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Rezvani S, Hosseini-Zahraei SH, Tootchi A, Guger C, Chaibakhsh Y, Saberi A, Chaibakhsh A. A review on the performance of brain-computer interface systems used for patients with locked-in and completely locked-in syndrome. Cogn Neurodyn 2024; 18:1419-1443. [PMID: 39104673 PMCID: PMC11297882 DOI: 10.1007/s11571-023-09995-3] [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: 03/10/2023] [Revised: 06/28/2023] [Accepted: 07/30/2023] [Indexed: 08/07/2024] Open
Abstract
Patients with locked-in syndrome (LIS) and complete locked-in syndrome (CLIS) own a fully functional brain restricted within a non-functional body. In order to help LIS patients stay connected with their surroundings, brain-computer interfaces (BCIs) and related technologies have emerged. BCIs translate brain activity into actions that can be performed by external devices enabling LIS patients to communicate, leading to an increase in their quality of life. The past decade has seen the rapid development of BCIs that have the potential to be used for patients with locked-in syndrome, from which a great deal is tested only on healthy subjects and not on actual patients. This study aims to (1) provide the readers with a comprehensive study that contributes to this growing area of research by exploring the performance of BCIs tested specifically on LIS and CLIS patients, (2) give an overview of different modalities and paradigms used in different stages of the locked-in syndrome, and (3) discuss the contributions and limitations of BCIs introduced for the LIS and CLIS patients in the state-of-the-art and lay a groundwork for researchers interested in this field.
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Affiliation(s)
- Sanaz Rezvani
- Department of Mechanical Engineering, University, University of Guilan, Campus 2, Rasht, 41447-84475 Guilan Iran
- Intelligent Systems and Advanced Control Lab, University of Guilan, Rasht, 41938-13776 Guilan Iran
| | | | - Amirreza Tootchi
- Department of Mechanical & Energy Engineering, Indiana University - Purdue University Indianapolis (IUPUI), 723 W Michigan Street, Indianapolis, IN 46202 USA
| | | | - Yasmin Chaibakhsh
- Department of Cardiac Anesthesia, Rajaie Cardiovascular Medical and Research Centre, Iran University of Medical Sciences, Tehran, 19956-14331 Iran
| | - Alia Saberi
- Department of Neurology, Poursina Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, 41937-13194 Guilan Iran
| | - Ali Chaibakhsh
- Intelligent Systems and Advanced Control Lab, University of Guilan, Rasht, 41938-13776 Guilan Iran
- Faculty of Mechanical Engineering, University of Guilan, Rasht, 41996-13776 Guilan Iran
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Séguin P, Maby E, Fouillen M, Otman A, Luauté J, Giraux P, Morlet D, Mattout J. The challenge of controlling an auditory BCI in the case of severe motor disability. J Neuroeng Rehabil 2024; 21:9. [PMID: 38238759 PMCID: PMC10795353 DOI: 10.1186/s12984-023-01289-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 11/29/2023] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND The locked-in syndrome (LIS), due to a lesion in the pons, impedes communication. This situation can also be met after some severe brain injury or in advanced Amyotrophic Lateral Sclerosis (ALS). In the most severe condition, the persons cannot communicate at all because of a complete oculomotor paralysis (Complete LIS or CLIS). This even prevents the detection of consciousness. Some studies suggest that auditory brain-computer interface (BCI) could restore a communication through a « yes-no» code. METHODS We developed an auditory EEG-based interface which makes use of voluntary modulations of attention, to restore a yes-no communication code in non-responding persons. This binary BCI uses repeated speech sounds (alternating "yes" on the right ear and "no" on the left ear) corresponding to either frequent (short) or rare (long) stimuli. Users are instructed to pay attention to the relevant stimuli only. We tested this BCI with 18 healthy subjects, and 7 people with severe motor disability (3 "classical" persons with locked-in syndrome and 4 persons with ALS). RESULTS We report online BCI performance and offline event-related potential analysis. On average in healthy subjects, online BCI accuracy reached 86% based on 50 questions. Only one out of 18 subjects could not perform above chance level. Ten subjects had an accuracy above 90%. However, most patients could not produce online performance above chance level, except for two people with ALS who obtained 100% accuracy. We report individual event-related potentials and their modulation by attention. In addition to the classical P3b, we observed a signature of sustained attention on responses to frequent sounds, but in healthy subjects and patients with good BCI control only. CONCLUSIONS Auditory BCI can be very well controlled by healthy subjects, but it is not a guarantee that it can be readily used by the target population of persons in LIS or CLIS. A conclusion that is supported by a few previous findings in BCI and should now trigger research to assess the reasons of such a gap in order to propose new and efficient solutions. CLINICAL TRIAL REGISTRATIONS No. NCT02567201 (2015) and NCT03233282 (2013).
