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Chen Y, Wang F, Li T, Zhao L, Gong A, Nan W, Ding P, Fu Y. Considerations and discussions on the clear definition and definite scope of brain-computer interfaces. Front Neurosci 2024; 18:1449208. [PMID: 39161655 PMCID: PMC11330831 DOI: 10.3389/fnins.2024.1449208] [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: 06/14/2024] [Accepted: 07/22/2024] [Indexed: 08/21/2024] Open
Abstract
Brain-computer interface (BCI) is a revolutionizing human-computer interaction with potential applications in both medical and non-medical fields, emerging as a cutting-edge and trending research direction. Increasing numbers of groups are engaging in BCI research and development. However, in recent years, there has been some confusion regarding BCI, including misleading and hyped propaganda about BCI, and even non-BCI technologies being labeled as BCI. Therefore, a clear definition and a definite scope for BCI are thoroughly considered and discussed in the paper, based on the existing definitions of BCI, including the six key or essential components of BCI. In the review, different from previous definitions of BCI, BCI paradigms and neural coding are explicitly included in the clear definition of BCI provided, and the BCI user (the brain) is clearly identified as a key component of the BCI system. Different people may have different viewpoints on the definition and scope of BCI, as well as some related issues, which are discussed in the article. This review argues that a clear definition and definite scope of BCI will benefit future research and commercial applications. It is hoped that this review will reduce some of the confusion surrounding BCI and promote sustainable development in this field.
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Affiliation(s)
- Yanxiao Chen
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Fan Wang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Tianwen Li
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
- Faculty of Science, Kunming University of Science and Technology, Kunming, China
| | - Lei Zhao
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
- Faculty of Science, Kunming University of Science and Technology, Kunming, China
| | - Anmin Gong
- School of Information Engineering, Chinese People’s Armed Police Force Engineering University, Xi’an, China
| | - Wenya Nan
- School of Psychology, Shanghai Normal University, Shanghai, China
| | - Peng Ding
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Yunfa Fu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
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Meng J, Li S, Li G, Luo R, Sheng X, Zhu X. A model-based brain switch via periodic motor imagery modulation for asynchronous brain-computer interfaces. J Neural Eng 2024; 21:046035. [PMID: 39029496 DOI: 10.1088/1741-2552/ad6595] [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: 02/05/2024] [Accepted: 07/19/2024] [Indexed: 07/21/2024]
Abstract
Objective.Brain switches provide a tangible solution to asynchronized brain-computer interface, which decodes user intention without a pre-programmed structure. However, most brain switches based on electroencephalography signals have high false positive rates (FPRs), resulting in less practicality. This research aims to improve the operating mode and usability of the brain switch.Approach.Here, we propose a novel virtual physical model-based brain switch that leverages periodic active modulation. An optimization problem of minimizing the triggering time subject to a required FPR is formulated, numerical and analytical approximate solutions are obtained based on the model.Main results.Our motor imagery (MI)-based brain switch can reach 0.8FP/h FPR with a median triggering time of 58 s. We evaluated the proposed brain switch during online device control, and their average FPRs substantially outperformed the conventional brain switches in the literature. We further improved the proposed brain switch with the Common Spatial Pattern (CSP) and optimization method. An average FPR of 0.3 FPs/h was obtained for the MI-CSP-based brain switch, and the average triggering time improved to 21.6 s.Significance.This study provides a new approach that could significantly reduce the brain switch's FPR to less than 1 Fps/h, which was less than 10% of the FPR (decreasing by more than a magnitude of order) by other endogenous methods, and the reaction time was comparable to the state-of-the-art approaches. This represents a significant advancement over the current non-invasive asynchronous BCI and will open widespread avenues for translating BCI towards clinical applications.
