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Studnicki A, Seidler RD, Ferris DP. A table tennis serve versus rally hit elicits differential hemispheric electrocortical power fluctuations. J Neurophysiol 2023; 130:1444-1456. [PMID: 37964746 PMCID: PMC10994643 DOI: 10.1152/jn.00091.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 10/10/2023] [Accepted: 11/08/2023] [Indexed: 11/16/2023] Open
Abstract
Human visuomotor control requires coordinated interhemispheric interactions to exploit the brain's functional lateralization. In right-handed individuals, the left hemisphere (right arm) is better for dynamic control and the right hemisphere (left arm) is better for impedance control. Table tennis is a game that requires precise movements of the paddle, whole body coordination, and cognitive engagement, providing an ecologically valid way to study visuomotor integration. The sport has many different types of strokes (e.g., serve, return, and rally shots), which should provide unique cortical dynamics given differences in the sensorimotor demands. The goal of this study was to determine the hemispheric specialization of table tennis serving - a sequential, self-paced, bimanual maneuver. We used time-frequency analysis, event-related potentials, and functional connectivity measures of source-localized electrocortical clusters and compared serves with other types of shots, which varied in the types of movement required, attentional focus, and other task demands. We found greater alpha (8-12 Hz) and beta (13-30 Hz) power in the right sensorimotor cortex than in the left sensorimotor cortex, and we found a greater magnitude of spectral power fluctuations in the right sensorimotor cortex for serve hits than return or rally hits, in all right-handed participants. Surprisingly, we did not find a difference in interhemispheric functional connectivity between a table tennis serve and return or rally hits, even though a serve could arguably be a more complex maneuver. Studying real-world brain dynamics of table tennis provides insight into bilateral sensorimotor integration.NEW & NOTEWORTHY We found different spectral power fluctuations in the left and right sensorimotor cortices during table tennis serves, returns, and rallies. Our findings contribute to the basic science understanding of hemispheric specialization in a real-world context.
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Affiliation(s)
- Amanda Studnicki
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, United States
| | - Rachael D Seidler
- Department of Applied Physiology & Kinesiology, University of Florida, Gainesville, Florida, United States
| | - Daniel P Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, United States
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2
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Holmes N, Rea M, Hill RM, Leggett J, Edwards LJ, Hobson PJ, Boto E, Tierney TM, Rier L, Rivero GR, Shah V, Osborne J, Fromhold TM, Glover P, Brookes MJ, Bowtell R. Enabling ambulatory movement in wearable magnetoencephalography with matrix coil active magnetic shielding. Neuroimage 2023; 274:120157. [PMID: 37149237 PMCID: PMC10465235 DOI: 10.1016/j.neuroimage.2023.120157] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/13/2023] [Accepted: 05/03/2023] [Indexed: 05/08/2023] Open
Abstract
The ability to collect high-quality neuroimaging data during ambulatory participant movement would enable a wealth of neuroscientific paradigms. Wearable magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) has the potential to allow participant movement during a scan. However, the strict zero magnetic field requirement of OPMs means that systems must be operated inside a magnetically shielded room (MSR) and also require active shielding using electromagnetic coils to cancel residual fields and field changes (due to external sources and sensor movements) that would otherwise prevent accurate neuronal source reconstructions. Existing active shielding systems only compensate fields over small, fixed regions and do not allow ambulatory movement. Here we describe the matrix coil, a new type of active shielding system for OPM-MEG which is formed from 48 square unit coils arranged on two planes which can compensate magnetic fields in regions that can be flexibly placed between the planes. Through the integration of optical tracking with OPM data acquisition, field changes induced by participant movement are cancelled with low latency (25 ms). High-quality MEG source data were collected despite the presence of large (65 cm translations and 270° rotations) ambulatory participant movements.
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Affiliation(s)
- Niall Holmes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK; Cerca Magnetics Limited, Unit 2 Castlebridge Office Village, Kirtley Drive, Nottingham NG7 1LD, UK.
