recognising intentional and non-intentional mental tasks on a BCI system by applying machine learning techniques to the EEG signals. First-level clustering is performed to distinguish between intentional control (IC) and non-intentional control (NC) state patterns. Then, the patterns recognised as IC are passed on to a second stage where supervised learning techniques are used to classify them. In BCI applications, it is critical to correctly classify NC states with a low false positive rate (FPR) to avoid undesirable effects. According to the literature, we selected a maximum FPR of 10%. Under these conditions, our proposal achieved an average test accuracy of 66.6%, with an 8.2% FPR, for the BCI competition IIIa dataset. The main contribution of this paper is the hierarchical approach, based on machine learning paradigms, which performs intentional and non-intentional discrimination and, depending on the case, classifies the intended command selected by the user.
Collapse
Affiliation(s)
- Asier Salazar-Ramirez
- Department of Computer Architecture and Technology, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain
| | - Jose I. Martin
- Department of Computer Architecture and Technology, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain
- * E-mail:
| | - Raquel Martinez
- Department of System Engineering and Automation, University of the Basque Country (UPV/EHU), Bilbao, Spain
| | - Andoni Arruti
- Department of Computer Architecture and Technology, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain
| | - Javier Muguerza
- Department of Computer Architecture and Technology, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain
| | - Basilio Sierra
- Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain
| |
Collapse
|
15
|
Costa AP, Møller JS, Iversen HK, Puthusserypady S. An adaptive CSP filter to investigate user independence in a 3-class MI-BCI paradigm. Comput Biol Med 2018; 103:24-33. [PMID: 30336362 DOI: 10.1016/j.compbiomed.2018.09.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 09/20/2018] [Accepted: 09/24/2018] [Indexed: 01/01/2023]
Abstract
This paper describes the implementation of a Brain Computer Interface (BCI) scheme using a common spatial patterns (CSP) filter in combination with a Recursive Least Squares (RLS) approach to iteratively update the coefficients of the CSP filter. The proposed adaptive CSP (ACSP) algorithm is made more robust by introducing regularization using Diagonal Loading (DL), and thus will be able to significantly reduce the length of training sessions when introducing new patients to the BCI system. The system is tested on a 4-class multi-limb motor imagery (MI) data set from the BCI competition IV (2a), and a more complex single limb 3-class MI dataset recorded in-house. The latter dataset is produced to mimic an upper limb rehabilitation session, e.g., after stroke. The findings indicate that when extensive calibration data is available, the ACSP performs comparably to the CSP (kappa value of 0.523 and 0.502, respectively, for the 4-class problem); for reduced calibration sessions, the ACSP significantly improved the performance of the system (up to 4-fold). The proposed paradigm proved feasible and the ACSP algorithm seems to enable a user or semi user independent scenario, where the need for long system calibration sessions without feedback is eliminated.
Collapse
Affiliation(s)
- Ana P Costa
- Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, 2800, Denmark.
| | - Jakob S Møller
- Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, 2800, Denmark.
| | - Helle K Iversen
- Department of Neurology, Rigshospitalet, Glostrup, 2600, Denmark.
| | - Sadasivan Puthusserypady
- Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, 2800, Denmark.
