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Gangadharan SK, Ramakrishnan S, Paek A, Ravindran A, Prasad VA, Vidal JLC. Characterization of Event Related Desynchronization in Chronic Stroke Using Motor Imagery Based Brain Computer Interface for Upper Limb Rehabilitation. Ann Indian Acad Neurol 2024; 27:297-306. [PMID: 38835164 PMCID: PMC11232817 DOI: 10.4103/aian.aian_1056_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/02/2024] [Indexed: 06/06/2024] Open
Abstract
OBJECTIVE Motor imagery-based brain-computer interface (MI-BCI) is a promising novel mode of stroke rehabilitation. The current study aims to investigate the feasibility of MI-BCI in upper limb rehabilitation of chronic stroke survivors and also to study the early event-related desynchronization after MI-BCI intervention. METHODS Changes in the characteristics of sensorimotor rhythm modulations in response to a short brain-computer interface (BCI) intervention for upper limb rehabilitation of stroke-disabled hand and normal hand were examined. The participants were trained to modulate their brain rhythms through motor imagery or execution during calibration, and they played a virtual marble game during the feedback session, where the movement of the marble was controlled by their sensorimotor rhythm. RESULTS Ipsilesional and contralesional activities were observed in the brain during the upper limb rehabilitation using BCI intervention. All the participants were able to successfully control the position of the virtual marble using their sensorimotor rhythm. CONCLUSIONS The preliminary results support the feasibility of BCI in upper limb rehabilitation and unveil the capability of MI-BCI as a promising medical intervention. This study provides a strong platform for clinicians to build upon new strategies for stroke rehabilitation by integrating MI-BCI with various therapeutic options to induce neural plasticity and recovery.
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Affiliation(s)
- Sagila K Gangadharan
- Department of Electrical Engineering, Indian Institute of Technology Palakkad, Palakkad, Kerala, India
| | - Subasree Ramakrishnan
- Department of Neurology, National Institute of Mental Health and Neuroscience, Bengaluru, Karnataka, India
| | - Andrew Paek
- Department of Electrical and Computer Engineering, Noninvasive Brain Machine Interface Systems Lab, University of Houston, Houston, USA
| | - Akshay Ravindran
- Department of Electrical and Computer Engineering, Noninvasive Brain Machine Interface Systems Lab, University of Houston, Houston, USA
| | - Vinod A Prasad
- Infocomm Technology Cluster, Singapore Institute of Technology, Singapore
| | - Jose L Contreras Vidal
- Department of Electrical and Computer Engineering, Noninvasive Brain Machine Interface Systems Lab, University of Houston, Houston, USA
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Herbert C. Analyzing and computing humans by means of the brain using Brain-Computer Interfaces - understanding the user - previous evidence, self-relevance and the user's self-concept as potential superordinate human factors of relevance. Front Hum Neurosci 2024; 17:1286895. [PMID: 38435127 PMCID: PMC10904616 DOI: 10.3389/fnhum.2023.1286895] [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: 08/31/2023] [Accepted: 12/11/2023] [Indexed: 03/05/2024] Open
Abstract
Brain-computer interfaces (BCIs) are well-known instances of how technology can convert a user's brain activity taken from non-invasive electroencephalography (EEG) into computer commands for the purpose of computer-assisted communication and interaction. However, not all users are attaining the accuracy required to use a BCI consistently, despite advancements in technology. Accordingly, previous research suggests that human factors could be responsible for the variance in BCI performance among users. Therefore, the user's internal mental states and traits including motivation, affect or cognition, personality traits, or the user's satisfaction, beliefs or trust in the technology have been investigated. Going a step further, this manuscript aims to discuss which human factors could be potential superordinate factors that influence BCI performance, implicitly, explicitly as well as inter- and intraindividually. Based on the results of previous studies that used comparable protocols to examine the motivational, affective, cognitive state or personality traits of healthy and vulnerable EEG-BCI users within and across well-investigated BCIs (P300-BCIs or SMR-BCIs, respectively), it is proposed that the self-relevance of tasks and stimuli and the user's self-concept provide a huge potential for BCI applications. As potential key human factors self-relevance and the user's self-concept (self-referential knowledge and beliefs about one's self) guide information processing and modulate the user's motivation, attention, or feelings of ownership, agency, and autonomy. Changes in the self-relevance of tasks and stimuli as well as self-referential processing related to one's self (self-concept) trigger changes in neurophysiological activity in specific brain networks relevant to BCI. Accordingly, concrete examples will be provided to discuss how past and future research could incorporate self-relevance and the user's self-concept in the BCI setting - including paradigms, user instructions, and training sessions.
