1
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Egger J, Kostoglou K, Müller-Putz GR. Chrono-EEG dynamics influencing hand gesture decoding: a 10-hour study. Sci Rep 2024; 14:20247. [PMID: 39215011 PMCID: PMC11364647 DOI: 10.1038/s41598-024-70609-x] [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: 02/23/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024] Open
Abstract
Long-term electroencephalography (EEG) recordings have primarily been used to study resting-state fluctuations. These recordings provide valuable insights into various phenomena such as sleep stages, cognitive processes, and neurological disorders. However, this study explores a new angle, focusing for the first time on the evolving nature of EEG dynamics over time within the context of movement. Twenty-two healthy individuals were measured six times from 2 p.m. to 12 a.m. with intervals of 2 h while performing four right-hand gestures. Analysis of movement-related cortical potentials (MRCPs) revealed a reduction in amplitude for the motor and post-motor potential during later hours of the day. Evaluation in source space displayed an increase in the activity of M1 of the contralateral hemisphere and the SMA of both hemispheres until 8 p.m. followed by a decline until midnight. Furthermore, we investigated how changes over time in MRCP dynamics affect the ability to decode motor information. This was achieved by developing classification schemes to assess performance across different scenarios. The observed variations in classification accuracies over time strongly indicate the need for adaptive decoders. Such adaptive decoders would be instrumental in delivering robust results, essential for the practical application of BCIs during day and nighttime usage.
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Affiliation(s)
- Johanna Egger
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Kyriaki Kostoglou
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Gernot R Müller-Putz
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria.
- BioTechMed, Graz, Austria.
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2
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Oh E, Shin S, Kim SP. Brain-computer interface in critical care and rehabilitation. Acute Crit Care 2024; 39:24-33. [PMID: 38224957 PMCID: PMC11002623 DOI: 10.4266/acc.2023.01382] [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: 10/30/2023] [Accepted: 11/08/2023] [Indexed: 01/17/2024] Open
Abstract
This comprehensive review explores the broad landscape of brain-computer interface (BCI) technology and its potential use in intensive care units (ICUs), particularly for patients with motor impairments such as quadriplegia or severe brain injury. By employing brain signals from various sensing techniques, BCIs offer enhanced communication and motor rehabilitation strategies for patients. This review underscores the concept and efficacy of noninvasive, electroencephalogram-based BCIs in facilitating both communicative interactions and motor function recovery. Additionally, it highlights the current research gap in intuitive "stop" mechanisms within motor rehabilitation protocols, emphasizing the need for advancements that prioritize patient safety and individualized responsiveness. Furthermore, it advocates for more focused research that considers the unique requirements of ICU environments to address the challenges arising from patient variability, fatigue, and limited applicability of current BCI systems outside of experimental settings.
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Affiliation(s)
- Eunseo Oh
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea
| | - Seyoung Shin
- Department of Mechanical Engineering, Sungkyunkwan University, Suwon, Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea
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3
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Borras M, Romero S, Rojas-Martinez M, Serna LY, Mananas MA. Spinal Cord Injury Patients Exhibit Changes in Motor-Related Activity and Topographic Distribution. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083728 DOI: 10.1109/embc40787.2023.10340794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Spinal Cord Injury (SCI) is a common disease that usually limits the patient's independence by affecting their motor function. SCI patients usually present neuroplasticity, which allows brain signals transmission through spread pathways. Some innovative rehabilitation therapies, such as functional electrical stimulation (FES) or Brain-computer interfaces (BCIs) jointly with motor neuroprostheses, provide hope for functional restoration. BCIs require the analysis of event-related EEG potentials (ERPs). Movement-related cortical potentials (MRCPs) and event-related desynchroni-zation and synchronization (ERD/ERS) are the most commonly studied ERPs during motor activity. ERPs of healthy subjects may vary from SCI patients. Thus, this study aimed to compare ERPs between healthy subjects and SCI patients during upper-limb movements (forearm supination and pronation, and hand open). Differences between controls and SCI patients were shown in terms of ERPs' amplitude as well as in topographic maps. Changes in amplitude were more substantial in ERD potentials than in MRCPs, while topographic maps showed better localization of all features in healthy patients. The level of SCI injury determines the patients' mobility. A comparison between complete, partial and no motor function subjects showed lower values of feature's amplitudes in the latter group.Clinical Relevance- This demonstrates the existence of significant statistical differences between healthy and SCI subjects, and might be helpful when performing SCI rehabilitation techniques such as designing BCI and neuroprostheses, or analyzing and understanding the brain plasticity process.
