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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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Lai E, Mai X, Ji M, Li S, Meng J. High-Frequency Discrete-Interval Binary Sequence in Asynchronous C-VEP-Based BCI for Visual Fatigue Reduction. IEEE J Biomed Health Inform 2024; 28:2769-2780. [PMID: 38442053 DOI: 10.1109/jbhi.2024.3373332] [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: 03/07/2024]
Abstract
In code-modulated visual evoked potential (c-VEP) based BCI systems, flickering visual stimuli may result in visual fatigue. Thus, we introduced a discrete-interval binary sequence (DIBS) as visual stimulus modulation, with its power spectrum optimized to emphasize high-frequency components (40 Hz-60 Hz). 8 and 17 subjects participated, respectively, in offline and online experiments on a 4-target asynchronous c-VEP-based BCI system designed to realize a high positive predictive value (PPV), a low false positive rate (FPR) during idle states, and a high true positive rate (TPR) in control states, while minimizing visual fatigue level. Two visual stimuli modulations were introduced and compared: a maximum length sequence (m-sequence) and the high-frequency discrete-interval binary sequence (DIBS). The decoding algorithm was compared among the canonical correlation analysis (CCA), the task-related component analysis (TRCA), and two approaches of sub-band component weight calculation (the traditional method and the proportional method) for FBCCA and FBTRCA. In the online experiments, the average PPV, FPR and TPR achieved, respectively [Formula: see text], [Formula: see text], [Formula: see text] with m-sequence, while [Formula: see text], [Formula: see text] and [Formula: see text] with DIBS. Estimated by objective eye-related metrics and a subjective questionnaire, the visual fatigue in DIBS cases is significantly smaller than that in m-sequence cases. In this study, the feasibility of a novel modulation approach for visual fatigue reduction was proved in an asynchronous c-VEP system, while maintaining comparable performance to existing methods, which provides further insights towards enhancing this field's long-term viability and user-friendliness.
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Zheng L, Dong Y, Tian S, Pei W, Gao X, Wang Y. A calibration-free c-VEP based BCI employing narrow-band random sequences. J Neural Eng 2024; 21:026023. [PMID: 38513290 DOI: 10.1088/1741-2552/ad3679] [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: 11/21/2023] [Accepted: 03/21/2024] [Indexed: 03/23/2024]
Abstract
Objective.Code-modulated visual evoked potential (c-VEP) based brain-computer interfaces (BCIs) exhibit high encoding efficiency. Nevertheless, the majority of c-VEP based BCIs necessitate an initial training or calibration session, particularly when the number of targets expands, which impedes the practicality. To address this predicament, this study introduces a calibration-free c-VEP based BCI employing narrow-band random sequences.Approach.For the encoding method, a series of random sequences were generated within a specific frequency band. The c-VEP signals were subsequently elicited through the application of on-type grid flashes that were modulated by these sequences. For the calibration-free decoding algorithm, filter-bank canonical correlation analysis (FBCCA) was utilized with the reference templates generated from the original sequences. Thirty-five subjects participated into an online BCI experiment. The performances of c-VEP based BCIs utilizing narrow-band random sequences with frequency bands of 15-25 Hz (NBRS-15) and 8-16 Hz (NBRS-8) were compared with that of a steady-state visual evoked potential (SSVEP) based BCI within a frequency range of 8-15.8 Hz.Main results.The offline analysis results demonstrated a substantial correlation between the c-VEPs and the original narrow-band random sequences. After parameter optimization, the calibration-free system employing the NBRS-15 frequency band achieved an average information transfer rate (ITR) of 78.56 ± 37.03 bits/min, which exhibited no significant difference compared to the performance of the SSVEP based system when utilizing FBCCA. The proposed system achieved an average ITR of 102.1 ± 57.59 bits/min in a simulation of a 1000-target BCI system.Significance.This study introduces a novel calibration-free c-VEP based BCI system employing narrow-band random sequences and shows great potential of the proposed system in achieving a large number of targets and high ITR.