| | - Molly Rea
- Cerca Magnetics Limited, Unit 2 Castlebridge Office Village, Kirtley Drive, Nottingham NG7 1LD, UK; Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Ryan M Hill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK; Cerca Magnetics Limited, Unit 2 Castlebridge Office Village, Kirtley Drive, Nottingham NG7 1LD, UK
| | - James Leggett
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Lucy J Edwards
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Peter J Hobson
- School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Elena Boto
- Cerca Magnetics Limited, Unit 2 Castlebridge Office Village, Kirtley Drive, Nottingham NG7 1LD, UK; Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Tim M Tierney
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Lukas Rier
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Gonzalo Reina Rivero
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK; Cerca Magnetics Limited, Unit 2 Castlebridge Office Village, Kirtley Drive, Nottingham NG7 1LD, UK
| | - Vishal Shah
- QuSpin Inc., 331 South 104th Street, Suite 130, Louisville, CO 80027, USA
| | - James Osborne
- QuSpin Inc., 331 South 104th Street, Suite 130, Louisville, CO 80027, USA
| | - T Mark Fromhold
- School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Paul Glover
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK; Cerca Magnetics Limited, Unit 2 Castlebridge Office Village, Kirtley Drive, Nottingham NG7 1LD, UK
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK
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3
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Fu X, Guan C. Gait Pattern Recognition Based on Supervised Contrastive Learning Between EEG and EMG. 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: 38082819 DOI: 10.1109/embc40787.2023.10340491] [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
Electroencephalography (EEG) and lower-limb electromyography (EMG) signals are widely used in lower-limb kinematic classification and regression tasks. Since it directly measures muscle responses, EMG usually works better. However, due to the susceptibility of EMG signals to muscle fatigue, insufficient residual myoelectric activity, and the difficulty of precise localization, it is difficult to acquire EMG signals in practice. In contrast, EEG signals are stable and easy to sample. Therefore, in this work, we propose a multimodal training strategy based on supervised contrastive learning. With this training strategy, we can effectively use the guiding role of EMG in the training phase to help the model fit the gait with EEG signal while using only EEG signal in the testing phase to obtain better results than using any single modal signal to train and test the model. Finally, we compared the models trained with the strategy proposed in this paper with other models trained with EEG signals. The obtained Pearson's Correlation Coefficient value exceeds those of all baseline models.
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Castro Aguiar R, Sam Jeeva Raj EJ, Chakrabarty S. Simplified Markerless Stride Detection Pipeline (sMaSDP) for Surface EMG Segmentation. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094340. [PMID: 37177543 PMCID: PMC10181504 DOI: 10.3390/s23094340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 04/19/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023]
Abstract
To diagnose mobility impairments and select appropriate physiotherapy, gait assessment studies are often recommended. These studies are usually conducted in confined clinical settings, which may feel foreign to a subject and affect their motivation, coordination, and overall mobility. Conducting gait studies in unconstrained natural settings instead, such as the subject's Activities of Daily Life (ADL), could provide a more accurate assessment. To appropriately diagnose gait deficiencies, muscle activity should be recorded in parallel with typical kinematic studies. To achieve this, Electromyography (EMG) and kinematic are collected synchronously. Our protocol sMaSDP introduces a simplified markerless gait event detection pipeline for the segmentation of EMG signals via Inertial Measurement Unit (IMU) data, based on a publicly available dataset. This methodology intends to provide a simple, detailed sequence of processing steps for gait event detection via IMU and EMG, and serves as tutorial for beginners in unconstrained gait assessment studies. In an unconstrained gait experiment, 10 healthy subjects walk through a course designed to mimic everyday walking, with their kinematic and EMG data recorded, for a total of 20 trials. Five different walking modalities, such as level walking, ramp up/down, and staircase up/down are included. By segmenting and filtering the data, we generate an algorithm that detects heel-strike events, using a single IMU, and isolates EMG activity of gait cycles. Applicable to different datasets, sMaSDP was tested in healthy gait and gait data of Parkinson's Disease (PD) patients. Using sMaSDP, we extracted muscle activity in healthy walking and identified heel-strike events in PD patient data. The algorithm parameters, such as expected velocity and cadence, are adjustable and can further improve the detection accuracy, and our emphasis on the wearable technologies makes this solution ideal for ADL gait studies.
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Affiliation(s)
- Rafael Castro Aguiar
- School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK
| | | | - Samit Chakrabarty
- School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK
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5
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Gwon D, Won K, Song M, Nam CS, Jun SC, Ahn M. Review of public motor imagery and execution datasets in brain-computer interfaces. Front Hum Neurosci 2023; 17:1134869. [PMID: 37063105 PMCID: PMC10101208 DOI: 10.3389/fnhum.2023.1134869] [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: 12/31/2022] [Accepted: 03/10/2023] [Indexed: 04/18/2023] Open
Abstract
The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already. However, to the best of our knowledge, these studies have yet to investigate and evaluate the datasets, although data quality is essential for reliable results and the design of subject- or system-independent BCIs. In this study, we conducted a thorough investigation of motor imagery/execution EEG datasets recorded from healthy participants published over the past 13 years. The 25 datasets were collected from six repositories and subjected to a meta-analysis. In particular, we reviewed the specifications of the recording settings and experimental design, and evaluated the data quality measured by classification accuracy from standard algorithms such as Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for comparison and compatibility across the datasets. As a result, we found that various stimulation types, such as text, figure, or arrow, were used to instruct subjects what to imagine and the length of each trial also differed, ranging from 2.5 to 29 s with a mean of 9.8 s. Typically, each trial consisted of multiple sections: pre-rest (2.38 s), imagination ready (1.64 s), imagination (4.26 s, ranging from 1 to 10 s), the post-rest (3.38 s). In a meta-analysis of the total of 861 sessions from all datasets, the mean classification accuracy of the two-class (left-hand vs. right-hand motor imagery) problem was 66.53%, and the population of the BCI poor performers, those who are unable to reach proficiency in using a BCI system, was 36.27% according to the estimated accuracy distribution. Further, we analyzed the CSP features and found that each dataset forms a cluster, and some datasets overlap in the feature space, indicating a greater similarity among them. Finally, we checked the minimal essential information (continuous signals, event type/latency, and channel information) that should be included in the datasets for convenient use, and found that only 71% of the datasets met those criteria. Our attempts to evaluate and compare the public datasets are timely, and these results will contribute to understanding the dataset's quality and recording settings as well as the use of using public datasets for future work on BCIs.