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Affiliation(s)
- Jianjun Meng
- Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Songwei Li
- Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Guangye Li
- Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Ruijie Luo
- Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Xinjun Sheng
- Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Xiangyang Zhu
- Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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Jochumsen M, Lavesen ER, Griem AB, Falkenberg-Andersen C, Jensen SKG. The Effect of Caffeine on Movement-Related Cortical Potential Morphology and Detection. SENSORS (BASEL, SWITZERLAND) 2024; 24:4030. [PMID: 38931814 PMCID: PMC11209428 DOI: 10.3390/s24124030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 06/15/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024]
Abstract
Movement-related cortical potential (MRCP) is observed in EEG recordings prior to a voluntary movement. It has been used for e.g., quantifying motor learning and for brain-computer interfacing (BCIs). The MRCP amplitude is affected by various factors, but the effect of caffeine is underexplored. The aim of this study was to investigate if a cup of coffee with 85 mg caffeine modulated the MRCP amplitude and the classification of MRCPs versus idle activity, which estimates BCI performance. Twenty-six healthy participants performed 2 × 100 ankle dorsiflexion separated by a 10-min break before a cup of coffee was consumed, followed by another 100 movements. EEG was recorded during the movements and divided into epochs, which were averaged to extract three average MRCPs that were compared. Also, idle activity epochs were extracted. Features were extracted from the epochs and classified using random forest analysis. The MRCP amplitude did not change after consuming caffeine. There was a slight increase of two percentage points in the classification accuracy after consuming caffeine. In conclusion, a cup of coffee with 85 mg caffeine does not affect the MRCP amplitude, and improves MRCP-based BCI performance slightly. The findings suggest that drinking coffee is only a minor confounder in MRCP-related studies.
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Affiliation(s)
- Mads Jochumsen
- Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark
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Keough JR, Irvine B, Kelly D, Wrightson J, Comaduran Marquez D, Kinney-Lang E, Kirton A. Fatigue in children using motor imagery and P300 brain-computer interfaces. J Neuroeng Rehabil 2024; 21:61. [PMID: 38658998 PMCID: PMC11040843 DOI: 10.1186/s12984-024-01349-2] [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: 04/17/2023] [Accepted: 03/25/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND Brain-computer interface (BCI) technology offers children with quadriplegic cerebral palsy unique opportunities for communication, environmental exploration, learning, and game play. Research in adults demonstrates a negative impact of fatigue on BCI enjoyment, while effects on BCI performance are variable. To date, there have been no pediatric studies of BCI fatigue. The purpose of this study was to assess the effects of two different BCI paradigms, motor imagery and visual P300, on the development of self-reported fatigue and an electroencephalography (EEG) biomarker of fatigue in typically developing children. METHODS Thirty-seven typically-developing school-aged children were recruited to a prospective, crossover study. Participants attended three sessions: (A) motor imagery-BCI, (B) visual P300-BCI, and (C) video viewing (control). The motor imagery task involved an imagined left- or right-hand squeeze. The P300 task involved attending to one square on a 3 × 3 grid during a random single flash sequence. Each paradigm had respective calibration periods and a similar visual counting game. Primary outcomes were self-reported fatigue and the power of the EEG alpha band both collected during resting-state periods pre- and post-task. Self-reported fatigue was measured using a 10-point visual analog scale. EEG alpha band power was calculated as the integrated power spectral density from 8 to 12 Hz of the EEG spectrum. RESULTS Thirty-two children completed the protocol (age range 7-16, 63% female). Self-reported fatigue and EEG alpha band power increased across all sessions (F(1,155) = 33.9, p < 0.001; F = 5.0(1,149), p = 0.027 respectively). No differences in fatigue development were observed between session types. There was no correlation between self-reported fatigue and EEG alpha band power change. BCI performance varied between participants and paradigms as expected but was not associated with self-reported fatigue or EEG alpha band power. CONCLUSION Short periods (30-mintues) of BCI use can increase self-reported fatigue and EEG alpha band power to a similar degree in children performing motor imagery and P300 BCI paradigms. Performance was not associated with our measures of fatigue; the impact of fatigue on useability and enjoyment is unclear. Our results reflect the variability of fatigue and the BCI experience more broadly in children and warrant further investigation.
