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Li R, Bai D, Li Z, Yang S, Liu W, Zhang Y, Zhou J, Luo J, Wang W. The SSHVEP Paradigm-Based Brain Controlled Method for Grasping Robot Using MVMD Combined CNN Model. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2564-2578. [PMID: 38980788 DOI: 10.1109/tnsre.2024.3425636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
In recent years, the steady-state visual evoked potentials (SSVEP) based brain control method has been employed to help people with disabilities because of its advantages of high information transmission rate and low training time. However, the existing SSVEP brain control methods cannot adapt to dynamic or unstructured environments. Moreover, the recognition accuracy from the conventional decoding algorithm still needs to improve. To address the above problems, this study proposed a steady-state hybrid visual evoked potentials (SSHVEP) paradigm using the grasping targets in their environment to improve the connection between the subjects' and their dynamic environments. Moreover, a novel EEG decoding method, using the multivariate variational mode decomposition (MVMD) algorithm for adaptive sub-band division and convolutional neural network (CNN) for target recognition, was applied to improve the decoding accuracy of the SSHVEPs. 18 subjects participated in the offline and online experiments. The offline accuracy across 18 subjects by the 9-target SSHVEP paradigm was up to 95.41 ± 2.70 %, which is a 5.80% improvement compared to the conventional algorithm. To further validate the performance of the proposed method, the brain-controlled grasping robot system using the SSHVEP paradigm was built. The average accuracy reached 93.21 ± 10.18 % for the online experiment. All the experimental results demonstrated the effectiveness of the brain-computer interaction method based on the SSHVEP paradigm and the MVMD combined CNN algorithm studied in this paper.
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2
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Qin Y, Li B, Wang W, Shi X, Peng C, Lu Y. Classification Algorithm for fNIRS-based Brain Signals Using Convolutional Neural Network with Spatiotemporal Feature Extraction Mechanism. Neuroscience 2024; 542:59-68. [PMID: 38369007 DOI: 10.1016/j.neuroscience.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 01/17/2024] [Accepted: 02/14/2024] [Indexed: 02/20/2024]
Abstract
Brain Computer Interface (BCI) is a highly promising human-computer interaction method that can utilize brain signals to control external devices. BCI based on functional near-infrared spectroscopy (fNIRS) is considered a relatively new and promising paradigm. fNIRS is a technique of measuring functional changes in cerebral hemodynamics. It detects changes in the hemodynamic activity of the cerebral cortex by measuring oxyhemoglobin and deoxyhemoglobin (HbR) concentrations and inversely predicts the neural activity of the brain. At the present time, Deep learning (DL) methods have not been widely used in fNIRS decoding, and there are fewer studies considering both spatial and temporal dimensions for fNIRS classification. To solve these problems, we proposed an end-to-end hybrid neural network for feature extraction of fNIRS. The method utilizes a spatial-temporal convolutional layer for automatic extraction of temporally valid information and uses a spatial attention mechanism to extract spatially localized information. A temporal convolutional network (TCN) is used to further utilize the temporal information of fNIRS before the fully connected layer. We validated our approach on a publicly available dataset including 29 subjects, including left-hand and right-hand motor imagery (MI), mental arithmetic (MA), and a baseline task. The results show that the method has few training parameters and high accuracy, providing a meaningful reference for BCI development.