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Affiliation(s)
- Daeun Gwon
- Department of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea
| | - Kyungho Won
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Minseok Song
- Department of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea
| | - Chang S. Nam
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, United States
- Department of Industrial and Management Systems Engineering, Kyung Hee University, Yongin-si, Republic of Korea
| | - Sung Chan Jun
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
- AI Graudate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Minkyu Ahn
- Department of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea
- School of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea
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6
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Sharma A, Rai V, Calvert M, Dai Z, Guo Z, Boe D, Rombokas E. A Non-Laboratory Gait Dataset of Full Body Kinematics and Egocentric Vision. Sci Data 2023; 10:26. [PMID: 36635316 PMCID: PMC9837188 DOI: 10.1038/s41597-023-01932-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 01/03/2023] [Indexed: 01/14/2023] Open
Abstract
In this manuscript, we describe a unique dataset of human locomotion captured in a variety of out-of-the-laboratory environments captured using Inertial Measurement Unit (IMU) based wearable motion capture. The data contain full-body kinematics for walking, with and without stops, stair ambulation, obstacle course navigation, dynamic movements intended to test agility, and negotiating common obstacles in public spaces such as chairs. The dataset contains 24.2 total hours of movement data from a college student population with an approximately equal split of males to females. In addition, for one of the activities, we captured the egocentric field of view and gaze of the subjects using an eye tracker. Finally, we provide some examples of applications using the dataset and discuss how it might open possibilities for new studies in human gait analysis.
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Affiliation(s)
- Abhishek Sharma
- grid.34477.330000000122986657Mechanical Engineering, University of Washington, Seattle, 98195 USA
| | - Vijeth Rai
- grid.34477.330000000122986657Electrical and Computer Engineering, University of Washington, Seattle, 98195 USA
| | - Melissa Calvert
- grid.116068.80000 0001 2341 2786Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, 02142 USA
| | - Zhongyi Dai
- grid.34477.330000000122986657Electrical and Computer Engineering, University of Washington, Seattle, 98195 USA
| | - Zhenghao Guo
- grid.34477.330000000122986657Electrical and Computer Engineering, University of Washington, Seattle, 98195 USA
| | - David Boe
- grid.34477.330000000122986657Mechanical Engineering, University of Washington, Seattle, 98195 USA
| | - Eric Rombokas
- grid.34477.330000000122986657Mechanical Engineering, University of Washington, Seattle, 98195 USA ,grid.34477.330000000122986657Electrical and Computer Engineering, University of Washington, Seattle, 98195 USA
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7
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Studnicki A, Downey RJ, Ferris DP. Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22155867. [PMID: 35957423 PMCID: PMC9371038 DOI: 10.3390/s22155867] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/28/2022] [Accepted: 08/02/2022] [Indexed: 05/27/2023]
Abstract
Researchers can improve the ecological validity of brain research by studying humans moving in real-world settings. Recent work shows that dual-layer EEG can improve the fidelity of electrocortical recordings during gait, but it is unclear whether these positive results extrapolate to non-locomotor paradigms. For our study, we recorded brain activity with dual-layer EEG while participants played table tennis, a whole-body, responsive sport that could help investigate visuomotor feedback, object interception, and performance monitoring. We characterized artifacts with time-frequency analyses and correlated scalp and reference noise data to determine how well different sensors captured artifacts. As expected, individual scalp channels correlated more with noise-matched channel time series than with head and body acceleration. We then compared artifact removal methods with and without the use of the dual-layer noise electrodes. Independent Component Analysis separated channels into components, and we counted the number of high-quality brain components based on the fit of a dipole model and using an automated labeling algorithm. We found that using noise electrodes for data processing provided cleaner brain components. These results advance technological approaches for recording high fidelity brain dynamics in human behaviors requiring whole body movement, which will be useful for brain science research.