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4
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Cao L, Wu H, Chen S, Dong Y, Zhu C, Jia J, Fan C. A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain-Computer Interface-Based Stroke Rehabilitation. Brain Sci 2022; 12:1502. [PMID: 36358428 PMCID: PMC9688819 DOI: 10.3390/brainsci12111502] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/06/2022] [Accepted: 10/31/2022] [Indexed: 09/22/2023] Open
Abstract
Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain-computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabilitation by using overlapping time windows. For this aim, three different models, a convolutional neural network (CNN), graph isomorphism network (GIN), and long short-term memory (LSTM), are used for performing the classification task of motor attempt (MA). We conducted several experiments with different time window lengths, and the results showed that the deep learning approach based on overlapping time windows achieved improvements in classification accuracy, with the LSTM combined vote-counting strategy (VS) having achieved the highest average classification accuracy of 90.3% when the window size was 70. The results verified that the overlapping time window strategy is useful for increasing the efficiency of BCI rehabilitation.
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Affiliation(s)
- Lei Cao
- Department of Artificial Intelligence, Shanghai Maritime University, Shanghai 201306, China
| | - Hailiang Wu
- Department of Artificial Intelligence, Shanghai Maritime University, Shanghai 201306, China
| | - Shugeng Chen
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yilin Dong
- Department of Artificial Intelligence, Shanghai Maritime University, Shanghai 201306, China
| | - Changming Zhu
- Department of Artificial Intelligence, Shanghai Maritime University, Shanghai 201306, China
| | - Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Chunjiang Fan
- Department of Rehabilitation Medicine, Wuxi Rehabilitation Hospital, Wuxi 214001, China
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5
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Garipelli G, Rossy T, Perez-Marcos D, Jöhr J, Diserens K. Movement-Related Cortical Potentials in Embodied Virtual Mirror Visual Feedback. Front Neurol 2021; 12:646886. [PMID: 34211428 PMCID: PMC8239222 DOI: 10.3389/fneur.2021.646886] [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: 12/28/2020] [Accepted: 05/14/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Mirror therapy is thought to drive interhemispheric communication, resulting in a balanced activation. We hypothesized that embodied virtual mirror visual feedback (VR-MVF) presented on a computer screen may produce a similar activation. In this proof-of-concept study, we investigated differences in movement-related cortical potentials (MRCPs) in the electroencephalogram (EEG) from different visual feedback of user movements in 1 stroke patient and 13 age-matched adults. Methods: A 60-year-old right-handed (Edinburgh score >95) male ischemic stroke [left paramedian pontine, National Institutes of Health Stroke Scale (NIHSS) = 6] patient and 13 age-matched right-handed (Edinburgh score >80) healthy adults (58 ± 9 years; six female) participated in the study. We recorded 16-electrode electroencephalogram (EEG), while participants performed planar center-out movements in two embodied visual feedback conditions: (i) direct (movements translated to the avatar's ipsilateral side) and (ii) mirror (movements translated to the avatar's contralateral side) with left (direct left/mirror left) or right (direct right/mirror right) arms. Results: As hypothesized, we observed more balanced MRCP hemispheric negativity in the mirror right compared to the direct right condition [statistically significant at the FC4 electrode; 99.9% CI, (0.81, 13)]. MRCPs in the stroke participant showed reduced lateralized negativity in the direct left (non-paretic) situation compared to healthy participants. Interestingly, the potentials were stronger in the mirror left (non-paretic) compared to direct left case, with significantly more bilateral negativity at FC3 [95% CI (0.758 13.2)] and C2 [95% CI (0.04 9.52)]. Conclusions: Embodied mirror visual feedback is likely to influence bilateral sensorimotor cortical subthreshold activity during movement preparation and execution observed in MRCPs in both healthy participants and a stroke patient.