| |
Collapse
|
16
|
Tbalvandany SS, Harhangi BS, Prins AW, Schermer MHN. Embodiment in Neuro-engineering Endeavors: Phenomenological Considerations and Practical Implications. NEUROETHICS-NETH 2018. [DOI: 10.1007/s12152-018-9383-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
17
|
Taherian S, Davies TC. Caregiver and special education staff perspectives of a commercial brain-computer interface as access technology: a qualitative study. BRAIN-COMPUTER INTERFACES 2018. [DOI: 10.1080/2326263x.2018.1505191] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Sarvnaz Taherian
- School of Psychology, University of Auckland, Auckland, New Zealand
| | - T. Claire Davies
- Faculty of Engineering and Applied Science, Queens University, Kingston, Ontario, Canada
| |
Collapse
|
18
|
Riccio A, Schettini F, Simione L, Pizzimenti A, Inghilleri M, Olivetti-Belardinelli M, Mattia D, Cincotti F. On the Relationship Between Attention Processing and P300-Based Brain Computer Interface Control in Amyotrophic Lateral Sclerosis. Front Hum Neurosci 2018; 12:165. [PMID: 29892218 PMCID: PMC5985322 DOI: 10.3389/fnhum.2018.00165] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 04/09/2018] [Indexed: 12/13/2022] Open
Abstract
Our objective was to investigate the capacity to control a P3-based brain-computer interface (BCI) device for communication and its related (temporal) attention processing in a sample of amyotrophic lateral sclerosis (ALS) patients with respect to healthy subjects. The ultimate goal was to corroborate the role of cognitive mechanisms in event-related potential (ERP)-based BCI control in ALS patients. Furthermore, the possible differences in such attentional mechanisms between the two groups were investigated in order to unveil possible alterations associated with the ALS condition. Thirteen ALS patients and 13 healthy volunteers matched for age and years of education underwent a P3-speller BCI task and a rapid serial visual presentation (RSVP) task. The RSVP task was performed by participants in order to screen their temporal pattern of attentional resource allocation, namely: (i) the temporal attentional filtering capacity (scored as T1%); and (ii) the capability to adequately update the attentive filter in the temporal dynamics of the attentional selection (scored as T2%). For the P3-speller BCI task, the online accuracy and information transfer rate (ITR) were obtained. Centroid Latency and Mean Amplitude of N200 and P300 were also obtained. No significant differences emerged between ALS patients and Controls with regards to online accuracy (p = 0.13). Differently, the performance in controlling the P3-speller expressed as ITR values (calculated offline) were compromised in ALS patients (p < 0.05), with a delay in the latency of P3 when processing BCI stimuli as compared with Control group (p < 0.01). Furthermore, the temporal aspect of attentional filtering which was related to BCI control (r = 0.51; p < 0.05) and to the P3 wave amplitude (r = 0.63; p < 0.05) was also altered in ALS patients (p = 0.01). These findings ground the knowledge required to develop sensible classes of BCI specifically designed by taking into account the influence of the cognitive characteristics of the possible candidates in need of a BCI system for communication.
Collapse
Affiliation(s)
- Angela Riccio
- Neuroelectrical Imaging and BCI Laboratory, NeiLab, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Francesca Schettini
- Servizio Ausilioteca per Riabilitazione Assistita con Tecnologia (SARA-t), Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Luca Simione
- Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy
| | | | - Maurizio Inghilleri
- Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, Italy
| | - Marta Olivetti-Belardinelli
- Centro Interuniversitario di Ricerca sull'Elaborazione Cognitiva in Sistemi Naturali e Artificiali (ECoNA), Rome, Italy.,ECONA Interuniversity Centre for Reseach on Natural and Artificial Systems, Sapienza University of Rome, Rome, Italy
| | - Donatella Mattia
- Neuroelectrical Imaging and BCI Laboratory, NeiLab, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Febo Cincotti
- Neuroelectrical Imaging and BCI Laboratory, NeiLab, Fondazione Santa Lucia (IRCCS), Rome, Italy.,Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| |
Collapse
|
19
|
Ienca M, Kressig RW, Jotterand F, Elger B. Proactive Ethical Design for Neuroengineering, Assistive and Rehabilitation Technologies: the Cybathlon Lesson. J Neuroeng Rehabil 2017; 14:115. [PMID: 29137639 PMCID: PMC5686808 DOI: 10.1186/s12984-017-0325-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Accepted: 10/30/2017] [Indexed: 01/08/2023] Open
Abstract
Background Rapid advancements in rehabilitation science and the widespread application of engineering techniques are opening the prospect of a new phase of clinical and commercial maturity for Neuroengineering, Assistive and Rehabilitation Technologies (NARTs). As the field enters this new phase, there is an urgent need to address and anticipate the ethical implications associated with novel technological opportunities, clinical solutions, and social applications. Main idea In this paper we review possible approaches to the ethics of NART, and propose a framework for ethical design and development, which we call the Proactive Ethical Design (PED) framework. Conclusion A viable ethical framework for neuroengineering, assistive and rehabilitation technology should be characterized by the convergence of user-centered and value-sensitive approaches to product design through a proactive mode of ethical evaluation. We propose four basic normative requirements for the realization of this framework: minimization of power imbalances, compliance with biomedical ethics, translationality and social awareness. The aims and values of the CYBATHLON competition provide an operative model of this ethical framework and could drive an ethical shift in neuroengineering and rehabilitation.