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8
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Lee Y, Shin S. The Effect of Body Composition on Gait Variability Varies with Age: Interaction by Hierarchical Moderated Regression Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031171. [PMID: 35162200 PMCID: PMC8834456 DOI: 10.3390/ijerph19031171] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 02/06/2023]
Abstract
Although body composition has been found to affect various motor functions (e.g., locomotion and balance), there is limited information on the effect of the interaction between body composition and age on gait variability. The purpose of this study was to determine the effect of body composition on gait according to age. A total of 80 men (40 young and 40 older males) participated in the experiment. Body composition was measured using bioelectrical impedance analysis (BIA), and gait parameters were measured with seven-dimensional inertial measurement unit (IMU) sensors as each participant walked for 6 min at their preferred pace. Hierarchical moderated regression analysis, including height as a control variable and age as a moderator variable, was performed to determine whether body composition could predict gait parameters. In young males, stride length decreased as body fat percentage (BFP) increased (R2 = 13.4%), and in older males, stride length decreased more markedly as BFP increased (R2 = 26.3%). However, the stride length coefficient of variation (CV) of the older males increased significantly as BFP increased (R2 = 16.2%), but the stride length CV of young males did not change even when BFP increased. The increase in BFP was a factor that simultaneously caused a decrease in gait performance and an increase in gait instability in older males. Therefore, BFP is more important for a stable gait in older males.
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Affiliation(s)
- Yungon Lee
- Research Institute of Human Ecology, Yeungnam University, Gyeongsan-si 38541, Korea;
- Neuromuscular Control Laboratory, Yeungnam University, Gyeongsan-si 38541, Korea
- School of Kinesiology, College of Human Ecology & Kinesiology, Yeungnam University, 221ho, 280 Daehak-ro, Gyeongsan-si 38541, Korea
| | - Sunghoon Shin
- Research Institute of Human Ecology, Yeungnam University, Gyeongsan-si 38541, Korea;
- Neuromuscular Control Laboratory, Yeungnam University, Gyeongsan-si 38541, Korea
- School of Kinesiology, College of Human Ecology & Kinesiology, Yeungnam University, 221ho, 280 Daehak-ro, Gyeongsan-si 38541, Korea
- Correspondence: ; Tel.: +82-10-8940-2406
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9
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Lee YE, Shin GH, Lee M, Lee SW. Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running. Sci Data 2021; 8:315. [PMID: 34930915 PMCID: PMC8688416 DOI: 10.1038/s41597-021-01094-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 11/08/2021] [Indexed: 11/24/2022] Open
Abstract
We present a mobile dataset obtained from electroencephalography (EEG) of the scalp and around the ear as well as from locomotion sensors by 24 participants moving at four different speeds while performing two brain-computer interface (BCI) tasks. The data were collected from 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel electrooculography, and 9-channel inertial measurement units placed at the forehead, left ankle, and right ankle. The recording conditions were as follows: standing, slow walking, fast walking, and slight running at speeds of 0, 0.8, 1.6, and 2.0 m/s, respectively. For each speed, two different BCI paradigms, event-related potential and steady-state visual evoked potential, were recorded. To evaluate the signal quality, scalp- and ear-EEG data were qualitatively and quantitatively validated during each speed. We believe that the dataset will facilitate BCIs in diverse mobile environments to analyze brain activities and evaluate the performance quantitatively for expanding the use of practical BCIs.
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Affiliation(s)
- Young-Eun Lee
- grid.222754.40000 0001 0840 2678Korea University, Department of Brain and Cognitive Engineering, Seoul, 02841 Republic of Korea
| | - Gi-Hwan Shin
- grid.222754.40000 0001 0840 2678Korea University, Department of Brain and Cognitive Engineering, Seoul, 02841 Republic of Korea
| | - Minji Lee
- grid.222754.40000 0001 0840 2678Korea University, Department of Brain and Cognitive Engineering, Seoul, 02841 Republic of Korea
| | - Seong-Whan Lee
- Korea University, Department of Brain and Cognitive Engineering, Seoul, 02841, Republic of Korea. .,Korea University, Department of Artificial Intelligence, Seoul, 02841, Republic of Korea.
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10
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Ko LW, Stevenson C, Chang WC, Yu KH, Chi KC, Chen YJ, Chen CH. Integrated Gait Triggered Mixed Reality and Neurophysiological Monitoring as a Framework for Next-Generation Ambulatory Stroke Rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2435-2444. [PMID: 34748494 DOI: 10.1109/tnsre.2021.3125946] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Brain stroke affects millions of people in the world every year, with 50 to 60 percent of stroke survivors suffering from functional disabilities, for which early and sustained post-stroke rehabilitation is highly recommended. However, approximately one third of stroke patients do not receive early in hospital rehabilitation programs due to insufficient medical facilities or lack of motivation. Gait triggered mixed reality (GTMR) is a cognitive-motor dual task with multisensory feedback tailored for lower-limb post-stroke rehabilitation, which we propose as a potential method for addressing these rehabilitation challenges. Simultaneous gait and EEG data from nine stroke patients was recorded and analyzed to assess the applicability of GTMR to different stroke patients, determine any impacts of GTMR on patients, and better understand brain dynamics as stroke patients perform different rehabilitation tasks. Walking cadence improved significantly for stroke patients and lower-limb movement induced alpha band power suppression during GTMR tasks. The brain dynamics and gait performance across different severities of stroke motor deficits was also assessed; the intensity of walking induced event related desynchronization (ERD) was found to be related to motor deficits, as classified by Brunnstrom stage. In particular, stronger lower-limb movement induced ERD during GTMR rehabilitation tasks was found for patients with moderate motor deficits (Brunnstrom stage IV). This investigation demonstrates the efficacy of the GTMR paradigm for enhancing lower-limb rehabilitation, explores the neural activities of cognitive-motor tasks in different stages of stroke, and highlights the potential for joining enhanced rehabilitation and real-time neural monitoring for superior stroke rehabilitation.