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Affiliation(s)
- Yuxin Qin
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Baojiang Li
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Wenlong Wang
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Xingbin Shi
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Cheng Peng
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Yifan Lu
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
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Mu J, Liu S, Burkitt AN, Grayden DB. Multi-frequency steady-state visual evoked potential dataset. Sci Data 2024; 11:26. [PMID: 38177151 PMCID: PMC10766626 DOI: 10.1038/s41597-023-02841-5] [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: 04/06/2023] [Accepted: 12/11/2023] [Indexed: 01/06/2024] Open
Abstract
The Steady-State Visual Evoked Potential (SSVEP) is a widely used modality in Brain-Computer Interfaces (BCIs). Existing research has demonstrated the capabilities of SSVEP that use single frequencies for each target in various applications with relatively small numbers of commands required in the BCI. Multi-frequency SSVEP has been developed to extend the capability of single-frequency SSVEP to tasks that involve large numbers of commands. However, the development on multi-frequency SSVEP methodologies is falling behind compared to the number of studies with single-frequency SSVEP. This dataset was constructed to promote research in multi-frequency SSVEP by making SSVEP signals collected with different frequency stimulation settings publicly available. In this dataset, SSVEPs were collected from 35 participants using single-, dual-, and tri-frequency stimulation and with three different multi-frequency stimulation variants.
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Affiliation(s)
- Jing Mu
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, 3010, Australia.
- Graeme Clark Institute, The University of Melbourne, Parkville, Victoria, 3010, Australia.
| | - Shuo Liu
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Anthony N Burkitt
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, 3010, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, 3010, Australia
- Graeme Clark Institute, The University of Melbourne, Parkville, Victoria, 3010, Australia
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Moreno-Alcayde Y, Traver VJ, Leiva LA. Sneaky emotions: impact of data partitions in affective computing experiments with brain-computer interfacing. Biomed Eng Lett 2024; 14:103-113. [PMID: 38186953 PMCID: PMC10769959 DOI: 10.1007/s13534-023-00316-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/07/2023] [Accepted: 08/22/2023] [Indexed: 01/09/2024] Open
Abstract
Brain-Computer Interfacing (BCI) has shown promise in Machine Learning (ML) for emotion recognition. Unfortunately, how data are partitioned in training/test splits is often overlooked, which makes it difficult to attribute research findings to actual modeling improvements or to partitioning issues. We introduce the "data transfer rate" construct (i.e., how much data of the test samples are seen during training) and use it to examine data partitioning effects under several conditions. As a use case, we consider emotion recognition in videos using electroencephalogram (EEG) signals. Three data splits are considered, each representing a relevant BCI task: subject-independent (affective decoding), video-independent (affective annotation), and time-based (feature extraction). Model performance may change significantly (ranging e.g. from 50% to 90%) depending on how data is partitioned, in classification accuracy. This was evidenced in all experimental conditions tested. Our results show that (1) for affective decoding, it is hard to achieve performance above the baseline case (random classification) unless some data of the test subjects are considered in the training partition; (2) for affective annotation, having data from the same subject in training and test partitions, even though they correspond to different videos, also increases performance; and (3) later signal segments are generally more discriminative, but it is the number of segments (data points) what matters the most. Our findings not only have implications in how brain data are managed, but also in how experimental conditions and results are reported.
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Affiliation(s)
- Yoelvis Moreno-Alcayde
- Institute of New Imaging Technologies, Universitat Jaume I, Av. Vicent Sos Baynat, s/n, Castellón, 12071 Castellón Spain
| | - V. Javier Traver
- Institute of New Imaging Technologies, Universitat Jaume I, Av. Vicent Sos Baynat, s/n, Castellón, 12071 Castellón Spain
| | - Luis A. Leiva
- University of Luxembourg, Esch-sur-Alzette, Luxembourg
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Zafar A, Hussain SJ, Ali MU, Lee SW. Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23073714. [PMID: 37050774 PMCID: PMC10098559 DOI: 10.3390/s23073714] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/23/2023] [Accepted: 03/30/2023] [Indexed: 06/01/2023]
Abstract
In recent decades, the brain-computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce the dataset's dimensionality, increase the computing effectiveness, and enhance the BCI's performance. Using activity-related features leads to a high classification rate among the desired tasks. This study presents a wrapper-based metaheuristic feature selection framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, the temporal statistical features (i.e., the mean, slope, maximum, skewness, and kurtosis) were computed from all the available channels to form a training vector. Seven metaheuristic optimization algorithms were tested for their classification performance using a k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search optimization, the firefly algorithm, the bat algorithm, flower pollination optimization, whale optimization, and grey wolf optimization (GWO). The presented approach was validated based on an available online dataset of motor imagery (MI) and mental arithmetic (MA) tasks from 29 healthy subjects. The results showed that the classification accuracy was significantly improved by utilizing the features selected from the metaheuristic optimization algorithms relative to those obtained from the full set of features. All of the abovementioned metaheuristic algorithms improved the classification accuracy and reduced the feature vector size. The GWO yielded the highest average classification rates (p < 0.01) of 94.83 ± 5.5%, 92.57 ± 6.9%, and 85.66 ± 7.3% for the MA, MI, and four-class (left- and right-hand MI, MA, and baseline) tasks, respectively. The presented framework may be helpful in the training phase for selecting the appropriate features for robust fNIRS-based BCI applications.