Collapse
Affiliation(s)
- Marcello Ienca
- Institute for Biomedical Ethics, Faculty of Medicine, University of Basel, Bernoullistrasse 28, -4056, Basel, CH, Switzerland. .,Health Ethics & Policy Lab, Department of Health Sciences & Technology, ETH Zürich, Zürich, Switzerland.
| | - Reto W Kressig
- University Center for Medicine of Aging, Felix Platter Hospital, Basel, Switzerland.,Chair of Geriatrics, University of Basel, Basel, Switzerland
| | - Fabrice Jotterand
- Institute for Biomedical Ethics, Faculty of Medicine, University of Basel, Bernoullistrasse 28, -4056, Basel, CH, Switzerland.,Center for Bioethics and Medical Humanities, Institute for Health and Society, Medical College of Wisconsin, Madison, USA
| | - Bernice Elger
- Institute for Biomedical Ethics, Faculty of Medicine, University of Basel, Bernoullistrasse 28, -4056, Basel, CH, Switzerland.,University Center for Legal Medicine, University of Geneva, Geneva, Switzerland
| |
Collapse
|
20
|
Burwell S, Sample M, Racine E. Ethical aspects of brain computer interfaces: a scoping review. BMC Med Ethics 2017; 18:60. [PMID: 29121942 PMCID: PMC5680604 DOI: 10.1186/s12910-017-0220-y] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 10/31/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Brain-Computer Interface (BCI) is a set of technologies that are of increasing interest to researchers. BCI has been proposed as assistive technology for individuals who are non-communicative or paralyzed, such as those with amyotrophic lateral sclerosis or spinal cord injury. The technology has also been suggested for enhancement and entertainment uses, and there are companies currently marketing BCI devices for those purposes (e.g., gaming) as well as health-related purposes (e.g., communication). The unprecedented direct connection created by BCI between human brains and computer hardware raises various ethical, social, and legal challenges that merit further examination and discussion. METHODS To identify and characterize the key issues associated with BCI use, we performed a scoping review of biomedical ethics literature, analyzing the ethics concerns cited across multiple disciplines, including philosophy and medicine. RESULTS Based on this investigation, we report that BCI research and its potential translation to therapeutic intervention generate significant ethical, legal, and social concerns, notably with regards to personhood, stigma, autonomy, privacy, research ethics, safety, responsibility, and justice. Our review of the literature determined, furthermore, that while these issues have been enumerated extensively, few concrete recommendations have been expressed. CONCLUSIONS We conclude that future research should focus on remedying a lack of practical solutions to the ethical challenges of BCI, alongside the collection of empirical data on the perspectives of the public, BCI users, and BCI researchers.