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11
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Reznick E, Embry KR, Neuman R, Bolívar-Nieto E, Fey NP, Gregg RD. Lower-limb kinematics and kinetics during continuously varying human locomotion. Sci Data 2021; 8:282. [PMID: 34711856 PMCID: PMC8553836 DOI: 10.1038/s41597-021-01057-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 09/15/2021] [Indexed: 12/03/2022] Open
Abstract
Human locomotion involves continuously variable activities including walking, running, and stair climbing over a range of speeds and inclinations as well as sit-stand, walk-run, and walk-stairs transitions. Understanding the kinematics and kinetics of the lower limbs during continuously varying locomotion is fundamental to developing robotic prostheses and exoskeletons that assist in community ambulation. However, available datasets on human locomotion neglect transitions between activities and/or continuous variations in speed and inclination during these activities. This data paper reports a new dataset that includes the lower-limb kinematics and kinetics of ten able-bodied participants walking at multiple inclines (±0°; 5° and 10°) and speeds (0.8 m/s; 1 m/s; 1.2 m/s), running at multiple speeds (1.8 m/s; 2 m/s; 2.2 m/s and 2.4 m/s), walking and running with constant acceleration (±0.2; 0.5), and stair ascent/descent with multiple stair inclines (20°; 25°; 30° and 35°). This dataset also includes sit-stand transitions, walk-run transitions, and walk-stairs transitions. Data were recorded by a Vicon motion capture system and, for applicable tasks, a Bertec instrumented treadmill.
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Affiliation(s)
- Emma Reznick
- University of Michigan, Robotics Institute, Ann Arbor, MI, 48109, USA
| | - Kyle R Embry
- University of Texas at Dallas, Department of Mechanical Engineering, Richardson, TX, 75080, USA
- Shirley Ryan AbilityLab, Center for Bionic Medicine, Chicago, IL, 60611, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, 60611, USA
| | - Ross Neuman
- University of Texas at Austin, Department of Mechanical Engineering, Austin, TX, 78712, USA
| | - Edgar Bolívar-Nieto
- University of Michigan, Robotics Institute, Ann Arbor, MI, 48109, USA
- University of Michigan, Department of Electrical Engineering and Computer Science, Ann Arbor, MI, 48109, USA
| | - Nicholas P Fey
- University of Texas at Austin, Department of Mechanical Engineering, Austin, TX, 78712, USA
| | - Robert D Gregg
- University of Michigan, Robotics Institute, Ann Arbor, MI, 48109, USA.
- University of Michigan, Department of Electrical Engineering and Computer Science, Ann Arbor, MI, 48109, USA.
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12
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Investigating neural correlates of locomotion transition via temporal relation of EEG and EOG-recorded eye movements. Comput Biol Med 2021; 132:104350. [PMID: 33799217 DOI: 10.1016/j.compbiomed.2021.104350] [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: 11/20/2020] [Revised: 03/17/2021] [Accepted: 03/17/2021] [Indexed: 11/18/2022]
Abstract
The present study examines a temporal relation of walking behavior during locomotion transition (walking to stair ascent) to electrooculography (EOG) signals recorded from eye movement. Further, electroencephalography (EEG) signals from the occipital region of the brain are processed to understand the relative occurrence in EOG and EEG signals during the transition. The dipole sources in the occipital region with reference to EOG detection were estimated from independent components and then clustered using the k means algorithm. The dynamics of the dipoles in the occipital cluster in different frequency bands revealed significant desynchronization in the β and low γ bands, followed by resynchronization. This transitional behavior coincided with transient features suggesting possible saccadic movement of the eyes in the EOG signal. With the data from six able-bodied participants, the desynchronization in EEG from the occipital region was detected by nearly 2.2 ± 0.5s before the transition. Using preprocessing techniques on the EOG signal followed by detecting saccades from the derivative of the EOG signal, the eye movements were detected by nearly 2.5 ± 0.5s before the transition. The EOG decoded intention of transition appeared as early as 3.0 ± 1.63s before desynchronization in the EEG. A paired t-test analysis showed that the EOG-based intent decoding of transition reflects significantly earlier than occipital EEG (p < 0.00001). This study could lead to a multi-modal neural-machine interface that may produce results superior to previous attempts involving only EEG and EMG signals.
