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Cantillo-Negrete J, Carino-Escobar RI, Ortega-Robles E, Arias-Carrión O. A comprehensive guide to BCI-based stroke neurorehabilitation interventions. MethodsX 2023; 11:102452. [PMID: 38023311 PMCID: PMC10630643 DOI: 10.1016/j.mex.2023.102452] [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: 10/02/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
Brain-Computer Interfaces (BCIs) offer the potential to facilitate neurorehabilitation in stroke patients by decoding user intentions from the central nervous system, thereby enabling control over external devices. Despite their promise, the diverse range of intervention parameters and technical challenges in clinical settings have hindered the accumulation of substantial evidence supporting the efficacy and effectiveness of BCIs in stroke rehabilitation. This article introduces a practical guide designed to navigate through these challenges in conducting BCI interventions for stroke rehabilitation. Applicable regardless of infrastructure and study design limitations, this guide acts as a comprehensive reference for executing BCI-based stroke interventions. Furthermore, it encapsulates insights gleaned from administering hundreds of BCI rehabilitation sessions to stroke patients.•Presents a comprehensive methodology for implementing BCI-based upper extremity therapy in stroke patients.•Provides detailed guidance on the number of sessions, trials, as well as the necessary hardware and software for effective intervention.
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Affiliation(s)
- Jessica Cantillo-Negrete
- División de Investigación en Neurociencias Clínica, Instituto Nacional de Rehabilitación Luis Guillermo Ibarra, Mexico City, NM 14389, Mexico
| | - Ruben I. Carino-Escobar
- División de Investigación en Neurociencias Clínica, Instituto Nacional de Rehabilitación Luis Guillermo Ibarra, Mexico City, NM 14389, Mexico
| | - Emmanuel Ortega-Robles
- Unidad de Trastornos del Movimiento y Sueño, Hospital General Dr. Manuel Gea González, Mexico City 14080, Mexico
| | - Oscar Arias-Carrión
- Unidad de Trastornos del Movimiento y Sueño, Hospital General Dr. Manuel Gea González, Mexico City 14080, Mexico
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Dos Santos EM, San-Martin R, Fraga FJ. Comparison of subject-independent and subject-specific EEG-based BCI using LDA and SVM classifiers. Med Biol Eng Comput 2023; 61:835-845. [PMID: 36626112 DOI: 10.1007/s11517-023-02769-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 01/03/2023] [Indexed: 01/11/2023]
Abstract
Motor imagery brain-computer interface (MI-BCI) is one of the most used paradigms in EEG-based brain-computer interface (BCI). The current state-of-the-art in BCI involves tuning classifiers to subject-specific training data, acquired over several sessions, in order to perform calibration prior to actual use of the so-called subject-specific BCI system (SS-BCI). Herein, the goal is to provide a ready-to-use system requiring minimal effort for setup. Thus, our challenge was to design a subject-independent BCI (SI-BCI) to be used by any new user without the constraint of individual calibration. Outcomes from other studies with the same purpose were used to undertake comparisons and validate our findings. For the EEG signal processing, we used a combination of the delta (0.5-4 Hz), alpha (8-13 Hz), and beta+gamma (13-40 Hz) bands at a stage prior to feature extraction. Next, we extracted features from the 27-channel EEG using common spatial pattern (CSP) and performed binary classification (MI of right- and left-hand) with linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. These analyses were done for both the SS-BCI and SI-BCI models. We employed "leave-one-subject-out" (LOSO) arrangement and 10-fold cross-validation to evaluate our SI-BCI and SS-BCI systems, respectively. Compared with other two studies, our work was the only one that showed higher accuracy for the LDA classifier in SI-BCI as compared to SS-BCI. On the other hand, LDA accuracy was lower than accuracy achieved with SVM in both conditions (SI-BCI and SS-BCI). Our SS-BCI accuracy reached 76.85% using LDA and 94.20% using SVM and for SI-BCI we got 80.30% with LDA and 83.23% with SVM. We conclude that SI-BCI may be a feasible and relevant option, which can be used in scenarios where subjects are not able to submit themselves to long training sessions or to fast evaluation of the so called "BCI illiteracy." Comparatively, our strategy proved to be more efficient, giving us the best result for SI-BCI when faced against the classification performances of other three studies, even considering the caveat that different datasets were used in the comparison of the four studies.