Collapse
Affiliation(s)
- Sasha Burwell
- Neuroethics Research Unit, Institut de recherches cliniques de Montréal, 110 avenue des Pins Ouest, H2W lR7, Montréal, QC, Canada
| | - Matthew Sample
- Neuroethics Research Unit, Institut de recherches cliniques de Montréal, 110 avenue des Pins Ouest, H2W lR7, Montréal, QC, Canada.,Departments of Neurology and Neurosurgery, Experimental Medicine and Biomedical Ethics Unit, McGill University, 110 avenue des Pins Ouest, H2W lR7, Montréal, QC, Canada
| | - Eric Racine
- Neuroethics Research Unit, Institut de recherches cliniques de Montréal, 110 avenue des Pins Ouest, H2W lR7, Montréal, QC, Canada. .,Department of Experimental Medicine, McGill University, Montréal, Canada. .,Department of Medicine and Department of Social and Preventative Medicine, Université de Montréal, 110 avenue des Pins Ouest, H2W lR7, Montréal, QC, Canada. .,Departments of Neurology and Neurosurgery, Experimental Medicine and Biomedical Ethics Unit, McGill University, 110 avenue des Pins Ouest, H2W lR7, Montréal, QC, Canada.
| |
Collapse
|
21
|
Vansteensel MJ, Kristo G, Aarnoutse EJ, Ramsey NF. The brain-computer interface researcher’s questionnaire: from research to application. BRAIN-COMPUTER INTERFACES 2017. [DOI: 10.1080/2326263x.2017.1366237] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- M. J. Vansteensel
- Department of Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - G. Kristo
- Department of Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - E. J. Aarnoutse
- Department of Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - N. F. Ramsey
- Department of Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
22
|
Pels EGM, Aarnoutse EJ, Ramsey NF, Vansteensel MJ. Estimated Prevalence of the Target Population for Brain-Computer Interface Neurotechnology in the Netherlands. Neurorehabil Neural Repair 2017; 31:677-685. [PMID: 28639486 PMCID: PMC6396873 DOI: 10.1177/1545968317714577] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND People who suffer from paralysis have difficulties participating in society. Particularly burdensome is the locked-in syndrome (LIS). LIS patients are not able to move and speak but are cognitively healthy. They rely on assistive technology to interact with the world and may benefit from neurotechnological advances. Optimal research and design of such aids requires a well-defined target population. However, the LIS population is poorly characterized and the number of patients in this condition is unknown. OBJECTIVE Here we estimated and described the LIS patient population in the Netherlands to define the target population for assistive (neuro)technology. METHODS We asked physicians in the Netherlands if they had patients suffering from severe paralysis and communication problems in their files. Physicians responding affirmatively were asked to fill out a questionnaire on the patients' status. RESULTS We sent out 9570 letters to general practitioners (GPs), who reported 83 patients. After first screening, the GPs of 46 patients received the questionnaire. Based on the responses, 26 patients were classified as having LIS. Extrapolation of these numbers resulted in a prevalence of 0.73 patients per 100 000 inhabitants. Notable results from the questionnaire were the percentage of patients with neuromuscular disease (>50%) and living at home (>70%). CONCLUSIONS We revealed an etiologically diverse group of LIS patients. The functioning and needs of these patients were, however, similar and many relied on assistive technology. By characterizing the LIS population, our study may contribute to optimal development of assistive (neuro)technology.
Collapse
Affiliation(s)
- Elmar G M Pels
- 1 Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Erik J Aarnoutse
- 1 Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Nick F Ramsey
- 1 Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mariska J Vansteensel
- 1 Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| |
Collapse
|
23
|
Stahl BC, Wakunuma K, Rainey S, Hansen C. Improving brain computer interface research through user involvement - The transformative potential of integrating civil society organisations in research projects. PLoS One 2017; 12:e0171818. [PMID: 28207882 PMCID: PMC5313172 DOI: 10.1371/journal.pone.0171818] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 01/26/2017] [Indexed: 11/18/2022] Open
Abstract
Research on Brain Computer Interfaces (BCI) often aims to provide solutions for vulnerable populations, such as individuals with diseases, conditions or disabilities that keep them from using traditional interfaces. Such research thereby contributes to the public good. This contribution to the public good corresponds to a broader drive of research and funding policy that focuses on promoting beneficial societal impact. One way of achieving this is to engage with the public. In practical terms this can be done by integrating civil society organisations (CSOs) in research. The open question at the heart of this paper is whether and how such CSO integration can transform the research and contribute to the public good. To answer this question the paper describes five detailed qualitative case studies of research projects including CSOs. The paper finds that transformative impact of CSO integration is possible but by no means assured. It provides recommendations on how transformative impact can be promoted.