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Affiliation(s)
- Amad Zafar
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Shaik Javeed Hussain
- Department of Electrical and Electronics, Global College of Engineering and Technology, Muscat 112, Oman
| | - Muhammad Umair Ali
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Seung Won Lee
- Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
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Zafar A, Dad Kallu K, Atif Yaqub M, Ali MU, Hyuk Byun J, Yoon M, Su Kim K. A Hybrid GCN and Filter-Based Framework for Channel and Feature Selection: An fNIRS-BCI Study. INT J INTELL SYST 2023. [DOI: 10.1155/2023/8812844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
In this study, a channel and feature selection methodology is devised for brain-computer interface (BCI) applications using functional near-infrared spectroscopy (fNIRS). A graph convolutional network (GCN) is employed to select the appropriate and correlated fNIRS channels. Furthermore, in the feature extraction phase, the performance of two filter-based feature selection algorithms, (i) the minimum redundancy maximum relevance (mRMR) and (ii) ReliefF, is investigated. The five most commonly used temporal statistical features (i.e., mean, slope, maximum, skewness, and kurtosis) are used, whereas the conventional support vector machine (SVM) is utilized as a classifier for training and testing. The proposed methodology is validated using an available online dataset of motor imagery (left- and right-hand), mental arithmetic, and baseline tasks. First, the efficacy of the proposed methodology is shown for two-class BCI applications (i.e., left- vs. right-hand motor imagery and mental arithmetic vs. baseline). Second, the proposed framework is applied to four-class BCI applications (i.e., left- vs. right-hand motor imagery vs. mental arithmetic vs. baseline). The results show that the number of appropriate channels and features was significantly reduced, resulting in a significant increase in classification accuracy for both two-class and four-class BCI applications, respectively. Furthermore, both mRMR (i.e., 87.8% for motor imagery, 87.1% for mental arithmetic, and 78.7% for four-class) and ReliefF (i.e., 90.7% for motor imagery, 93.7% for mental arithmetic, and 81.6% for four-class) yielded high average classification accuracy
. However, the results of the ReliefF algorithm are more stable and significant.
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Zhao ZP, Nie C, Jiang CT, Cao SH, Tian KX, Yu S, Gu JW. Modulating Brain Activity with Invasive Brain-Computer Interface: A Narrative Review. Brain Sci 2023; 13:brainsci13010134. [PMID: 36672115 PMCID: PMC9856340 DOI: 10.3390/brainsci13010134] [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: 11/01/2022] [Revised: 12/17/2022] [Accepted: 01/05/2023] [Indexed: 01/15/2023] Open
Abstract
Brain-computer interface (BCI) can be used as a real-time bidirectional information gateway between the brain and machines. In particular, rapid progress in invasive BCI, propelled by recent developments in electrode materials, miniature and power-efficient electronics, and neural signal decoding technologies has attracted wide attention. In this review, we first introduce the concepts of neuronal signal decoding and encoding that are fundamental for information exchanges in BCI. Then, we review the history and recent advances in invasive BCI, particularly through studies using neural signals for controlling external devices on one hand, and modulating brain activity on the other hand. Specifically, regarding modulating brain activity, we focus on two types of techniques, applying electrical stimulation to cortical and deep brain tissues, respectively. Finally, we discuss the related ethical issues concerning the clinical application of this emerging technology.