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Meng J, Wu Z, Li S, Zhu X. Effects of Gaze Fixation on the Performance of a Motor Imagery-Based Brain-Computer Interface. Front Hum Neurosci 2022; 15:773603. [PMID: 35140593 PMCID: PMC8818858 DOI: 10.3389/fnhum.2021.773603] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 12/08/2021] [Indexed: 11/13/2022] Open
Abstract
Motor imagery-based brain-computer interfaces (BCIs) have been studied without controlling subjects’ gaze fixation position previously. The effect of gaze fixation and covert attention on the behavioral performance of BCI is still unknown. This study designed a gaze fixation controlled experiment. Subjects were required to conduct a secondary task of gaze fixation when performing the primary task of motor imagination. Subjects’ performance was analyzed according to the relationship between motor imagery target and the gaze fixation position, resulting in three BCI control conditions, i.e., congruent, incongruent, and center cross trials. A group of fourteen subjects was recruited. The average group performances of three different conditions did not show statistically significant differences in terms of BCI control accuracy, feedback duration, and trajectory length. Further analysis of gaze shift response time revealed a significantly shorter response time for congruent trials compared to incongruent trials. Meanwhile, the parietal occipital cortex also showed active neural activities for congruent and incongruent trials, and this was revealed by a contrast analysis of R-square values and lateralization index. However, the lateralization index computed from the parietal and occipital areas was not correlated with the BCI behavioral performance. Subjects’ BCI behavioral performance was not affected by the position of gaze fixation and covert attention. This indicated that motor imagery-based BCI could be used freely in robotic arm control without sacrificing performance.
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Affiliation(s)
- Jianjun Meng
- Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Jianjun Meng,
| | - Zehan Wu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Songwei Li
- Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiangyang Zhu
- Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Sadiq MT, Yu X, Yuan Z, Aziz MZ, Rehman NU, Ding W, Xiao G. Motor Imagery BCI Classification Based on Multivariate Variational Mode Decomposition. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2022. [DOI: 10.1109/tetci.2022.3147030] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Leeuwis N, Yoon S, Alimardani M. Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces. Front Hum Neurosci 2021; 15:732946. [PMID: 34720907 PMCID: PMC8555469 DOI: 10.3389/fnhum.2021.732946] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/03/2021] [Indexed: 11/25/2022] Open
Abstract
Motor Imagery BCI systems have a high rate of users that are not capable of modulating their brain activity accurately enough to communicate with the system. Several studies have identified psychological, cognitive, and neurophysiological measures that might explain this MI-BCI inefficiency. Traditional research had focused on mu suppression in the sensorimotor area in order to classify imagery, but this does not reflect the true dynamics that underlie motor imagery. Functional connectivity reflects the interaction between brain regions during the MI task and resting-state network and is a promising tool in improving MI-BCI classification. In this study, 54 novice MI-BCI users were split into two groups based on their accuracy and their functional connectivity was compared in three network scales (Global, Large and Local scale) during the resting-state, left vs. right-hand motor imagery task, and the transition between the two phases. Our comparison of High and Low BCI performers showed that in the alpha band, functional connectivity in the right hemisphere was increased in High compared to Low aptitude MI-BCI users during motor imagery. These findings contribute to the existing literature that indeed connectivity might be a valuable feature in MI-BCI classification and in solving the MI-BCI inefficiency problem.
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Affiliation(s)
- Nikki Leeuwis
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, Netherlands
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Leeuwis N, Paas A, Alimardani M. Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces. Front Hum Neurosci 2021; 15:634748. [PMID: 33889080 PMCID: PMC8055841 DOI: 10.3389/fnhum.2021.634748] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 03/08/2021] [Indexed: 12/19/2022] Open
Abstract
Brain-computer interfaces (BCIs) are communication bridges between a human brain and external world, enabling humans to interact with their environment without muscle intervention. Their functionality, therefore, depends on both the BCI system and the cognitive capacities of the user. Motor-imagery BCIs (MI-BCI) rely on the users' mental imagination of body movements. However, not all users have the ability to sufficiently modulate their brain activity for control of a MI-BCI; a problem known as BCI illiteracy or inefficiency. The underlying mechanism of this phenomenon and the cause of such difference among users is yet not fully understood. In this study, we investigated the impact of several cognitive and psychological measures on MI-BCI performance. Fifty-five novice BCI-users participated in a left- versus right-hand motor imagery task. In addition to their BCI classification error rate and demographics, psychological measures including personality factors, affinity for technology, and motivation during the experiment, as well as cognitive measures including visuospatial memory and spatial ability and Vividness of Visual Imagery were collected. Factors that were found to have a significant impact on MI-BCI performance were Vividness of Visual Imagery, and the personality factors of orderliness and autonomy. These findings shed light on individual traits that lead to difficulty in BCI operation and hence can help with early prediction of inefficiency among users to optimize training for them.