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Affiliation(s)
- Li Zheng
- Laboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, People's Republic of China
| | - Yida Dong
- Laboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, People's Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Sen Tian
- Brain Machine Fusion Intelligence Institute, Suzhou 215133, People's Republic of China
| | - Weihua Pei
- Laboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, People's Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Xiaorong Gao
- Department of Biomedical Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| | - Yijun Wang
- Laboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, People's Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Chinese Institute for Brain Research, Beijing 102206, People's Republic of China
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Shi B, Chen X, Yue Z, Zeng F, Yin S, Wang B, Wang J. Feature optimization based on improved novel global harmony search algorithm for motor imagery electroencephalogram classification. Front Comput Neurosci 2022; 16:1004301. [PMID: 36589278 PMCID: PMC9801329 DOI: 10.3389/fncom.2022.1004301] [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: 07/27/2022] [Accepted: 11/23/2022] [Indexed: 12/23/2022] Open
Abstract
Background Effectively decoding electroencephalogram (EEG) pattern for specific mental tasks is a crucial topic in the development of brain-computer interface (BCI). Extracting common spatial pattern (CSP) features from motor imagery EEG signals is often highly dependent on the selection of frequency band and time interval. Therefore, optimizing frequency band and time interval would contribute to effective feature extraction and accurate EEG decoding. Objective This study proposes an approach based on an improved novel global harmony search (INGHS) to optimize frequency-time parameters for effective CSP feature extraction. Methods The INGHS algorithm is applied to find the optimal frequency band and temporal interval. The linear discriminant analysis and support vector machine are used for EEG pattern decoding. Extensive experimental studies are conducted on three EEG datasets to assess the effectiveness of our proposed method. Results The average test accuracy obtained by the time-frequency parameters selected by the proposed INGHS method is slightly better than artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms. Furthermore, the INGHS algorithm is superior to PSO and ABC in running time. Conclusion These superior experimental results demonstrate that the optimal frequency band and time interval selected by the INGHS algorithm could significantly improve the decoding accuracy compared with the traditional CSP method. This method has a potential to improve the performance of MI-based BCI systems.
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Affiliation(s)
- Bin Shi
- Xi’an Research Institute of High-Technology, Xi’an, Shaanxi, China
| | - Xiaokai Chen
- Rehabilitation Medical Center, Huizhou Third People’s Hospital, Huizhou, China
| | - Zan Yue
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
- iHarbour Academy of Frontier Equipment (iAFE), Xi’an, China
| | - Feixiang Zeng
- Rehabilitation Medical Center, Huizhou Third People’s Hospital, Huizhou, China
| | - Shuai Yin
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
- iHarbour Academy of Frontier Equipment (iAFE), Xi’an, China
| | - Benguo Wang
- Department of Rehabilitation Medicine, Longgang District People’s Hospital of Shenzhen, Shenzhen, China
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of The Chinese University of Hong Kong, Shenzhen, China
| | - Jing Wang
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
- iHarbour Academy of Frontier Equipment (iAFE), Xi’an, China
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Zarei A, Mohammadzadeh Asl B. Automatic detection of code-modulated visual evoked potentials using novel covariance estimators and short-time EEG signals. Comput Biol Med 2022; 147:105771. [DOI: 10.1016/j.compbiomed.2022.105771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/05/2022] [Accepted: 06/19/2022] [Indexed: 11/03/2022]
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Shishkin SL. Active Brain-Computer Interfacing for Healthy Users. Front Neurosci 2022; 16:859887. [PMID: 35546879 PMCID: PMC9083451 DOI: 10.3389/fnins.2022.859887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/30/2022] [Indexed: 11/13/2022] Open
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A 120-Target Brain-Computer Interface Based on Code-Modulated Visual Evoked Potentials. J Neurosci Methods 2022; 375:109597. [DOI: 10.1016/j.jneumeth.2022.109597] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/17/2022] [Accepted: 04/08/2022] [Indexed: 11/20/2022]
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