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Affiliation(s)
| | - Tamara Rossy
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | - Jane Jöhr
- Acute Neurorehabilitation Unit, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Karin Diserens
- Acute Neurorehabilitation Unit, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
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6
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Cao L, Chen S, Jia J, Fan C, Wang H, Xu Z. An Inter- and Intra-Subject Transfer Calibration Scheme for Improving Feedback Performance of Sensorimotor Rhythm-Based BCI Rehabilitation. Front Neurosci 2021; 14:629572. [PMID: 33584182 PMCID: PMC7876404 DOI: 10.3389/fnins.2020.629572] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 12/21/2020] [Indexed: 01/11/2023] Open
Abstract
The Brain Computer Interface (BCI) system is a typical neurophysiological application which helps paralyzed patients with human-machine communication. Stroke patients with motor disabilities are able to perform BCI tasks for clinical rehabilitation. This paper proposes an effective scheme of transfer calibration for BCI rehabilitation. The inter- and intra-subject transfer learning approaches can improve the low-precision classification performance for experimental feedback. The results imply that the systematical scheme is positive in increasing the confidence of voluntary training for stroke patients. In addition, it also reduces the time consumption of classifier calibration.
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Affiliation(s)
- Lei Cao
- Department of Artificial Intelligence, Shanghai Maritime University, Shanghai, China
| | - Shugeng Chen
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | | | - Haoran Wang
- Department of Computer Science and Technology, Tongji University, Shanghai, China
| | - Zhixiong Xu
- Department of Artificial Intelligence, Shanghai Maritime University, Shanghai, China
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7
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Opsommer E, Chevalley O, Korogod N. Motor imagery for pain and motor function after spinal cord injury: a systematic review. Spinal Cord 2019; 58:262-274. [PMID: 31836873 DOI: 10.1038/s41393-019-0390-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 11/17/2019] [Accepted: 11/21/2019] [Indexed: 02/07/2023]
Abstract
STUDY DESIGN Systematic review. OBJECTIVES To evaluate the therapeutic benefits of motor imagery (MI) for the people with spinal cord injury (SCI). SETTING International. METHODS We searched electronic bibliographic databases, trial registers, and relevant reference lists. The review included experimental and quasi-experimental study designs as well as observational studies. For the critical appraisal of the 18 studies retrieved (three RCT, seven quasi-RCT, eight observational), we used instruments from the Joanna Briggs Institute. The primary outcome measure was pain. Secondary outcome measures included motor function and neurophysiological parameters. Adverse effects were extracted if reported in the included studies. Because of data heterogeneity, only a qualitative synthesis is offered. RESULTS The included studies involved 282 patients. In most, results were an improvement in motor function and decreased pain; however, some reported no effect or an increase in pain. Although protocols of MI intervention were heterogeneous, sessions of 8-20 min were used for pain treatments, and of 30-60 min were used for motor function improvement. Neurophysiological measurements showed changes in brain region activation and excitability imposed by SCI, which were partially recovered by MI interventions. No serious adverse effects were reported. CONCLUSIONS High heterogeneity in the SCI population, MI interventions, and outcomes measured makes it difficult to judge the therapeutic effects and best MI intervention protocol, especially for people with SCI with neuropathic pain. Further clinical trials evaluating MI intervention as adjunct therapy for pain in SCI patients are warranted.
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Affiliation(s)
- Emmanuelle Opsommer
- School of Health Sciences (HESAV) - University of Applied Sciences and Arts Western Switzerland (HES-SO), Avenue de Beaumont 21, 1011, Lausanne, Switzerland.
| | - Odile Chevalley
- School of Health Sciences (HESAV) - University of Applied Sciences and Arts Western Switzerland (HES-SO), Avenue de Beaumont 21, 1011, Lausanne, Switzerland
| | - Natalya Korogod
- School of Health Sciences (HESAV) - University of Applied Sciences and Arts Western Switzerland (HES-SO), Avenue de Beaumont 21, 1011, Lausanne, Switzerland
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8
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Farjadian AB, Nabian M, Hartman A, Yen SC, Nasseroleslami B. Visuomotor control of ankle joint using position vs. force. Eur J Neurosci 2019; 50:3235-3250. [PMID: 31273853 DOI: 10.1111/ejn.14502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 05/31/2019] [Accepted: 06/19/2019] [Indexed: 11/27/2022]
Abstract
Ankle joint plays a critical role in daily activities involving interactions with environment using force and position control. Neuromechanical dysfunctions (e.g., due to stroke or brain injury), therefore, have a major impact on individuals' quality of life. The effective design of neuro-rehabilitation protocols for robotic rehabilitation platforms relies on understanding the control characteristics of the ankle joint in interaction with external environment using force and position, as the findings in upper limb may not be generalizable to the lower limb. This study aimed to characterize the skilled performance of ankle joint in visuomotor position and force control. A two-degree-of-freedom (DOF) robotic footplate was used to measure individuals' force and position. Healthy individuals (n = 27) used ankle force or position for point-to-point and tracking control tasks in 1-DOF and 2-DOF virtual game environments. Subjects' performance was quantified as a function of accuracy and completion time. In contrast to comparable performance in 1-DOF control tasks, the performance in 2-DOF tasks was different and had characteristic patterns in the position and force conditions, with a significantly better performance for position. Subjective questionnaires on the perceived difficulty matched the objective experimental results, suggesting that the poor performance in force control was not due to experimental set-up or fatigue but can be attributed to the different levels of challenge needed in neural control. It is inferred that in visuomotor coordination, the neuromuscular specialization of ankle provides better control over position rather than force. These findings can inform the design of neuro-rehabilitation platforms, selection of effective tasks and therapeutic protocols.