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Affiliation(s)
- Nikki Leeuwis
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, Netherlands
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Briones-Cantero M, Fernández-de-Las-Peñas C, Lluch-Girbés E, Osuna-Pérez MC, Navarro-Santana MJ, Plaza-Manzano G, Martín-Casas P. Effects of Adding Motor Imagery to Early Physical Therapy in Patients with Knee Osteoarthritis who Had Received Total Knee Arthroplasty: A Randomized Clinical Trial. PAIN MEDICINE 2020; 21:3548-3555. [PMID: 32346743 DOI: 10.1093/pm/pnaa103] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
OBJECTIVE To investigate the effects of the inclusion of motor imagery (MI) principles into early physical therapy on pain, disability, pressure pain thresholds (PPTs), and range of motion in the early postsurgical phase after total knee arthroplasty (TKA). METHODS A randomized clinical trial including patients with knee osteoarthritis who have received TKA was conducted. Participants were randomized to receive five treatment sessions of either physical therapy with or without MI principles in an early postsurgical phase after a TKA (five days after surgery). Pain intensity (visual analog scale [VAS], 0-100), pain-related disability (short-form Western Ontario McMaster Universities Osteoarthritis Index [WOMAC], 0-32), pressure pain thresholds (PPTs), and knee range of motion were assessed before and after five daily treatment sessions by an assessor blinded to the subject's condition. RESULTS Twenty-four participants completed data collection and treatment. The adjusted analysis revealed significant group*time interactions for WOMAC (F = 17.29, P = 0.001, η2 = 0.48) and VAS (F = 14.56, P < 0.001, η2 = 0.45); patients receiving physiotherapy and MI principles experienced greater improvements in pain (Δ -28.0, 95% confidence interval [CI] = -43.0 to -13.0) and pain-related disability (Δ -6.0, 95% CI = -8.3 to -3.7) than those receiving physiotherapy alone. No significant group*time interactions for knee range of motion and PPTs were observed (all, P > 0.30). CONCLUSIONS The application of MI to early physiotherapy was effective for improving pain and disability, but not range of motion or pressure pain sensitivity, in the early postsurgical phase after TKA in people with knee osteoarthritis.
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Affiliation(s)
- María Briones-Cantero
- Unidad de Fisioterapia, Servicio de Rehabilitación, Hospital Universitario Doce de Octubre, Madrid, Spain
| | - César Fernández-de-Las-Peñas
- Department of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Universidad Rey Juan Carlos, Alcorcón, Madrid, Spain.,Cátedra Institucional en Docencia, Clínica e Investigación en Fisioterapia: Terapia Manual, Punción Seca, y Ejercicio Terapéutico, Universidad Rey Juan Carlos, Alcorcón, Madrid, Spain
| | - Enrique Lluch-Girbés
- Department of Physical Therapy, Universidad de Valencia, Valencia, Spain; Pain in Motion Research Group.,Department of Human Physiology (Chropiver), Faculty of Physical Education & Physiotherapy, Vrije Universiteit Brussel
| | | | | | - Gustavo Plaza-Manzano
- Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Patricia Martín-Casas
- Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid, Madrid, Spain.,Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Spain
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Carino-Escobar RI, Galicia-Alvarado M, Marrufo OR, Carrillo-Mora P, Cantillo-Negrete J. Brain-computer interface performance analysis of monozygotic twins with discordant hand dominance: A case study. Laterality 2020; 25:513-536. [PMID: 31918621 DOI: 10.1080/1357650x.2019.1710525] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Brain-computer interfaces (BCI) decode user's intentions to control external devices. However, performance variations across individuals have limited their use to laboratory environments. Handedness could contribute to these variations, especially when motor imagery (MI) tasks are used for BCI control. To further understand how handedness affects BCI control, performance differences between two monozygotic twins were analysed during offline movement and MI tasks, and while twins controlled a BCI using right-hand MI. Quantitative electroencephalography (qEEG), brain structures' volumes, and neuropsychological tests were assessed to evaluate physiological, anatomical and psychological relationships with BCI performance. Results showed that both twins had good motor imagery and attention abilities, similar volumes on most subcortical brain structures, more pronounced event-related desynchronization elicited by the twin performing non-dominant MI, and that this twin also obtained significant higher performances with the BCI. Linear regression analysis implied a strong association between twins' BCI performance, and more pronounced cortical activations in the contralateral hemisphere relative to hand MI. Therefore, it is possible that BCI performance was related with the ability of each twin to elicit cortical activations during hand MI, and less associated with subcortical brain structures' volumes and neuropsychological tests.