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Keshner EA, Lamontagne A. The Untapped Potential of Virtual Reality in Rehabilitation of Balance and Gait in Neurological Disorders. FRONTIERS IN VIRTUAL REALITY 2021; 2:641650. [PMID: 33860281 PMCID: PMC8046008 DOI: 10.3389/frvir.2021.641650] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Dynamic systems theory transformed our understanding of motor control by recognizing the continual interaction between the organism and the environment. Movement could no longer be visualized simply as a response to a pattern of stimuli or as a demonstration of prior intent; movement is context dependent and is continuously reshaped by the ongoing dynamics of the world around us. Virtual reality is one methodological variable that allows us to control and manipulate that environmental context. A large body of literature exists to support the impact of visual flow, visual conditions, and visual perception on the planning and execution of movement. In rehabilitative practice, however, this technology has been employed mostly as a tool for motivation and enjoyment of physical exercise. The opportunity to modulate motor behavior through the parameters of the virtual world is often ignored in practice. In this article we present the results of experiments from our laboratories and from others demonstrating that presenting particular characteristics of the virtual world through different sensory modalities will modify balance and locomotor behavior. We will discuss how movement in the virtual world opens a window into the motor planning processes and informs us about the relative weighting of visual and somatosensory signals. Finally, we discuss how these findings should influence future treatment design.
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Affiliation(s)
- Emily A. Keshner
- Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, PA, United States
- Correspondence: Emily A. Keshner,
| | - Anouk Lamontagne
- School of Physical and Occupational Therapy, McGill University, Montreal, QC, Canada
- Virtual Reality and Mobility Laboratory, CISSS Laval—Jewish Rehabilitation Hospital Site of the Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal, Laval, QC, Canada
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Basso JC, Satyal MK, Rugh R. Dance on the Brain: Enhancing Intra- and Inter-Brain Synchrony. Front Hum Neurosci 2021; 14:584312. [PMID: 33505255 PMCID: PMC7832346 DOI: 10.3389/fnhum.2020.584312] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 12/03/2020] [Indexed: 12/11/2022] Open
Abstract
Dance has traditionally been viewed from a Eurocentric perspective as a mode of self-expression that involves the human body moving through space, performed for the purposes of art, and viewed by an audience. In this Hypothesis and Theory article, we synthesize findings from anthropology, sociology, psychology, dance pedagogy, and neuroscience to propose The Synchronicity Hypothesis of Dance, which states that humans dance to enhance both intra- and inter-brain synchrony. We outline a neurocentric definition of dance, which suggests that dance involves neurobehavioral processes in seven distinct areas including sensory, motor, cognitive, social, emotional, rhythmic, and creative. We explore The Synchronicity Hypothesis of Dance through several avenues. First, we examine evolutionary theories of dance, which suggest that dance drives interpersonal coordination. Second, we examine fundamental movement patterns, which emerge throughout development and are omnipresent across cultures of the world. Third, we examine how each of the seven neurobehaviors increases intra- and inter-brain synchrony. Fourth, we examine the neuroimaging literature on dance to identify the brain regions most involved in and affected by dance. The findings presented here support our hypothesis that we engage in dance for the purpose of intrinsic reward, which as a result of dance-induced increases in neural synchrony, leads to enhanced interpersonal coordination. This hypothesis suggests that dance may be helpful to repattern oscillatory activity, leading to clinical improvements in autism spectrum disorder and other disorders with oscillatory activity impairments. Finally, we offer suggestions for future directions and discuss the idea that our consciousness can be redefined not just as an individual process but as a shared experience that we can positively influence by dancing together.