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Affiliation(s)
- Cornelia Herbert
- Department of Applied Emotion and Motivation Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany
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Fu J, Chen S, Shu X, Lin Y, Jiang Z, Wei D, Gao J, Jia J. Functional-oriented, portable brain-computer interface training for hand motor recovery after stroke: a randomized controlled study. Front Neurosci 2023; 17:1146146. [PMID: 37250399 PMCID: PMC10213744 DOI: 10.3389/fnins.2023.1146146] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/07/2023] [Indexed: 05/31/2023] Open
Abstract
Background Brain-computer interfaces (BCIs) have been proven to be effective for hand motor recovery after stroke. Facing kinds of dysfunction of the paretic hand, the motor task of BCIs for hand rehabilitation is relatively single, and the operation of many BCI devices is complex for clinical use. Therefore, we proposed a functional-oriented, portable BCI equipment and explored the efficiency of hand motor recovery after a stroke. Materials and methods Stroke patients were randomly assigned to the BCI group and the control group. The BCI group received BCI-based grasp/open motor training, while the control group received task-oriented guidance training. Both groups received 20 sessions of motor training in 4 weeks, and each session lasted for 30 min. The Fugl-Meyer assessment of the upper limb (FMA-UE) was applied for the assessment of rehabilitation outcomes, and the EEG signals were obtained for processing. Results The progress of FMA-UE between the BCI group [10.50 (5.75, 16.50)] and the control group [5.00 (4.00, 8.00)] was significantly different (Z = -2.834, P = 0.005). Meanwhile, the FMA-UE of both groups improved significantly (P < 0.001). A total of 24 patients in the BCI group achieved the minimal clinically important difference (MCID) of FMA-UE with an effective rate of 80%, and 16 in the control group achieved the MCID, with an effective rate of 51.6%. The lateral index of the open task in the BCI group was significantly decreased (Z = -2.704, P = 0.007). The average BCI accuracy for 24 stroke patients in 20 sessions was 70.7%, which was improved by 5.0% in the final session compared with the first session. Conclusion Targeted hand movement and two motor task modes, namely grasp and open, to be applied in a BCI design may be suitable in stroke patients with hand dysfunction. The functional-oriented, portable BCI training can promote hand recovery after a stroke, and it is expected to be widely used in clinical practice. The lateral index change of inter-hemispheric balance may be the mechanism of motor recovery. Trial registration number ChiCTR2100044492.
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Affiliation(s)
- Jianghong Fu
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Shugeng Chen
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaokang Shu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yifang Lin
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Zewu Jiang
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Dongshuai Wei
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiajia Gao
- Department of Rehabilitation Medicine, Shanghai No. 3 Rehabilitation Hospital, Shanghai, China
| | - Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
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Fu J, Jiang Z, Shu X, Chen S, Jia J. Correlation between the ERD in grasp/open tasks of BCIs and hand function of stroke patients: a cross-sectional study. Biomed Eng Online 2023; 22:36. [PMID: 37061673 PMCID: PMC10105926 DOI: 10.1186/s12938-023-01091-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 03/02/2023] [Indexed: 04/17/2023] Open
Abstract
BACKGROUND AND AIMS Brain-computer interfaces (BCIs) are emerging as a promising tool for upper limb recovery after stroke, and motor tasks are an essential part of BCIs for patient training and control of rehabilitative/assistive BCIs. However, the correlation between brain activation with different levels of motor impairment and motor tasks in BCIs is still not so clear. Thus, we aim to compare the brain activation of different levels of motor impairment in performing the hand grasping and opening tasks in BCIs. METHODS We instructed stroke patients to perform motor attempts (MA) to grasp and open the affected hand for 30 trials, respectively. During this period, they underwent EEG acquisition and BCIs accuracy recordings. They also received detailed history records and behavioral scale assessments (the Fugl-Meyer assessment of upper limb, FMA-UE). RESULTS The FMA-UE was negatively correlated with the event-related desynchronization (ERD) of the affected hemisphere during open MA (R = - 0.423, P = 0.009) but not with grasp MA (R = - 0.058, P = 0.733). Then we divided the stroke patients into group 1 (Brunnstrom recovery stages between I to II, n = 19) and group 2 (Brunnstrom recovery stages between III to VI, n = 23). No difference during the grasping task (t = 0.091, P = 0.928), but a significant difference during the open task (t = 2.156, P = 0.037) was found between the two groups on the affected hemisphere. No significant difference was found in the unaffected hemisphere. CONCLUSIONS The study indicated that brain activation is positively correlated with the hand function of stroke in open-hand tasks. In the grasping task, the patients in the different groups have a similar brain response, while in the open task, mildly injured patients have more brain activation in open the hand than the poor hand function patients.