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Affiliation(s)
- Ruben I Carino-Escobar
- Division of Research in Medical Engineering, National Institute of Rehabilitation "Luis Guillermo Ibarra Ibarra", Mexico City, Mexico
| | - Marlene Galicia-Alvarado
- Department of Electrodiagnostic, National Institute of Rehabilitation "Luis Guillermo Ibarra Ibarra", Mexico City, Mexico
| | - Oscar R Marrufo
- Department of Neuroimage, National Institute of Neurology and Neurosurgery "Manuel Velasco Suárez", Mexico City, Mexico
| | - Paul Carrillo-Mora
- Division of Neuroscience, National Institute of Rehabilitation "Luis Guillermo Ibarra Ibarra", Mexico City, Mexico
| | - Jessica Cantillo-Negrete
- Division of Research in Medical Engineering, National Institute of Rehabilitation "Luis Guillermo Ibarra Ibarra", Mexico City, Mexico
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Wang Z, Zhou Y, Chen L, Gu B, Liu S, Xu M, Qi H, He F, Ming D. A BCI based visual-haptic neurofeedback training improves cortical activations and classification performance during motor imagery. J Neural Eng 2019; 16:066012. [DOI: 10.1088/1741-2552/ab377d] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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10
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Joadder M, Siuly S, Kabir E, Wang H, Zhang Y. A New Design of Mental State Classification for Subject Independent BCI Systems. Ing Rech Biomed 2019. [DOI: 10.1016/j.irbm.2019.05.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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11
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Joadder MAM, Myszewski JJ, Rahman MH, Wang I. A performance based feature selection technique for subject independent MI based BCI. Health Inf Sci Syst 2019; 7:15. [PMID: 31428313 DOI: 10.1007/s13755-019-0076-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 07/17/2019] [Indexed: 10/26/2022] Open
Abstract
Purpose Significant research has been conducted in the field of brain computer interface (BCI) algorithm development, however, many of the resulting algorithms are both complex, and specific to a particular user as the most successful methodology can vary between individuals and sessions. The objective of this study was to develop a simple yet effective method of feature selection to improve the accuracy of a subject independent BCI algorithm and streamline the process of BCI algorithm development. Over the past several years, several high precision features have been suggested by researchers to classify different motor imagery tasks. This research applies fourteen of these features as a feature pool that can be used as a reference for future researchers. Additionally, we look for the most efficient feature or feature set with four different classifiers that best differentiates several motor imagery tasks. In this work we have successfully employed a feature fusion method to obtain the best sub-set of features. We have proposed a novel computer aided feature selection method to determine the best set of features for distinguishing between motor imagery tasks in lieu of the manual feature selection that has been performed in past studies. The features selected by this method were then fed into a Linear Discriminant Analysis, K-nearest neighbor, decision tree, or support vector machine classifier for classification to determine the overall performance. Methods The methods used were a novel performance based additive feature fusion algorithm working in conjunction with machine learning in order to classify the motor imagery signals into particular states. The data used for this study was collected from BCI competition III dataset IVa. Result The result of this algorithm was a classification accuracy of 99% for a subject independent algorithm with less computation cost compared to traditional methods, in addition to multiple feature/classifier combinations that outperform current subject independent methods. Conclusion The conclusion of this study and its significance is that it developed a viable methodology for simple, efficient feature selection and BCI algorithm development, which leads to an overall increase in algorithm classification accuracy.