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Affiliation(s)
- Amir Bahador Farjadian
- Active Adaptive Control Laboratory, Massachusetts Institute of Technology, Boston, MA, USA
| | - Mohsen Nabian
- Department of Mechanical & Industrial Engineering, Northeastern University, Boston, MA, USA.,Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Amber Hartman
- Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, MA, USA
| | - Sheng-Che Yen
- Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, MA, USA
| | - Bahman Nasseroleslami
- Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Dublin, Ireland
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9
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Ofner P, Schwarz A, Pereira J, Wyss D, Wildburger R, Müller-Putz GR. Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury. Sci Rep 2019; 9:7134. [PMID: 31073142 PMCID: PMC6509331 DOI: 10.1038/s41598-019-43594-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 04/26/2019] [Indexed: 01/08/2023] Open
Abstract
We show that persons with spinal cord injury (SCI) retain decodable neural correlates of attempted arm and hand movements. We investigated hand open, palmar grasp, lateral grasp, pronation, and supination in 10 persons with cervical SCI. Discriminative movement information was provided by the time-domain of low-frequency electroencephalography (EEG) signals. Based on these signals, we obtained a maximum average classification accuracy of 45% (chance level was 20%) with respect to the five investigated classes. Pattern analysis indicates central motor areas as the origin of the discriminative signals. Furthermore, we introduce a proof-of-concept to classify movement attempts online in a closed loop, and tested it on a person with cervical SCI. We achieved here a modest classification performance of 68.4% with respect to palmar grasp vs hand open (chance level 50%).
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Affiliation(s)
- Patrick Ofner
- Graz University of Technology, Institute of Neural Engineering, BCI-Lab, Graz, Austria
| | - Andreas Schwarz
- Graz University of Technology, Institute of Neural Engineering, BCI-Lab, Graz, Austria
| | - Joana Pereira
- Graz University of Technology, Institute of Neural Engineering, BCI-Lab, Graz, Austria
| | | | | | - Gernot R Müller-Putz
- Graz University of Technology, Institute of Neural Engineering, BCI-Lab, Graz, Austria.
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10
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Aliakbaryhosseinabadi S, Kamavuako EN, Jiang N, Farina D, Mrachacz-Kersting N. Classification of Movement Preparation Between Attended and Distracted Self-Paced Motor Tasks. IEEE Trans Biomed Eng 2019; 66:3060-3071. [PMID: 30794165 DOI: 10.1109/tbme.2019.2900206] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Brain-computer interface (BCI) systems aim to control external devices by using brain signals. The performance of these systems is influenced by the user's mental state, such as attention. In this study, we classified two attention states to a target task (attended and distracted task level) while attention to the task is altered by one of three types of distractors. METHODS A total of 27 participants were allocated into three experimental groups and exposed to one type of distractor. An attended condition that was the same across the three groups comprised only the main task execution (self-paced dorsiflexion) while the distracted condition was concurrent execution of the main task and an oddball task (dual-task condition). Electroencephalography signals were recorded from 28 electrodes to classify the two attention states of attended or distracted task conditions by extracting temporal and spectral features. RESULTS The results showed that the ensemble classification accuracy using the combination of temporal and spectral features (spectro-temporal features, 82.3 ± 2.7%) was greater than using temporal (69 ± 2.2%) and spectral (80.3 ± 2.6%) features separately. The classification accuracy was computed using a combination of different channel locations, and it was demonstrated that a combination of parietal and centrally located channels was superior for classification of two attention states during movement preparation (parietal channels: 84.6 ± 1.3%, central and parietal channels: 87.2 ± 1.5%). CONCLUSION It is possible to monitor the users' attention to the task for different types of distractors. SIGNIFICANCE It has implications for online BCI systems where the requirement is for high accuracy of intention detection.