Collapse
Affiliation(s)
- Bernd Carsten Stahl
- Centre for Computing and Social Responsibility, School of Computer Science and Informatics, De Montfort University, Leicester, United Kingdom
| | - Kutoma Wakunuma
- Centre for Computing and Social Responsibility, School of Computer Science and Informatics, De Montfort University, Leicester, United Kingdom
| | - Stephen Rainey
- Centre for Computing and Social Responsibility, School of Computer Science and Informatics, De Montfort University, Leicester, United Kingdom
| | - Christian Hansen
- Centre for Computing and Social Responsibility, School of Computer Science and Informatics, De Montfort University, Leicester, United Kingdom
| |
Collapse
|
24
|
Vansteensel MJ, Pels EGM, Bleichner MG, Branco MP, Denison T, Freudenburg ZV, Gosselaar P, Leinders S, Ottens TH, Van Den Boom MA, Van Rijen PC, Aarnoutse EJ, Ramsey NF. Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS. N Engl J Med 2016; 375:2060-2066. [PMID: 27959736 PMCID: PMC5326682 DOI: 10.1056/nejmoa1608085] [Citation(s) in RCA: 286] [Impact Index Per Article: 35.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Options for people with severe paralysis who have lost the ability to communicate orally are limited. We describe a method for communication in a patient with late-stage amyotrophic lateral sclerosis (ALS), involving a fully implanted brain-computer interface that consists of subdural electrodes placed over the motor cortex and a transmitter placed subcutaneously in the left side of the thorax. By attempting to move the hand on the side opposite the implanted electrodes, the patient accurately and independently controlled a computer typing program 28 weeks after electrode placement, at the equivalent of two letters per minute. The brain-computer interface offered autonomous communication that supplemented and at times supplanted the patient's eye-tracking device. (Funded by the Government of the Netherlands and the European Union; ClinicalTrials.gov number, NCT02224469 .).
Collapse
Affiliation(s)
- Mariska J Vansteensel
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (M.J.V., E.G.M.P., M.P.B., Z.V.F., P.G., S.L., M.A.V.D.B., P.C.V.R., E.J.A., N.F.R.), and the Department of Anesthesiology (T.H.O.), University Medical Center Utrecht, Utrecht, the Netherlands; the Neuropsychology Laboratory, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany (M.G.B.); and Neuromodulation Core Technology, Medtronic, Minneapolis (T.D.)
| | - Elmar G M Pels
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (M.J.V., E.G.M.P., M.P.B., Z.V.F., P.G., S.L., M.A.V.D.B., P.C.V.R., E.J.A., N.F.R.), and the Department of Anesthesiology (T.H.O.), University Medical Center Utrecht, Utrecht, the Netherlands; the Neuropsychology Laboratory, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany (M.G.B.); and Neuromodulation Core Technology, Medtronic, Minneapolis (T.D.)
| | - Martin G Bleichner
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (M.J.V., E.G.M.P., M.P.B., Z.V.F., P.G., S.L., M.A.V.D.B., P.C.V.R., E.J.A., N.F.R.), and the Department of Anesthesiology (T.H.O.), University Medical Center Utrecht, Utrecht, the Netherlands; the Neuropsychology Laboratory, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany (M.G.B.); and Neuromodulation Core Technology, Medtronic, Minneapolis (T.D.)