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Affiliation(s)
- Jianghong Fu
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, 12 Mid-Wulumuqi Road, Jing'an District, Shanghai, 200040, China
| | - ZeWu Jiang
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, 12 Mid-Wulumuqi Road, Jing'an District, Shanghai, 200040, China
| | - Xiaokang Shu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shugeng Chen
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, 12 Mid-Wulumuqi Road, Jing'an District, Shanghai, 200040, China.
| | - Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, 12 Mid-Wulumuqi Road, Jing'an District, Shanghai, 200040, China.
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
- National Center for Neurological Disorders, Shanghai, China.
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Fu J, Chen S, Jia J. Sensorimotor Rhythm-Based Brain-Computer Interfaces for Motor Tasks Used in Hand Upper Extremity Rehabilitation after Stroke: A Systematic Review. Brain Sci 2022; 13:brainsci13010056. [PMID: 36672038 PMCID: PMC9856697 DOI: 10.3390/brainsci13010056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/05/2022] [Accepted: 12/25/2022] [Indexed: 12/29/2022] Open
Abstract
Brain-computer interfaces (BCIs) are becoming more popular in the neurological rehabilitation field, and sensorimotor rhythm (SMR) is a type of brain oscillation rhythm that can be captured and analyzed in BCIs. Previous reviews have testified to the efficacy of the BCIs, but seldom have they discussed the motor task adopted in BCIs experiments in detail, as well as whether the feedback is suitable for them. We focused on the motor tasks adopted in SMR-based BCIs, as well as the corresponding feedback, and searched articles in PubMed, Embase, Cochrane library, Web of Science, and Scopus and found 442 articles. After a series of screenings, 15 randomized controlled studies were eligible for analysis. We found motor imagery (MI) or motor attempt (MA) are common experimental paradigms in EEG-based BCIs trials. Imagining/attempting to grasp and extend the fingers is the most common, and there were multi-joint movements, including wrist, elbow, and shoulder. There were various types of feedback in MI or MA tasks for hand grasping and extension. Proprioception was used more frequently in a variety of forms. Orthosis, robot, exoskeleton, and functional electrical stimulation can assist the paretic limb movement, and visual feedback can be used as primary feedback or combined forms. However, during the recovery process, there are many bottleneck problems for hand recovery, such as flaccid paralysis or opening the fingers. In practice, we should mainly focus on patients' difficulties, and design one or more motor tasks for patients, with the assistance of the robot, FES, or other combined feedback, to help them to complete a grasp, finger extension, thumb opposition, or other motion. Future research should focus on neurophysiological changes and functional improvements and further elaboration on the changes in neurophysiology during the recovery of motor function.
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Affiliation(s)
- Jianghong Fu
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Shugeng Chen
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Shanghai 200040, China
- Correspondence: ; Tel./Fax: +86-021-5288-7820
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Gangadharan K S, Vinod AP. Direction decoding of imagined hand movements using subject-specific features from parietal EEG. J Neural Eng 2022; 19. [PMID: 35901779 DOI: 10.1088/1741-2552/ac8501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 07/28/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Research on decoding brain signals for controlling external devices is rapidly emerging, owing to its versatile potential applications including neuro-prosthetic control and neurorehabilitation. Electroencephalogram (EEG)-based non-invasive Brain Computer Interface (BCI) systems decode brain signals to establish an augmented communication and control pathway between the brain and the computer. The development of an efficient BCI system requires accurate decoding of neural activity underlying user's intentions. This study investigates the directional tuning of Electroencephalogram (EEG) characteristics from posterior parietal region, associated with bidirectional hand movement imagination (motor imagery) in left and right directions. APPROACH The imagined movement directions of the chosen hand were decoded using a combination of envelope and phase features derived from parietal EEG of both hemispheres. The proposed algorithm uses wavelet for spectral decomposition, and discriminative subject-specific subband levels are identified based on Fisher analysis of envelope and phase features. The selected features from the discriminative subband levels are used for classifying left and right motor imagery directions of the hand using Support Vector Machine Classifier. Furthermore, the performance of the proposed algorithm is evaluated by incorporating a maximum variance-based EEG time bin selection algorithm. MAIN RESULTS With the time bin selection approach using subject-specific features, the proposed algorithm yielded an average left vs right motor imagery direction decoding accuracy of 73.33% across 15 healthy subjects. In addition, decoding accuracy offered by the phase features was higher than that of the envelope features, indicating the importance of phase features in MI kinematics decoding. SIGNIFICANCE The results reveal the significance of parietal EEG in decoding imagined kinematics and open new possibilities for future BCI research.