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11
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Höller Y, Thomschewski A, Uhl A, Bathke AC, Nardone R, Leis S, Trinka E, Höller P. HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury. Front Neurol 2018; 9:955. [PMID: 30510537 PMCID: PMC6252382 DOI: 10.3389/fneur.2018.00955] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 10/24/2018] [Indexed: 12/16/2022] Open
Abstract
Brain computer interfaces (BCIs) are thought to revolutionize rehabilitation after SCI, e.g., by controlling neuroprostheses, exoskeletons, functional electrical stimulation, or a combination of these components. However, most BCI research was performed in healthy volunteers and it is unknown whether these results can be translated to patients with spinal cord injury because of neuroplasticity. We sought to examine whether high-density EEG (HD-EEG) could improve the performance of motor-imagery classification in patients with SCI. We recorded HD-EEG with 256 channels in 22 healthy controls and 7 patients with 14 recordings (4 patients had more than one recording) in an event related design. Participants were instructed acoustically to either imagine, execute, or observe foot and hand movements, or to rest. We calculated Fast Fourier Transform (FFT) and full frequency directed transfer function (ffDTF) for each condition and classified conditions pairwise with support vector machines when using only 2 channels over the sensorimotor area, full 10-20 montage, high-density montage of the sensorimotor cortex, and full HD-montage. Classification accuracies were comparable between patients and controls, with an advantage for controls for classifications that involved the foot movement condition. Full montages led to better results for both groups (p < 0.001), and classification accuracies were higher for FFT than for ffDTF (p < 0.001), for which the feature vector might be too long. However, full-montage 10–20 montage was comparable to high-density configurations. Motor-imagery driven control of neuroprostheses or BCI systems may perform as well in patients as in healthy volunteers with adequate technical configuration. We suggest the use of a whole-head montage and analysis of a broad frequency range.
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Affiliation(s)
- Yvonne Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Aljoscha Thomschewski
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Andreas Uhl
- Department of Computer Sciences, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Arne C Bathke
- Department of Mathematics, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Raffaele Nardone
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Department of Neurology, Franz Tappeiner Hospital, Merano, Italy
| | - Stefan Leis
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Peter Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria
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12
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Pereira J, Sburlea AI, Müller-Putz GR. EEG patterns of self-paced movement imaginations towards externally-cued and internally-selected targets. Sci Rep 2018; 8:13394. [PMID: 30190543 PMCID: PMC6127278 DOI: 10.1038/s41598-018-31673-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 08/23/2018] [Indexed: 11/25/2022] Open
Abstract
In this study, we investigate the neurophysiological signature of the interacting processes which lead to a single reach-and-grasp movement imagination (MI). While performing this task, the human healthy participants could either define their movement targets according to an external cue, or through an internal selection process. After defining their target, they could start the MI whenever they wanted. We recorded high density electroencephalographic (EEG) activity and investigated two neural correlates: the event-related potentials (ERPs) associated with the target selection, which reflect the perceptual and cognitive processes prior to the MI, and the movement-related cortical potentials (MRCPs), associated with the planning of the self-paced MI. We found differences in frontal and parietal areas between the late ERP components related to the internally-driven selection and the externally-cued process. Furthermore, we could reliably estimate the MI onset of the self-paced task. Next, we extracted MRCP features around the MI onset to train classifiers of movement vs. rest directly on self-paced MI data. We attained performance significantly higher than chance level for both time-locked and asynchronous classification. These findings contribute to the development of more intuitive brain-computer interfaces in which movement targets are defined internally and the movements are self-paced.
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Affiliation(s)
- Joana Pereira
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
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13
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Yu Z, Li L, Song J, Lv H. The Study of Visual-Auditory Interactions on Lower Limb Motor Imagery. Front Neurosci 2018; 12:509. [PMID: 30087594 PMCID: PMC6066580 DOI: 10.3389/fnins.2018.00509] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 07/05/2018] [Indexed: 11/15/2022] Open
Abstract
In order to improve the activation of the mirror neuron system and the ability of the visual-cued motor imagery further, the multi-stimuli-cued unilateral lower limb motor imagery is studied in this paper. The visual-auditory evoked pathway is proposed and the sensory process is studied. To analyze the visual-auditory interactions, the kinesthetic motor imagery with the visual-auditory stimulus, visual stimulus and no stimulus are involved. The motor-related rhythm suppression is applied on quantitative evaluation. To explore the statistical sensory process, the causal relationships among the functional areas and the event-related potentials are investigated. The results have demonstrated the outstanding performances of the visual-auditory evoked motor imagery on the improvement of the mirror neuron system activation and the motor imagery ability. Besides, the abundant information interactions among functional areas and the positive impacts of the auditory stimulus in the motor and the visual areas have been revealed. The possibility that the sensory processes evoked by the visual-auditory interactions differ from the one elicited by kinesthetic motor imagery, has also been indicated. This study will promisingly offer an efficient way to motor rehabilitation, thus favorable for hemiparesis and partial paralysis patients.