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Affiliation(s)
- Zhi-Ping Zhao
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Chuang Nie
- Strategic Support Force Medical Center, Beijing 100101, China
| | - Cheng-Teng Jiang
- Savaid Medical School, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Sheng-Hao Cao
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kai-Xi Tian
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (S.Y.); (J.-W.G.); Tel.: +86-010-8254-4786 (S.Y.); +86-010-6635-6729 (J.-W.G.)
| | - Jian-Wen Gu
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- Strategic Support Force Medical Center, Beijing 100101, China
- Correspondence: (S.Y.); (J.-W.G.); Tel.: +86-010-8254-4786 (S.Y.); +86-010-6635-6729 (J.-W.G.)
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Palumbo A. Microsoft HoloLens 2 in Medical and Healthcare Context: State of the Art and Future Prospects. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22207709. [PMID: 36298059 PMCID: PMC9611914 DOI: 10.3390/s22207709] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 09/29/2022] [Accepted: 10/07/2022] [Indexed: 05/08/2023]
Abstract
In the world reference context, although virtual reality, augmented reality and mixed reality have been emerging methodologies for several years, only today technological and scientific advances have made them suitable to revolutionize clinical care and medical contexts through the provision of enhanced functionalities and improved health services. This systematic review provides the state-of-the-art applications of the Microsoft® HoloLens 2 in a medical and healthcare context. Focusing on the potential that this technology has in providing digitally supported clinical care, also but not only in relation to the COVID-19 pandemic, studies that proved the applicability and feasibility of HoloLens 2 in a medical and healthcare scenario were considered. The review presents a thorough examination of the different studies conducted since 2019, focusing on HoloLens 2 medical sub-field applications, device functionalities provided to users, software/platform/framework used, as well as the study validation. The results provided in this paper could highlight the potential and limitations of the HoloLens 2-based innovative solutions and bring focus to emerging research topics, such as telemedicine, remote control and motor rehabilitation.
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Affiliation(s)
- Arrigo Palumbo
- Department of Medical and Surgical Sciences, Magna Græcia University, 88100 Catanzaro, Italy
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Xu DQ, Li MA. A dual alignment-based multi-source domain adaptation framework for motor imagery EEG classification. APPL INTELL 2022; 53:10766-10788. [PMID: 36039116 PMCID: PMC9402410 DOI: 10.1007/s10489-022-04077-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/08/2022] [Indexed: 11/25/2022]
Affiliation(s)
- Dong-qin Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 China
| | - Ming-ai Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124 China
- Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124 China
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Longo L, Wickens CD, Hancock PA, Hancock GM. Human Mental Workload: A Survey and a Novel Inclusive Definition. Front Psychol 2022; 13:883321. [PMID: 35719509 PMCID: PMC9201728 DOI: 10.3389/fpsyg.2022.883321] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 05/10/2022] [Indexed: 12/05/2022] Open
Abstract
Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. A second reason is that the three main classes of measures, which are self-report, task performance, and physiological indices, have been used in isolation or in pairs, but more rarely in conjunction all together. Multiple definitions complement each another and we propose a novel inclusive definition of mental workload to support the next generation of empirical-based research. Similarly, by comprehensively employing physiological, task-performance, and self-report measures, more robust assessments of mental workload can be achieved.
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Affiliation(s)
- Luca Longo
- Artificial Intelligence and Cognitive Load Lab, The Applied Intelligence Research Centre, School of Computer Science, Technological University Dublin, Dublin, Ireland
| | - Christoper D Wickens
- Department of Psychology, Colorado State University, Fort Collins, CO, United States
| | - Peter A Hancock
- Department of Psychology, Institute for Simulation and Training, University of Central Florida, Orlando, FL, United States
| | - Gabriela M Hancock
- Department of Psychology, California State University, Long Beach, CA, United States
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