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Affiliation(s)
- Md A Mannan Joadder
- 1Department of Electrical, & Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Joshua J Myszewski
- 2Department of Biomedical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI 53211 USA
| | - Mohammad H Rahman
- 2Department of Biomedical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI 53211 USA
| | - Inga Wang
- 3Department of Occupational Science & Technology, University of Wisconsin-Milwaukee, Milwaukee, WI 53211 USA
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Vukelić M, Belardinelli P, Guggenberger R, Royter V, Gharabaghi A. Different oscillatory entrainment of cortical networks during motor imagery and neurofeedback in right and left handers. Neuroimage 2019; 195:190-202. [PMID: 30951847 DOI: 10.1016/j.neuroimage.2019.03.067] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 03/02/2019] [Accepted: 03/27/2019] [Indexed: 01/08/2023] Open
Abstract
Volitional modulation and neurofeedback of sensorimotor oscillatory activity is currently being evaluated as a strategy to facilitate motor restoration following stroke. Knowledge on the interplay between this regional brain self-regulation, distributed network entrainment and handedness is, however, limited. In a randomized cross-over design, twenty-one healthy subjects (twelve right-handers [RH], nine left-handers [LH]) performed kinesthetic motor imagery of left (48 trials) and right finger extension (48 trials). A brain-machine interface turned event-related desynchronization in the beta frequency-band (16-22 Hz) during motor imagery into passive hand opening by a robotic orthosis. Thereby, every participant subsequently activated either the dominant (DH) or non-dominant hemisphere (NDH) to control contralateral hand opening. The task-related cortical networks were studied with electroencephalography. The magnitude of the induced oscillatory modulation range in the sensorimotor cortex was independent of both handedness (RH, LH) and hemispheric specialization (DH, NDH). However, the regional beta-band modulation was associated with different alpha-band networks in RH and LH: RH presented a stronger inter-hemispheric connectivity, while LH revealed a stronger intra-hemispheric interaction. Notably, these distinct network entrainments were independent of hemispheric specialization. In healthy subjects, sensorimotor beta-band activity can be robustly modulated by motor imagery and proprioceptive feedback in both hemispheres independent of handedness. However, right and left handers show different oscillatory entrainment of cortical alpha-band networks during neurofeedback. This finding may inform neurofeedback interventions in future to align them more precisely with the underlying physiology.
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Affiliation(s)
- Mathias Vukelić
- Division of Functional and Restorative Neurosurgery, Tuebingen Neuro Campus, Eberhard Karls University Tuebingen, Germany
| | - Paolo Belardinelli
- Division of Functional and Restorative Neurosurgery, Tuebingen Neuro Campus, Eberhard Karls University Tuebingen, Germany
| | - Robert Guggenberger
- Division of Functional and Restorative Neurosurgery, Tuebingen Neuro Campus, Eberhard Karls University Tuebingen, Germany
| | - Vladislav Royter
- Division of Functional and Restorative Neurosurgery, Tuebingen Neuro Campus, Eberhard Karls University Tuebingen, Germany
| | - Alireza Gharabaghi
- Division of Functional and Restorative Neurosurgery, Tuebingen Neuro Campus, Eberhard Karls University Tuebingen, Germany.
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Catrambone V, Greco A, Averta G, Bianchi M, Valenza G, Scilingo EP. Predicting Object-Mediated Gestures From Brain Activity: An EEG Study on Gender Differences. IEEE Trans Neural Syst Rehabil Eng 2019; 27:411-418. [DOI: 10.1109/tnsre.2019.2898469] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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14
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Rahma ON, Rahmatillah A. Drowsiness Analysis Using Common Spatial Pattern and Extreme Learning Machine Based on Electroencephalogram Signal. JOURNAL OF MEDICAL SIGNALS & SENSORS 2019; 9:130-136. [PMID: 31316907 PMCID: PMC6601224 DOI: 10.4103/jmss.jmss_54_18] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
An alarm system has become essential to prevent someone from drowsiness while driving, considering the high incidence due to fatigue or drowsiness. This study offered an alternative to overcome all the limitations provided by the conventional system to detect sleepiness based on the driver's brain electrical activity using wearable electroencephalogram (EEG), which is lighter and easy to use. The EEG signals were collected using EMOTIV Epoc + and then were decomposed into narrowband frequency, such as delta, theta, alpha, and beta using DWT. The relative power, as the result of feature extraction, then were processed further by calculating its variance using the common spatial pattern (CSP) method to optimize the accuracy of extreme learning machine (ELM). Comparison of relative power between awake and drowsy state showed that during the drowsy state, theta-wave, alpha-wave, and beta-wave were tend to be higher than in the awake state. However, despite with the help of ELM, the accuracy was not too high (below 87%). The feature extraction which continued by calculating its variance using CSP algorithm before classified by ELM obtained a high accuracy, even with small amount of data training. This showed that CSP combining with ELM could be useful to shorten the time in training/calibration session, yet still, obtained high accuracy in classifying the awake state and drowsy state. The overall average accuracy of testing ranged from 91.67% to 93.75%. This study could increase the ability of EEG in detecting drowsiness that is important to prevent the risk caused by driving in a drowsy state.