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Affiliation(s)
- Zhongliang Yu
- School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China
| | - Lili Li
- School of Physics, Liaoning University, Shenyang, China
| | - Jinchun Song
- School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China
| | - Hangyuan Lv
- School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China
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14
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Liu D, Chen W, Chavarriaga R, Pei Z, Millán JDR. Decoding of Self-paced Lower-Limb Movement Intention: A Case Study on the Influence Factors. Front Hum Neurosci 2017; 11:560. [PMID: 29218004 PMCID: PMC5703734 DOI: 10.3389/fnhum.2017.00560] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2017] [Accepted: 11/06/2017] [Indexed: 12/31/2022] Open
Abstract
Brain-machine interfaces (BMIs) have been applied as new rehabilitation tools for motor disabled individuals. Active involvement of cerebral activity has been shown to enhance neuroplasticity and thus to restore mobility. Various studies have focused on the detection of upper-limb movement intention, while the fewer study has investigated the lower-limb movement intention decoding. This study presents a BMI to decode the self-paced lower-limb movement intention, with 10 healthy subjects participating in the experiment. We varied four influence factors including the movement type (dorsiflexion or plantar flexion), the limb side (left or right leg), the processing method (time-series analysis based on MRCP, i.e., movement-related cortical potential or frequency-domain estimation based on SMR, i.e., sensory motor rhythm) and the frequency band (e.g., delta, theta, mu, beta and MRCP band at [0.1 1] Hz), to estimate both single-trial and sample-based performance. Feature analysis was then conducted to show the discriminant power (DP) and brain modulations. The average detection latency was -0.334 ± 0.216 s in single-trial basis across all conditions. An average area under the curve (AUC) of 91.0 ± 3.5% and 68.2 ± 4.6% was obtained for the MRCP-based and SMR-based method in the classification, respectively. The best performance was yielded from plantar flexion with left leg using time-series analysis on the MRCP band. The feature analysis indicated a cross-subject consistency of DP with the MRCP-based method and subject-specific variance of DP with the SMR-based method. The results presented here might be further exploited in a rehabilitation scenario. The comprehensive factor analysis might be used to shed light on the design of an effective brain switch to trigger external robotic devices.
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Affiliation(s)
- Dong Liu
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.,Defitech Chair in Brain-Machine Interface, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Weihai Chen
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Ricardo Chavarriaga
- Defitech Chair in Brain-Machine Interface, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Zhongcai Pei
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - José Del R Millán
- Defitech Chair in Brain-Machine Interface, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
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15
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Wöhrle H, Tabie M, Kim SK, Kirchner F, Kirchner EA. A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction. SENSORS (BASEL, SWITZERLAND) 2017; 17:E1552. [PMID: 28671632 PMCID: PMC5539567 DOI: 10.3390/s17071552] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 06/19/2017] [Accepted: 06/28/2017] [Indexed: 01/22/2023]
Abstract
A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient's upcoming movements using electroencephalography (EEG) or electromyography (EMG). However, these modalities have different temporal properties and classification accuracies, which results in specific advantages and disadvantages. To use physiological data analysis in rehabilitation devices, the processing should be performed in real-time, guarantee close to natural movement onset support, provide high mobility, and should be performed by miniaturized systems that can be embedded into the rehabilitation device. We present a novel Field Programmable Gate Array (FPGA) -based system for real-time movement prediction using physiological data. Its parallel processing capabilities allows the combination of movement predictions based on EEG and EMG and additionally a P300 detection, which is likely evoked by instructions of the therapist. The system is evaluated in an offline and an online study with twelve healthy subjects in total. We show that it provides a high computational performance and significantly lower power consumption in comparison to a standard PC. Furthermore, despite the usage of fixed-point computations, the proposed system achieves a classification accuracy similar to systems with double precision floating-point precision.