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Affiliation(s)
- Julia C Basso
- Department of Human Nutrition, Foods, and Exercise, Virginia Tech, Blacksburg, VA, United States.,Center for Transformative Research on Health Behaviors, Fralin Biomedical Research Institute, Virginia Tech, Blacksburg, VA, United States.,School of Neuroscience, Virginia Tech, Blacksburg, VA, United States
| | - Medha K Satyal
- Graduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Blacksburg, VA, United States
| | - Rachel Rugh
- Center for Communicating Science, Virginia Tech, Blacksburg, VA, United States.,School of Performing Arts, Virginia Tech, Blacksburg, VA, United States
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Brain Connectivity Modulation After Exoskeleton-Assisted Gait in Chronic Hemiplegic Stroke Survivors: A Pilot Study. Am J Phys Med Rehabil 2020; 99:694-700. [PMID: 32084035 DOI: 10.1097/phm.0000000000001395] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The aim of this study was to investigate electroencephalographic (EEG) connectivity short-term changes, quantified by node strength and betweenness centrality, induced by a single trial of exoskeleton-assisted gait in chronic stroke survivors. DESIGN Study design was randomized crossover. Electroencephalographic data (64-channel system) were recorded before gait (baseline) and after unassisted overground walking and overground exoskeleton-assisted walking. Coherence was estimated for alpha1, alpha2, and beta frequency ranges. Graph analysis assessed network model properties: node strength and betweenness centrality. RESULTS Nine participants were included in the final analysis. In the group (four participants) with a left-hemisphere stroke lesion (dominant hemisphere), over the vertex, node strength increased in alpha1, alpha2, and beta bands, and betweenness centrality decreased in alpha2 both after unassisted overground walking and exoskeleton-assisted walking. In the group (five participants) with a right-hemisphere lesion (nondominant hemisphere), node strength increased in alpha1 and alpha2 over the contralesional sensorimotor area and ipsilesional prefrontal area after overground exoskeleton-assisted walking, compared with baseline and unassisted overground walking. CONCLUSION A single session of exoskeleton training provides short-term neuroplastic modulation in chronic stroke. In participants with a nondominant hemisphere lesion, exoskeleton training induces activations similar to those observed in able-bodied participants, suggesting a role of lesion lateralization in networks' reorganization.
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Continuous angular position estimation of human ankle during unconstrained locomotion. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101968] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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17
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Horsak B, Slijepcevic D, Raberger AM, Schwab C, Worisch M, Zeppelzauer M. GaiTRec, a large-scale ground reaction force dataset of healthy and impaired gait. Sci Data 2020; 7:143. [PMID: 32398644 PMCID: PMC7217853 DOI: 10.1038/s41597-020-0481-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 04/06/2020] [Indexed: 11/21/2022] Open
Abstract
The quantification of ground reaction forces (GRF) is a standard tool for clinicians to quantify and analyze human locomotion. Such recordings produce a vast amount of complex data and variables which are difficult to comprehend. This makes data interpretation challenging. Machine learning approaches seem to be promising tools to support clinicians in identifying and categorizing specific gait patterns. However, the quality of such approaches strongly depends on the amount of available annotated data to train the underlying models. Therefore, we present GAITREC, a comprehensive and completely annotated large-scale dataset containing bi-lateral GRF walking trials of 2,084 patients with various musculoskeletal impairments and data from 211 healthy controls. The dataset comprises data of patients after joint replacement, fractures, ligament ruptures, and related disorders at the hip, knee, ankle or calcaneus during their entire stay(s) at a rehabilitation center. The data sum up to a total of 75,732 bi-lateral walking trials and enable researchers to classify gait patterns at a large-scale as well as to analyze the entire recovery process of patients.
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Affiliation(s)
- Brian Horsak
- St. Pölten University of Applied Sciences, Institute of Health Sciences, St. Pölten, Austria.
| | - Djordje Slijepcevic
- St. Pölten University of Applied Sciences, Institute of Creative Media Technologies, St. Pölten, Austria
| | - Anna-Maria Raberger
- St. Pölten University of Applied Sciences, Institute of Health Sciences, St. Pölten, Austria
| | - Caterine Schwab
- St. Pölten University of Applied Sciences, Institute of Health Sciences, St. Pölten, Austria
| | - Marianne Worisch
- Rehabilitation Center Weißer Hof, Austrian Workers' Compensation Board (AUVA), Klosterneuburg, Austria
| | - Matthias Zeppelzauer
- St. Pölten University of Applied Sciences, Institute of Creative Media Technologies, St. Pölten, Austria
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18
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de Freitas AM, Sanchez G, Lecaignard F, Maby E, Soares AB, Mattout J. EEG artifact correction strategies for online trial-by-trial analysis. J Neural Eng 2020; 17:016035. [DOI: 10.1088/1741-2552/ab581d] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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19
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Wagner J, Martinez-Cancino R, Delorme A, Makeig S, Solis-Escalante T, Neuper C, Mueller-Putz G. High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task. Sci Data 2019; 6:211. [PMID: 31624252 PMCID: PMC6797727 DOI: 10.1038/s41597-019-0223-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 09/06/2019] [Indexed: 02/07/2023] Open
Abstract
In this report we present a mobile brain/body imaging (MoBI) dataset that allows study of source-resolved cortical dynamics supporting coordinated gait movements in a rhythmic auditory cueing paradigm. Use of an auditory pacing stimulus stream has been recommended to identify deficits and treat gait impairments in neurologic populations. Here, the rhythmic cueing paradigm required healthy young participants to walk on a treadmill (constant speed) while attempting to maintain step synchrony with an auditory pacing stream and to adapt their step length and rate to unanticipated shifts in tempo of the pacing stimuli (e.g., sudden shifts to a faster or slower tempo). High-density electroencephalography (EEG, 108 channels), surface electromyography (EMG, bilateral tibialis anterior), pressure sensors on the heel (to register timing of heel strikes), and goniometers (knee, hip, and ankle joint angles) were concurrently recorded in 20 participants. The data is provided in the Brain Imaging Data Structure (BIDS) format to promote data sharing and reuse, and allow the inclusion of the data into fully automated data analysis workflows.
