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Guan S, Yuan Z, Wang F, Li J, Kang X, Lu B. Multi-class Motor Imagery Recognition of Single Joint in Upper Limb Based on Multi-domain Feature Fusion. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11185-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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de Seta V, Toppi J, Colamarino E, Molle R, Castellani F, Cincotti F, Mattia D, Pichiorri F. Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients. Front Hum Neurosci 2022; 16:1016862. [PMID: 36483633 PMCID: PMC9722732 DOI: 10.3389/fnhum.2022.1016862] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 10/26/2022] [Indexed: 10/05/2023] Open
Abstract
Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to improve classification performances. However, in a rehabilitative context, brain and muscular features should be extracted to promote a favorable motor outcome, reinforcing not only the volitional control in the central motor system, but also the effective projection of motor commands to target muscles, i.e., central-to-peripheral communication. For this reason, we considered cortico-muscular coupling (CMC) as a feature for a h-BCI devoted to post-stroke upper limb motor rehabilitation. In this study, we performed a pseudo-online analysis on 13 healthy participants (CTRL) and 12 stroke patients (EXP) during executed (CTRL, EXP unaffected arm) and attempted (EXP affected arm) hand grasping and extension to optimize the translation of CMC computation and CMC-based movement detection from offline to online. Results showed that updating the CMC computation every 125 ms (shift of the sliding window) and accumulating two predictions before a final classification decision were the best trade-off between accuracy and speed in movement classification, independently from the movement type. The pseudo-online analysis on stroke participants revealed that both attempted and executed grasping/extension can be classified through a CMC-based movement detection with high performances in terms of classification speed (mean delay between movement detection and EMG onset around 580 ms) and accuracy (hit rate around 85%). The results obtained by means of this analysis will ground the design of a novel non-invasive h-BCI in which the control feature is derived from a combined EEG and EMG connectivity pattern estimated during upper limb movement attempts.
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Affiliation(s)
- Valeria de Seta
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Jlenia Toppi
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Emma Colamarino
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Rita Molle
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Filippo Castellani
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Febo Cincotti
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Donatella Mattia
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Floriana Pichiorri
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
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Letourneau S, Zewdie ET, Jadavji Z, Andersen J, Burkholder LM, Kirton A. Clinician awareness of brain computer interfaces: a Canadian national survey. J Neuroeng Rehabil 2020; 17:2. [PMID: 31907010 PMCID: PMC6945584 DOI: 10.1186/s12984-019-0624-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 11/13/2019] [Indexed: 12/13/2022] Open
Abstract
Background Individuals with severe neurological disabilities but preserved cognition, including children, are often precluded from connecting with their environments. Brain computer interfaces (BCI) are a potential solution where advancing technologies create new clinical opportunities. We evaluated clinician awareness as a modifiable barrier to progress and identified eligible populations. Methods We executed a national, population-based, cross-sectional survey of physician specialists caring for persons with severe disability. An evidence- and experience-based survey had three themes: clinician BCI knowledge, eligible populations, and potential impact. A BCI knowledge index was created and scored. Canadian adult and pediatric neurologists, physiatrists and a subset of developmental pediatricians were contacted. Secure, web-based software administered the survey via email with online data collection. Results Of 922 valid emails (664 neurologists, 253 physiatrists), 137 (15%) responded. One third estimated that ≥10% of their patients had severe neurological disability with cognitive capacity. BCI knowledge scores were low with > 40% identifying as less than “vaguely aware” and only 15% as “somewhat familiar” or better. Knowledge did not differ across specialties. Only 6 physicians (4%) had patients using BCI. Communication and wheelchair control rated highest for potentially improving quality of life. Most (81%) felt BCI had high potential to improve quality of life. Estimates suggested that > 13,000 Canadians (36 M population) might benefit from BCI technologies. Conclusions Despite high potential and thousands of patients who might benefit, BCI awareness among clinicians caring for disabled persons is poor. Further, functional priorities for BCI applications may differ between medical professionals and potential BCI users, perhaps reflecting that clinicians possess a less accurate understanding of the desires and needs of potential end-users. Improving knowledge and engaging both clinicians and patients could facilitate BCI program development to improve patient outcomes.