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Affiliation(s)
- Hendrik Wöhrle
- DFKI GmbH, Robotics Innovation Center (RIC), Robert-Hooke-Str. 1, D-28359 Bremen, Germany.
| | - Marc Tabie
- DFKI GmbH, Robotics Innovation Center (RIC), Robert-Hooke-Str. 1, D-28359 Bremen, Germany.
| | - Su Kyoung Kim
- DFKI GmbH, Robotics Innovation Center (RIC), Robert-Hooke-Str. 1, D-28359 Bremen, Germany.
| | - Frank Kirchner
- DFKI GmbH, Robotics Innovation Center (RIC), Robert-Hooke-Str. 1, D-28359 Bremen, Germany.
- Robotics Group, Department of Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 1, D-28359 Bremen, Germany.
| | - Elsa Andrea Kirchner
- DFKI GmbH, Robotics Innovation Center (RIC), Robert-Hooke-Str. 1, D-28359 Bremen, Germany.
- Robotics Group, Department of Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 1, D-28359 Bremen, Germany.
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16
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Tidoni E, Gergondet P, Fusco G, Kheddar A, Aglioti SM. The Role of Audio-Visual Feedback in a Thought-Based Control of a Humanoid Robot: A BCI Study in Healthy and Spinal Cord Injured People. IEEE Trans Neural Syst Rehabil Eng 2016; 25:772-781. [PMID: 28113631 DOI: 10.1109/tnsre.2016.2597863] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The efficient control of our body and successful interaction with the environment are possible through the integration of multisensory information. Brain-computer interface (BCI) may allow people with sensorimotor disorders to actively interact in the world. In this study, visual information was paired with auditory feedback to improve the BCI control of a humanoid surrogate. Healthy and spinal cord injured (SCI) people were asked to embody a humanoid robot and complete a pick-and-place task by means of a visual evoked potentials BCI system. Participants observed the remote environment from the robot's perspective through a head mounted display. Human-footsteps and computer-beep sounds were used as synchronous/asynchronous auditory feedback. Healthy participants achieved better placing accuracy when listening to human footstep sounds relative to a computer-generated sound. SCI people demonstrated more difficulty in steering the robot during asynchronous auditory feedback conditions. Importantly, subjective reports highlighted that the BCI mask overlaying the display did not limit the observation of the scenario and the feeling of being in control of the robot. Overall, the data seem to suggest that sensorimotor-related information may improve the control of external devices. Further studies are required to understand how the contribution of residual sensory channels could improve the reliability of BCI systems.
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17
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Xu R, Jiang N, Dosen S, Lin C, Mrachacz-Kersting N, Dremstrup K, Farina D. Endogenous Sensory Discrimination and Selection by a Fast Brain Switch for a High Transfer Rate Brain-Computer Interface. IEEE Trans Neural Syst Rehabil Eng 2016; 24:901-10. [PMID: 26849869 DOI: 10.1109/tnsre.2016.2523565] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this study, we present a novel multi-class brain-computer interface (BCI) for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input. The user discriminated these choices by his/her endogenous sensory ability and selected the desired choice with an intuitive motor task. This selection was detected by a fast brain switch based on real-time detection of movement-related cortical potentials from scalp EEG. We demonstrated the feasibility of such a system with a four-class BCI, yielding a true positive rate of ∼ 80% and ∼ 70%, and an information transfer rate of ∼ 7 bits/min and ∼ 5 bits/min, for the movement and imagination selection command, respectively. Furthermore, when the system was extended to eight classes, the throughput of the system was improved, demonstrating the capability of accommodating a large number of classes. Combining the endogenous sensory discrimination with the fast brain switch, the proposed system could be an effective, multi-class, gaze-independent BCI system for communication and control applications.