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Affiliation(s)
- Perrine Séguin
- Lyon Neuroscience Research Center, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, 69000, Lyon, France
- University Hospital of Saint-Etienne, 42000, Saint-Etienne, France
- Jean Monnet University, 42000, Saint-Etienne, France
| | - Emmanuel Maby
- Lyon Neuroscience Research Center, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, 69000, Lyon, France
| | - Mélodie Fouillen
- Lyon Neuroscience Research Center, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, 69000, Lyon, France
| | - Anatole Otman
- Lyon Neuroscience Research Center, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, 69000, Lyon, France
| | - Jacques Luauté
- Lyon Neuroscience Research Center, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, 69000, Lyon, France
- Hospices Civils de Lyon, 69000, Lyon, France
| | - Pascal Giraux
- Lyon Neuroscience Research Center, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, 69000, Lyon, France
- University Hospital of Saint-Etienne, 42000, Saint-Etienne, France
- Jean Monnet University, 42000, Saint-Etienne, France
| | - Dominique Morlet
- Lyon Neuroscience Research Center, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, 69000, Lyon, France
| | - Jérémie Mattout
- Lyon Neuroscience Research Center, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, 69000, Lyon, France.
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Abstract
Covert consciousness is a state of residual awareness following severe brain injury or neurological disorder that evades routine bedside behavioral detection. Patients with covert consciousness have preserved awareness but are incapable of self-expression through ordinary means of behavior or communication. Growing recognition of the limitations of bedside neurobehavioral examination in reliably detecting consciousness, along with advances in neurotechnologies capable of detecting brain states or subtle signs indicative of consciousness not discernible by routine examination, carry promise to transform approaches to classifying, diagnosing, prognosticating and treating disorders of consciousness. Here we describe and critically evaluate the evolving clinical category of covert consciousness, including approaches to its diagnosis through neuroimaging, electrophysiology, and novel behavioral tools, its prognostic relevance, and open questions pertaining to optimal clinical management of patients with covert consciousness recovering from severe brain injury.
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Affiliation(s)
- Michael J. Young
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Brian L. Edlow
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Yelena G. Bodien
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA
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Schnetzer L, McCoy M, Bergmann J, Kunz A, Leis S, Trinka E. Locked-in syndrome revisited. Ther Adv Neurol Disord 2023; 16:17562864231160873. [PMID: 37006459 PMCID: PMC10064471 DOI: 10.1177/17562864231160873] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 02/14/2023] [Indexed: 03/31/2023] Open
Abstract
The locked-in syndrome (LiS) is characterized by quadriplegia with preserved vertical eye and eyelid movements and retained cognitive abilities. Subcategorization, aetiologies and the anatomical foundation of LiS are discussed. The damage of different structures in the pons, mesencephalon and thalamus are attributed to symptoms of classical, complete and incomplete LiS and the locked-in plus syndrome, which is characterized by additional impairments of consciousness, making the clinical distinction to other chronic disorders of consciousness at times difficult. Other differential diagnoses are cognitive motor dissociation (CMD) and akinetic mutism. Treatment options are reviewed and an early, interdisciplinary and aggressive approach, including the provision of psychological support and coping strategies is favoured. The establishment of communication is a main goal of rehabilitation. Finally, the quality of life of LiS patients and ethical implications are considered. While patients with LiS report a high quality of life and well-being, medical professionals and caregivers have largely pessimistic perceptions. The negative view on life with LiS must be overthought and the autonomy and dignity of LiS patients prioritized. Knowledge has to be disseminated, diagnostics accelerated and technical support system development promoted. More well-designed research but also more awareness of the needs of LiS patients and their perception as individual persons is needed to enable a life with LiS that is worth living.
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Affiliation(s)
| | - Mark McCoy
- Department of Neurology, Neurological Intensive Care and Neurorehabilitation, Christian Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Jürgen Bergmann
- Department of Neurology, Neurological Intensive Care and Neurorehabilitation, Christian Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Alexander Kunz
- Department of Neurology, Neurological Intensive Care and Neurorehabilitation, Christian Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
- Karl Landsteiner Institute of Neurorehabilitation and Space Neurology, Salzburg, Austria
| | - Stefan Leis
- Department of Neurology, Neurological Intensive Care and Neurorehabilitation, Christian Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Neurological Intensive Care and Neurorehabilitation, Christian Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
- MRI Research Unit, Neuroscience Institute, Christian Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
- Karl Landsteiner Institute of Neurorehabilitation and Space Neurology, Salzburg, Austria
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Ding L, Duan W, Wang Y, Lei X. Test-retest reproducibility comparison in resting and the mental task states: A sensor and source-level EEG spectral analysis. Int J Psychophysiol 2022; 173:20-28. [PMID: 35017028 DOI: 10.1016/j.ijpsycho.2022.01.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 01/04/2022] [Accepted: 01/04/2022] [Indexed: 01/04/2023]
Abstract
Previous test-retest analysis of EEG mostly focused on eyes open and eyes closed resting-state. However, less attention was paid to the EEG during the subject-driven mental imaginary task state. In the current study, we compared the test-retest reproducibility of EEG spectrum in three mental imaginary task states (i.e. performed mental arithmetic, recalled the events of their day, and silently sang lyrics) and two resting states (i.e. eyes open and closed) during three EEG sessions. The resting state with eyes closed has the highest reproducibility, while the resting state with eyes opened has the lowest reproducibility for the spectral features of EEG signals at the sensor level. However, the reproducibility during eyes-open ranked higher among the five states at the source level. Moreover, the mental arithmetic state has the highest reproducibility among all the three task states. And its reproducibility in certain rhythms (theta, gamma, etc) was higher than the resting states. The reproducibility of the EEG spectrum was also investigated from the perspective of large-scale brain networks. The dorsal attention network showed the highest reproducibility in a wide frequency range of the alpha and beta rhythms. Our study suggests the importance of task selection based on the target brain region and the target frequency band. This may provide some suggestions for future researchers to choose appropriate experimental paradigms and provide a guideline on EEG study for the basic and clinical applications.