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Affiliation(s)
- Sagila Gangadharan K
- Electrical Engineering, Indian Institute of Technology Palakkad, Indian Institute of Technology Palakkad, Pudussery West, Palakkad, Palakkad, Kerala, 678623, INDIA
| | - A P Vinod
- Singapore Institute of Technology InfoComm Technology, 10 Dover Drive, Singapore 138683, Singapore, 138683, SINGAPORE
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Caicedo-Acosta J, Castaño GA, Acosta-Medina C, Alvarez-Meza A, Castellanos-Dominguez G. Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators. SENSORS (BASEL, SWITZERLAND) 2021; 21:1932. [PMID: 33801817 PMCID: PMC7999933 DOI: 10.3390/s21061932] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 02/20/2021] [Accepted: 02/25/2021] [Indexed: 11/16/2022]
Abstract
Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neurophysiological and mechanistic interpretation in MI-related applications. Thus, to understand the organization of information processing, measures of functional connectivity are used. Also, models of neural network regression prediction are becoming popular, These intend to reduce the need for extracting features manually. However, predicting MI practicing's neurophysiological inefficiency raises several problems, like enhancing network regression performance because of the overfitting risk. Here, to increase the prediction performance, we develop a deep network regression model that includes three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning between neighboring runs. Validation is performed using functional connectivity predictors extracted from two electroencephalographic databases acquired in conditions close to real MI applications (150 users), resulting in a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability.
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Affiliation(s)
- Julian Caicedo-Acosta
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170001, Colombia; (C.A.-M.); (A.A.-M.); (G.C.-D.)
| | - German A. Castaño
- Grupo de investigación Cultura de la Calidad en la Educación, Universidad Nacional de Colombia, Manizales 170001, Colombia;
| | - Carlos Acosta-Medina
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170001, Colombia; (C.A.-M.); (A.A.-M.); (G.C.-D.)
| | - Andres Alvarez-Meza
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170001, Colombia; (C.A.-M.); (A.A.-M.); (G.C.-D.)
| | - German Castellanos-Dominguez
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170001, Colombia; (C.A.-M.); (A.A.-M.); (G.C.-D.)
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Vidaurre C, Haufe S, Jorajuría T, Müller KR, Nikulin VV. Sensorimotor Functional Connectivity: A Neurophysiological Factor Related to BCI Performance. Front Neurosci 2021; 14:575081. [PMID: 33390877 PMCID: PMC7775663 DOI: 10.3389/fnins.2020.575081] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 11/16/2020] [Indexed: 12/29/2022] Open
Abstract
Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. About 20% of potential users of sensorimotor BCIs do not gain reliable control of the system. The inefficiency to decode user's intentions requires the identification of neurophysiological factors determining “good” and “poor” BCI performers. One of the important neurophysiological aspects in BCI research is that the neuronal oscillations, used to control these systems, show a rich repertoire of spatial sensorimotor interactions. Considering this, we hypothesized that neuronal connectivity in sensorimotor areas would define BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in a calibration and an online feedback session recorded on the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post- as well as pre-stimulus connectivity in the calibration recording is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study demonstrates that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sensorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.
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Affiliation(s)
- Carmen Vidaurre
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
| | - Stefan Haufe
- Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Tania Jorajuría
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
| | - Klaus-Robert Müller
- Department of Machine Learning, Berlin University of Technology, Berlin, Germany.,Department of Artificial Intelligence, Korea University, Seoul, South Korea.,Max Planck Institute for Informatics, Saarbrücken, Germany.,Google Research, Brain Team, Berlin, Germany
| | - Vadim V Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Center for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
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