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18
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Xu R, Jiang N, Mrachacz-Kersting N, Dremstrup K, Farina D. Factors of Influence on the Performance of a Short-Latency Non-Invasive Brain Switch: Evidence in Healthy Individuals and Implication for Motor Function Rehabilitation. Front Neurosci 2016; 9:527. [PMID: 26834551 PMCID: PMC4720791 DOI: 10.3389/fnins.2015.00527] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 12/30/2015] [Indexed: 11/23/2022] Open
Abstract
Brain-computer interfacing (BCI) has recently been applied as a rehabilitation approach for patients with motor disorders, such as stroke. In these closed-loop applications, a brain switch detects the motor intention from brain signals, e.g., scalp EEG, and triggers a neuroprosthetic device, either to deliver sensory feedback or to mimic real movements, thus re-establishing the compromised sensory-motor control loop and promoting neural plasticity. In this context, single trial detection of motor intention with short latency is a prerequisite. The performance of the event detection from EEG recordings is mainly determined by three factors: the type of motor imagery (e.g., repetitive, ballistic), the frequency band (or signal modality) used for discrimination (e.g., alpha, beta, gamma, and MRCP, i.e., movement-related cortical potential), and the processing technique (e.g., time-series analysis, sub-band power estimation). In this study, we investigated single trial EEG traces during movement imagination on healthy individuals, and provided a comprehensive analysis of the performance of a short-latency brain switch when varying these three factors. The morphological investigation showed a cross-subject consistency of a prolonged negative phase in MRCP, and a delayed beta rebound in sensory-motor rhythms during repetitive tasks. The detection performance had the greatest accuracy when using ballistic MRCP with time-series analysis. In this case, the true positive rate (TPR) was ~70% for a detection latency of ~200 ms. The results presented here are of practical relevance for designing BCI systems for motor function rehabilitation.
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Affiliation(s)
- Ren Xu
- Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience, University Medical CenterGöttingen, Germany; Institute of Computer Science, Faculty of Mathematics and Computer Secience, Georg-August UniversityGöttingen, Germany
| | - Ning Jiang
- Department of Systems Design Engineering, University of Waterloo Waterloo, ON, Canada
| | - Natalie Mrachacz-Kersting
- Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University Aalborg, Denmark
| | - Kim Dremstrup
- Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University Aalborg, Denmark
| | - Dario Farina
- Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Germany
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19
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Tidoni E, Tieri G, Aglioti SM. Re-establishing the disrupted sensorimotor loop in deafferented and deefferented people: The case of spinal cord injuries. Neuropsychologia 2015; 79:301-9. [PMID: 26115603 DOI: 10.1016/j.neuropsychologia.2015.06.029] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Revised: 06/15/2015] [Accepted: 06/21/2015] [Indexed: 11/26/2022]
Abstract
Acting efficiently in the world depends on the activity of motor and somatosensory systems, the integration of which is necessary for the proper functioning of the sensorimotor loop (SL). Profound alterations of SL functioning follow spinal cord injury (SCI), a condition that brings about a disconnection of the body from the brain. Such disconnection creates a substantial deprivation of somatosensorial inputs and motor outputs. Consequent somatic deficits and motor paralysis affect the body below the lesion level. A complete restoration of normal functions of the SL cannot be expected until basic neuroscience has found a way to re-establish the interrupted neural connectivity. Meanwhile, studies should focus on the development of technical solutions for dealing with the disruption of the sensorimotor loop. This review discusses the structural and functional adaptive reorganization of the brain after SCI, and the maladaptive mechanisms that impact on the processing of body related information, which alter motor imagery strategies and EEG signals. Studies that show how residual functions (e.g. face tactile sensitivity) may help people to restore a normal body image are also reviewed. Finally, data on how brain and residual body signals may be used to improve brain computer interface systems is discussed in relation to the issue of how such systems may help SCI people to re-enter the world and interact with objects and other individuals.
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Affiliation(s)
- E Tidoni
- Department of Psychology, University of Rome "La Sapienza", Rome, Italy; Fondazione Santa Lucia, IRCCS, Rome, Italy.
| | - G Tieri
- Fondazione Santa Lucia, IRCCS, Rome, Italy; Braintrends Ltd, Applied Neuroscience, Rome, Italy
| | - S M Aglioti
- Department of Psychology, University of Rome "La Sapienza", Rome, Italy; Fondazione Santa Lucia, IRCCS, Rome, Italy.
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20
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Vuckovic A, Pineda JA, LaMarca K, Gupta D, Guger C. Interaction of BCI with the underlying neurological conditions in patients: pros and cons. FRONTIERS IN NEUROENGINEERING 2014; 7:42. [PMID: 25477814 PMCID: PMC4235364 DOI: 10.3389/fneng.2014.00042] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Accepted: 11/03/2014] [Indexed: 11/13/2022]
Affiliation(s)
| | - Jaime A Pineda
- Cognitive Science Department, University of California San Diego, La Jolla, CA, USA
| | - Kristen LaMarca
- Clinical Psyhology, California School of Professional Psychology San Diego, CA, USA
| | - Disha Gupta
- Burke Rehabilitation Center, Burke-Cornell Medical Research Institute White Plains, NY, USA
| | - Christoph Guger
- Guger Technologies OG, g.tec medical engineering GmbH Graz, Austria
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