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Affiliation(s)
| | - Akif Rahmatillah
- Department of Physics, Airlangga University, Surabaya, Indonesia
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Hong J, Qin X, Li J, Niu J, Wang W. Signal processing algorithms for motor imagery brain-computer interface: State of the art. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-181309] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Jie Hong
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
| | - Xiansheng Qin
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
| | - Jing Li
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
| | - Junlong Niu
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
| | - Wenjie Wang
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
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Monge-Pereira E, Ibañez-Pereda J, Alguacil-Diego IM, Serrano JI, Spottorno-Rubio MP, Molina-Rueda F. Use of Electroencephalography Brain-Computer Interface Systems as a Rehabilitative Approach for Upper Limb Function After a Stroke: A Systematic Review. PM R 2017; 9:918-932. [PMID: 28512066 DOI: 10.1016/j.pmrj.2017.04.016] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Revised: 03/12/2017] [Accepted: 04/19/2017] [Indexed: 10/19/2022]
Abstract
BACKGROUND Brain-computer interface (BCI) systems have been suggested as a promising tool for neurorehabilitation. However, to date, there is a lack of homogeneous findings. Furthermore, no systematic reviews have analyzed the degree of validation of these interventions for upper limb (UL) motor rehabilitation poststroke. OBJECTIVES The study aims were to compile all available studies that assess an UL intervention based on an electroencephalography (EEG) BCI system in stroke; to analyze the methodological quality of the studies retrieved; and to determine the effects of these interventions on the improvement of motor abilities. TYPE: This was a systematic review. LITERATURE SURVEY Searches were conducted in PubMed, PEDro, Embase, Cumulative Index to Nursing and Allied Health, Web of Science, and Cochrane Central Register of Controlled Trial from inception to September 30, 2015. METHODOLOGY This systematic review compiles all available studies that assess UL intervention based on an EEG-BCI system in patients with stroke, analyzing their methodological quality using the Critical Review Form for Quantitative Studies, and determining the grade of recommendation of these interventions for improving motor abilities as established by the Oxford Centre for Evidence-based Medicine. The articles were selected according to the following criteria: studies evaluating an EEG-based BCI intervention; studies including patients with a stroke and hemiplegia, regardless of lesion origin or temporal evolution; interventions using an EEG-based BCI to restore functional abilities of the affected UL, regardless of the interface used or its combination with other therapies; and studies using validated tools to evaluate motor function. SYNTHESIS After the literature search, 13 articles were included in this review: 4 studies were randomized controlled trials; 1 study was a controlled study; 4 studies were case series studies; and 4 studies were case reports. The methodological quality of the included papers ranged from 6 to 15, and the level of evidence varied from 1b to 5. The articles included in this review involved a total of 141 stroke patients. CONCLUSIONS This systematic review suggests that BCI interventions may be a promising rehabilitation approach in subjects with stroke. LEVEL OF EVIDENCE II.
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Affiliation(s)
- Esther Monge-Pereira
- Motion Analysis, Biomechanics, Ergonomy and Motor Control Laboratory (LAMBECOM group), Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine Department, Health Sciences Faculty, Rey Juan Carlos University, Alcorcón, Madrid, Spain; Departamento de Fisioterapia, Terapia Ocupacional, Rehabilitación y Medicina Física, Universidad Rey Juan Carlos, Alcorcón (Madrid), Avda. de Atenas, s/n. CP, 28922, Spain(∗).
| | - Jaime Ibañez-Pereda
- Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, London, United Kingdom(†)
| | - Isabel M Alguacil-Diego
- Motion Analysis, Biomechanics, Ergonomy and Motor Control Laboratory (LAMBECOM group), Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine Department, Health Sciences Faculty, Rey Juan Carlos University, Alcorcón, Madrid, Spain(‡)
| | - Jose I Serrano
- Neural and Cognitive Engineering Group, Centro de Automática y Robótica, (CSIC), Arganda del Rey, Spain(§)
| | | | - Francisco Molina-Rueda
- Motion Analysis, Biomechanics, Ergonomy and Motor Control Laboratory (LAMBECOM group), Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine Department, Health Sciences Faculty, Rey Juan Carlos University, Alcorcón, Madrid, Spain(¶)
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Classification of motor imagery electroencephalography signals using spiking neurons with different input encoding strategies. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2767-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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