| | - Mariana P Branco
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (M.J.V., E.G.M.P., M.P.B., Z.V.F., P.G., S.L., M.A.V.D.B., P.C.V.R., E.J.A., N.F.R.), and the Department of Anesthesiology (T.H.O.), University Medical Center Utrecht, Utrecht, the Netherlands; the Neuropsychology Laboratory, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany (M.G.B.); and Neuromodulation Core Technology, Medtronic, Minneapolis (T.D.)
| | - Timothy Denison
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (M.J.V., E.G.M.P., M.P.B., Z.V.F., P.G., S.L., M.A.V.D.B., P.C.V.R., E.J.A., N.F.R.), and the Department of Anesthesiology (T.H.O.), University Medical Center Utrecht, Utrecht, the Netherlands; the Neuropsychology Laboratory, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany (M.G.B.); and Neuromodulation Core Technology, Medtronic, Minneapolis (T.D.)
| | - Zachary V Freudenburg
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (M.J.V., E.G.M.P., M.P.B., Z.V.F., P.G., S.L., M.A.V.D.B., P.C.V.R., E.J.A., N.F.R.), and the Department of Anesthesiology (T.H.O.), University Medical Center Utrecht, Utrecht, the Netherlands; the Neuropsychology Laboratory, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany (M.G.B.); and Neuromodulation Core Technology, Medtronic, Minneapolis (T.D.)
| | - Peter Gosselaar
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (M.J.V., E.G.M.P., M.P.B., Z.V.F., P.G., S.L., M.A.V.D.B., P.C.V.R., E.J.A., N.F.R.), and the Department of Anesthesiology (T.H.O.), University Medical Center Utrecht, Utrecht, the Netherlands; the Neuropsychology Laboratory, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany (M.G.B.); and Neuromodulation Core Technology, Medtronic, Minneapolis (T.D.)
| | - Sacha Leinders
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (M.J.V., E.G.M.P., M.P.B., Z.V.F., P.G., S.L., M.A.V.D.B., P.C.V.R., E.J.A., N.F.R.), and the Department of Anesthesiology (T.H.O.), University Medical Center Utrecht, Utrecht, the Netherlands; the Neuropsychology Laboratory, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany (M.G.B.); and Neuromodulation Core Technology, Medtronic, Minneapolis (T.D.)
| | - Thomas H Ottens
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (M.J.V., E.G.M.P., M.P.B., Z.V.F., P.G., S.L., M.A.V.D.B., P.C.V.R., E.J.A., N.F.R.), and the Department of Anesthesiology (T.H.O.), University Medical Center Utrecht, Utrecht, the Netherlands; the Neuropsychology Laboratory, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany (M.G.B.); and Neuromodulation Core Technology, Medtronic, Minneapolis (T.D.)
| | - Max A Van Den Boom
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (M.J.V., E.G.M.P., M.P.B., Z.V.F., P.G., S.L., M.A.V.D.B., P.C.V.R., E.J.A., N.F.R.), and the Department of Anesthesiology (T.H.O.), University Medical Center Utrecht, Utrecht, the Netherlands; the Neuropsychology Laboratory, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany (M.G.B.); and Neuromodulation Core Technology, Medtronic, Minneapolis (T.D.)
| | - Peter C Van Rijen
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (M.J.V., E.G.M.P., M.P.B., Z.V.F., P.G., S.L., M.A.V.D.B., P.C.V.R., E.J.A., N.F.R.), and the Department of Anesthesiology (T.H.O.), University Medical Center Utrecht, Utrecht, the Netherlands; the Neuropsychology Laboratory, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany (M.G.B.); and Neuromodulation Core Technology, Medtronic, Minneapolis (T.D.)
| | - Erik J Aarnoutse
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (M.J.V., E.G.M.P., M.P.B., Z.V.F., P.G., S.L., M.A.V.D.B., P.C.V.R., E.J.A., N.F.R.), and the Department of Anesthesiology (T.H.O.), University Medical Center Utrecht, Utrecht, the Netherlands; the Neuropsychology Laboratory, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany (M.G.B.); and Neuromodulation Core Technology, Medtronic, Minneapolis (T.D.)