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Affiliation(s)
- Lihong Ding
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing 400715, China
| | - Wei Duan
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing 400715, China
| | - Yulin Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing 400715, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing 400715, China.
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Hossaini A, Valeriani D, Nam CS, Ferrante R, Mahmud M. A Functional BCI Model by the P2731 working group: Physiology. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1968665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Ali Hossaini
- Department of Engineering, King’s College London, London, UK
| | | | - Chang S. Nam
- Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA
| | | | - Mufti Mahmud
- School of Science and Technology, Nottingham Trent University, Nottingham, UK
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Zapała D, Hossaini A, Kianpour M, Sahonero-Alvarez G, Ayesh A. A functional BCI model by the P2731 working group: psychology. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1935124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Dariusz Zapała
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Lublin, Poland
| | - Ali Hossaini
- Department of Engineering, King’s College London, London, UK
| | - Mazaher Kianpour
- Department of Information Security and Communication Technology, Norwegian University of Science and Technology, Gjøvik, Norway
| | - Guillermo Sahonero-Alvarez
- Center for Research, Development and Innovation in Mechatronics Engineering,Department of Mechatronics Engineering, Universidad Catolica Boliviana San Pablo, La Paz, Bolivia
| | - Aladdin Ayesh
- Faculty of Computing,Engineering and Media,De Montfort University, Leicester, UK
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Brandl S, Blankertz B. Motor Imagery Under Distraction- An Open Access BCI Dataset. Front Neurosci 2020; 14:566147. [PMID: 33192253 PMCID: PMC7604514 DOI: 10.3389/fnins.2020.566147] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 08/21/2020] [Indexed: 12/14/2022] Open
Affiliation(s)
- Stephanie Brandl
- Department of Machine Learning, Technische Universität Berlin, Berlin, Germany
| | - Benjamin Blankertz
- Department of Neurotechnology, Technische Universität Berlin, Berlin, Germany
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Jiang J, Wang C, Wu J, Qin W, Xu M, Yin E. Temporal Combination Pattern Optimization Based on Feature Selection Method for Motor Imagery BCIs. Front Hum Neurosci 2020; 14:231. [PMID: 32714167 PMCID: PMC7344307 DOI: 10.3389/fnhum.2020.00231] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 05/25/2020] [Indexed: 11/19/2022] Open
Abstract
Common spatial pattern (CSP) method is widely used for spatial filtering and brain pattern extraction from electroencephalogram (EEG) signals in motor imagery (MI)-based brain-computer interfaces (BCIs). The participant-specific time window relative to the visual cue has a significant impact on the effectiveness of the CSP. However, the time window is usually selected experientially or manually. To solve this problem, we propose a novel feature selection approach for MI-based BCIs. Specifically, multiple time segments were obtained by decomposing each EEG sample of the MI task. Furthermore, the features were extracted by CSP from each time segment and were combined to form a new feature vector. Finally, the optimal temporal combination patterns for the new feature vector were selected based on four feature selection algorithms, i.e., mutual information, least absolute shrinkage and selection operator, principal component analysis and stepwise linear discriminant analysis (denoted as MUIN, LASSO, PCA, and SWLDA, respectively), and the classification algorithm was employed to evaluate the average classification accuracy. With three BCI competition datasets, the results of the four proposed algorithms were compared with traditional CSP algorithm in classification accuracy. Experimental results show that compared with traditional algorithm, the proposed methods significantly improve performance. Specifically, the LASSO achieved the highest accuracy (88.58%) among the proposed methods. Importantly, the average classification accuracies using the proposed approaches significantly improved 10.14% (MUIN), 11.40% (LASSO), 6.08% (PCA), and 10.25% (SWLDA) compared to that using CSP. These results indicate that the proposed approach is expected to be practical in MI-based BCIs.
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Affiliation(s)
- Jing Jiang
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Chunhui Wang
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Jinghan Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Wei Qin
- Unmanned Systems Research Center, National Innovation Institute of Defense Technology, Academy of Military Sciences China, Beijing, China.,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China
| | - Minpeng Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Erwei Yin
- Unmanned Systems Research Center, National Innovation Institute of Defense Technology, Academy of Military Sciences China, Beijing, China.,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China
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