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Affiliation(s)
- Joanna Rg Keough
- Departments of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Brian Irvine
- Departments of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Dion Kelly
- Departments of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - James Wrightson
- Departments of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Daniel Comaduran Marquez
- Departments of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Eli Kinney-Lang
- Departments of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Adam Kirton
- Departments of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
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Chen K, Liu Z, Li Z, Liu Q, Ai Q, Ma L. An improved multi-source domain adaptation network for inter-subject mental fatigue detection based on DANN. BIOMED ENG-BIOMED TE 2023:bmt-2022-0354. [PMID: 36797837 DOI: 10.1515/bmt-2022-0354] [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: 09/05/2022] [Accepted: 02/01/2023] [Indexed: 02/18/2023]
Abstract
OBJECTIVES Electroencephalogram (EEG) is often used to detect mental fatigue because of its real-time characteristic and objective nature. However, because of the individual variability of EEG among different individuals, tedious and time-consuming calibration sessions are needed. METHODS Therefore, we propose a multi-source domain adaptation network for inter-subject mental fatigue detection named FLDANN, which is short for focal loss based domain-adversarial training of neural network. As for mental state feature extraction, power spectrum density is extracted based on the Welch method from four sub-bands of EEG signals. The features of the source domain and target domain are fed into the FLDANN network. The contributions of FLDANN include: (1) It uses the idea of adversarial to reduce feature differences between the source and target domain. (2) A loss function named focal loss is used to assign weights to source and target domain samples. RESULTS The experiment result shows that when the number of the source domains increases, the classification accuracy of domain-adversarial training of neural network (DANN) gradually decreases and finally tends to be stable. The proposed method achieves an accuracy of 84.10% ± 8.75% on the SEED-VIG dataset and 65.42% ± 7.47% on the self-designed dataset. In addition, the proposed method is compared with other domain adaptation methods and the results show that the proposed method outperforms those state-of-the-art methods. CONCLUSIONS The result proves that the proposed method is able to solve the problem of individual differences across subjects and to solve the problem of low classification performance of multi-source domain transfer learning.
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Affiliation(s)
- Kun Chen
- School of Information Engineering, Wuhan University of Technology, Wuhan, China
| | - Zhiyong Liu
- School of Information Engineering, Wuhan University of Technology, Wuhan, China
| | - Zhilei Li
- School of Information Engineering, Wuhan University of Technology, Wuhan, China
| | - Quan Liu
- School of Information Engineering, Wuhan University of Technology, Wuhan, China
| | - Qingsong Ai
- School of Information Engineering, Wuhan University of Technology, Wuhan, China.,School of Computer Science and Information Engineering, Hubei University, Wuhan, China
| | - Li Ma
- School of Information Engineering, Wuhan University of Technology, Wuhan, China
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Li X, Liu Y, Kang J, Sun Y, Xu Y, Yuan Y, Han Y, Xie P. Identifying Amnestic Mild Cognitive Impairment With Convolutional Neural Network Adapted to the Spectral Entropy Heat Map of the Electroencephalogram. Front Hum Neurosci 2022; 16:924222. [PMID: 35874151 PMCID: PMC9298556 DOI: 10.3389/fnhum.2022.924222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
Mild cognitive impairment (MCI) is a preclinical stage of Alzheimer's disease (AD), and early diagnosis and intervention may delay its deterioration. However, the electroencephalogram (EEG) differences between patients with amnestic mild cognitive impairment (aMCI) and healthy controls (HC) subjects are not as significant compared to those with AD. This study addresses this situation by proposing a computer-aided diagnostic method that also aims to improve model performance and assess the sensitive areas of the subject's brain. The EEG data of 46 subjects (20HC/26aMCI) were enhanced with windowed moving segmentation and transformed from 1D temporal data to 2D spectral entropy images to measure the efficient information in the time-frequency domain from the point of view of information entropy; A novel convolution module is devised, which considerably reduces the number of model learning parameters and saves computing resources on the premise of ensuring diagnostic performance; One more thing, the cognitive diagnostic contribution of the corresponding channels in each brain region was measured by the weight coefficient of the input and convolution unit. Our results showed that when the segmental window overlap rate was increased from 0 to 75%, the corresponding generalization accuracy increased from 91.673 ± 0.9578% to 94.642 ± 0.4035%; Approximately 35% reduction in model learnable parameters by optimizing the network structure while maintaining accuracy; The top four channels were FP1, F7, T5, and F4, corresponding to the frontal and temporal lobes, in descending order of the mean value of the weight coefficients. This paper proposes a novel method based on spectral entropy image and convolutional neural network (CNN), which provides a new perspective for the identifying of aMCI based on EEG.
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Affiliation(s)
- Xin Li
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
| | - Yi Liu
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
| | - Jiannan Kang
- College of Electronic and Information Engineering, Hebei University, Baoding, China
| | - Yu Sun
- China-Japan Friendship Hospital, Beijing, China
| | - Yonghong Xu
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
| | - Yi Yuan
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Ping Xie
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
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