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (M.J.V., E.G.M.P., M.P.B., Z.V.F., P.G., S.L., M.A.V.D.B., P.C.V.R., E.J.A., N.F.R.), and the Department of Anesthesiology (T.H.O.), University Medical Center Utrecht, Utrecht, the Netherlands; the Neuropsychology Laboratory, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany (M.G.B.); and Neuromodulation Core Technology, Medtronic, Minneapolis (T.D.)
| |
Collapse
|
25
|
Abstract
Brain-computer interfaces are systems that use signals recorded from the brain to enable communication and control applications for individuals who have impaired function. This technology has developed to the point that it is now being used by individuals who can actually benefit from it. However, there are several outstanding issues that prevent widespread use. These include the ease of obtaining high-quality recordings by home users, the speed, and accuracy of current devices and adapting applications to the needs of the user. In this chapter, we discuss some of these unsolved issues.
Collapse
|
26
|
Riccio A, Pichiorri F, Schettini F, Toppi J, Risetti M, Formisano R, Molinari M, Astolfi L, Cincotti F, Mattia D. Interfacing brain with computer to improve communication and rehabilitation after brain damage. PROGRESS IN BRAIN RESEARCH 2016; 228:357-87. [PMID: 27590975 DOI: 10.1016/bs.pbr.2016.04.018] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Communication and control of the external environment can be provided via brain-computer interfaces (BCIs) to replace a lost function in persons with severe diseases and little or no chance of recovery of motor abilities (ie, amyotrophic lateral sclerosis, brainstem stroke). BCIs allow to intentionally modulate brain activity, to train specific brain functions, and to control prosthetic devices, and thus, this technology can also improve the outcome of rehabilitation programs in persons who have suffered from a central nervous system injury (ie, stroke leading to motor or cognitive impairment). Overall, the BCI researcher is challenged to interact with people with severe disabilities and professionals in the field of neurorehabilitation. This implies a deep understanding of the disabled condition on the one hand, and it requires extensive knowledge on the physiology and function of the human brain on the other. For these reasons, a multidisciplinary approach and the continuous involvement of BCI users in the design, development, and testing of new systems are desirable. In this chapter, we will focus on noninvasive EEG-based systems and their clinical applications, highlighting crucial issues to foster BCI translation outside laboratories to eventually become a technology usable in real-life realm.
Collapse
Affiliation(s)
- A Riccio
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - F Pichiorri
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy; Sapienza University of Rome, Rome, Italy
| | - F Schettini
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - J Toppi
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy; Sapienza University of Rome, Rome, Italy
| | - M Risetti
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - R Formisano
- Post-Coma Unit, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - M Molinari
- Spinal Cord Unit, IRCCS Santa Lucia Foundation, Rome, Italy
| | - L Astolfi
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy; Sapienza University of Rome, Rome, Italy
| | - F Cincotti
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy; Sapienza University of Rome, Rome, Italy
| | - D Mattia
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy.
| |
Collapse
|
27
|
Kübler A, Müller-Putz G, Mattia D. User-centred design in brain–computer interface research and development. Ann Phys Rehabil Med 2015; 58:312-4. [DOI: 10.1016/j.rehab.2015.06.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2015] [Revised: 05/22/2015] [Accepted: 06/12/2015] [Indexed: 10/23/2022]
|
28
|
Luauté J, Laffont I. BCIs and physical medicine and rehabilitation: The future is now. Ann Phys Rehabil Med 2015; 58:1-2. [DOI: 10.1016/j.rehab.2014.12.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2014] [Revised: 12/18/2014] [Accepted: 12/18/2014] [Indexed: 10/24/2022]
|