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Affiliation(s)
- Johanna Wagner
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA.
- Laboratory for Brain Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, Graz, Austria.
| | - Ramon Martinez-Cancino
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA
- Electric and Computer Engineering Department, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Arnaud Delorme
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA
| | - Scott Makeig
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA
| | - Teodoro Solis-Escalante
- Laboratory for Brain Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- Department of Rehabilitation, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Christa Neuper
- Laboratory for Brain Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- Department of Psychology, University of Graz, Graz, Austria
| | - Gernot Mueller-Putz
- Laboratory for Brain Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, Graz, Austria
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Rashid U, Niazi IK, Signal N, Farina D, Taylor D. Optimal automatic detection of muscle activation intervals. J Electromyogr Kinesiol 2019; 48:103-111. [PMID: 31299564 DOI: 10.1016/j.jelekin.2019.06.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
A significant challenge in surface electromyography (sEMG) is the accurate identification of onsets and offsets of muscle activations. Manual labelling and automatic detection are currently used with varying degrees of reliability, accuracy and time efficiency. Automatic methods still require significant manual input to set the optimal parameters for the detection algorithm. These parameters usually need to be adjusted for each individual, muscle and movement task. We propose a method to automatically identify optimal detection parameters in a minimally supervised way. The proposed method solves an optimisation problem that only requires as input the number of activation bursts in the sEMG in a given time interval. This approach was tested on an extended version of the widely adopted double thresholding algorithm, although the optimisation could be applied to any detection algorithm. sEMG data from 22 healthy participants performing a single (ankle dorsiflexion) and a multi-joint (step on/off) task were used for evaluation. Detection rate, concordance, F1 score as an average of sensitivity and precision, degree of over detection, and degree of under detection were used as performance metrices. The proposed method improved the performance of the double thresholding algorithm in multi-joint movement and had the same performance in single joint movement with respect to the performance of the double thresholding algorithm with task specific global parameters. Moreover, the proposed method was robust when an error of up to ±10% was introduced in the number of activation bursts in the optimisation phase regardless of the movement. In conclusion, our optimised method has improved the automation of a sEMG detection algorithm which may reduce the time burden associated with current sEMG processing.
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Affiliation(s)
- Usman Rashid
- Health & Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand.
| | - Imran Khan Niazi
- Health & Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand; Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand; SMI, Department of Health Science and Technology, Aalborg University, Denmark.
| | - Nada Signal
- Health & Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand.
| | - Dario Farina
- Department of Bioengineering, Imperial College London, UK.
| | - Denise Taylor
- Health & Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand.
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Cao Z, Chuang CH, King JK, Lin CT. Multi-channel EEG recordings during a sustained-attention driving task. Sci Data 2019; 6:19. [PMID: 30952963 PMCID: PMC6472414 DOI: 10.1038/s41597-019-0027-4] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Accepted: 02/26/2019] [Indexed: 11/29/2022] Open
Abstract
We describe driver behaviour and brain dynamics acquired from a 90-minute sustained-attention task in an immersive driving simulator. The data included 62 sessions of 32-channel electroencephalography (EEG) data for 27 subjects driving on a four-lane highway who were instructed to keep the car cruising in the centre of the lane. Lane-departure events were randomly induced to cause the car to drift from the original cruising lane towards the left or right lane. A complete trial included events with deviation onset, response onset, and response offset. The next trial, in which the subject was instructed to drive back to the original cruising lane, began 5-10 seconds after finishing the previous trial. We believe that this dataset will lead to the development of novel neural processing methodology that can be used to index brain cortical dynamics and detect driving fatigue and drowsiness. This publicly available dataset will be beneficial to the neuroscience and brain-computer interface communities.
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Affiliation(s)
- Zehong Cao
- Discipline of ICT, School of Technology, Environments and Design, College of Sciences and Engineering, University of Tasmania, Hobart, TAS, Australia.
| | - Chun-Hsiang Chuang
- Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan
| | - Jung-Kai King
- Brain Research Center, National Chiao Tung University, Hsinchu, Taiwan
| | - Chin-Teng Lin
- Centre for Artificial Intelligence, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, Australia.
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Parada FJ. Understanding Natural Cognition in Everyday Settings: 3 Pressing Challenges. Front Hum Neurosci 2018; 12:386. [PMID: 30319381 PMCID: PMC6170945 DOI: 10.3389/fnhum.2018.00386] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 09/06/2018] [Indexed: 12/28/2022] Open
Affiliation(s)
- Francisco J. Parada
- Laboratorio de Neurociencia Cognitiva y Social, Facultad de Psicología, Universidad Diego Portales, Santiago, Chile
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