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Affiliation(s)
- Sasha Letourneau
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W., Calgary, AB, T2N 1N4, Canada
| | - Ephrem Takele Zewdie
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W., Calgary, AB, T2N 1N4, Canada
| | - Zeanna Jadavji
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W., Calgary, AB, T2N 1N4, Canada.,Clinical Neurosciences, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W, Calgary, AB, AB T2N 1N4, Canada
| | - John Andersen
- Department of Pediatrics, University of Alberta, 116 St. and 85 Ave, Edmonton, AB T6G 2R3, Canada
| | - Lee M Burkholder
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W., Calgary, AB, T2N 1N4, Canada.,Clinical Neurosciences, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W, Calgary, AB, AB T2N 1N4, Canada
| | - Adam Kirton
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W., Calgary, AB, T2N 1N4, Canada. .,Clinical Neurosciences, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W, Calgary, AB, AB T2N 1N4, Canada. .,Department of Pediatrics, University of Alberta, 116 St. and 85 Ave, Edmonton, AB T6G 2R3, Canada. .,Alberta Children's Hospital Research Institute, 28 Oki Drive S.W, Calgary, AB, T3B6A8, Canada. .,Hotchkiss Brain Institute, University of Calgary, 2500 University Drive N.W, Calgary, AB, T2N 1N4, Canada.
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Guan S, Zhao K, Yang S. Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:5627156. [PMID: 30804988 PMCID: PMC6360593 DOI: 10.1155/2019/5627156] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 12/20/2018] [Accepted: 01/03/2019] [Indexed: 11/30/2022]
Abstract
This paper proposes a novel classification framework and a novel data reduction method to distinguish multiclass motor imagery (MI) electroencephalography (EEG) for brain computer interface (BCI) based on the manifold of covariance matrices in a Riemannian perspective. For method 1, a subject-specific decision tree (SSDT) framework with filter geodesic minimum distance to Riemannian mean (FGMDRM) is designed to identify MI tasks and reduce the classification error in the nonseparable region of FGMDRM. Method 2 includes a feature extraction algorithm and a classification algorithm. The feature extraction algorithm combines semisupervised joint mutual information (semi-JMI) with general discriminate analysis (GDA), namely, SJGDA, to reduce the dimension of vectors in the Riemannian tangent plane. And the classification algorithm replaces the FGMDRM in method 1 with k-nearest neighbor (KNN), named SSDT-KNN. By applying method 2 on BCI competition IV dataset 2a, the kappa value has been improved from 0.57 to 0.607 compared to the winner of dataset 2a. And method 2 also obtains high recognition rate on the other two datasets.
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Affiliation(s)
- Shan Guan
- School of Mechanical Engineering, Northeast Electric Power University, 132012 Jilin, China
| | - Kai Zhao
- School of Mechanical Engineering, Northeast Electric Power University, 132012 Jilin, China
| | - Shuning Yang
- School of Mechanical Engineering, Northeast Electric Power University, 132012 Jilin, China
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Use of the Stockwell Transform in the Detection of P300 Evoked Potentials with Low-Cost Brain Sensors. SENSORS 2018; 18:s18051483. [PMID: 29747374 PMCID: PMC5982572 DOI: 10.3390/s18051483] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Revised: 04/14/2018] [Accepted: 04/21/2018] [Indexed: 11/17/2022]
Abstract
The evoked potential is a neuronal activity that originates when a stimulus is presented. To achieve its detection, various techniques of brain signal processing can be used. One of the most studied evoked potentials is the P300 brain wave, which usually appears between 300 and 500 ms after the stimulus. Currently, the detection of P300 evoked potentials is of great importance due to its unique properties that allow the development of applications such as spellers, lie detectors, and diagnosis of psychiatric disorders. The present study was developed to demonstrate the usefulness of the Stockwell transform in the process of identifying P300 evoked potentials using a low-cost electroencephalography (EEG) device with only two brain sensors. The acquisition of signals was carried out using the Emotiv EPOC® device—a wireless EEG headset. In the feature extraction, the Stockwell transform was used to obtain time-frequency information. The algorithms of linear discriminant analysis and a support vector machine were used in the classification process. The experiments were carried out with 10 participants; men with an average age of 25.3 years in good health. In general, a good performance (75⁻92%) was obtained in identifying P300 evoked potentials.
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