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SSVEP Enhancement in Mixed Reality Environment for brain-computer interfaces

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2025年 33卷 420-430页

作者： Wu, Jieyu He, Feng Xiao, Xiaolin Gao, Runyuan Meng, Lin Liu, Xiuyun Xu, Minpeng Ming, Dong Tianjin Univ Acad Med Engn & Translat Med Tianjin 300072 Peoples R China Haihe Lab Brain Comp Interact & Human Machine Inte Tianjin 300392 Peoples R China Tianjin Univ Coll Precis Instruments & Optoelect Engn Dept Biomed Engn Tianjin Peoples R China

Expanding the application possibilities of brain-computer interfaces (BCIs) is possible through their implementation in mixed reality (MR) environments. However, visual stimuli are displayed against a realistic scene in the MR environment, which degrades BCI performance. The purpose of this study was to optimize stimulus colors in order to improve the MR-BCI system's performance. In the MR environment, a 10-command SSVEP-BCI was deployed. Various stimulus colors and background colors for the BCI system were tested and optimized in offline and online experiments. Color contrast ratios (CCRs) between stimulus and background colors were introduced to assess the performance difference among all conditions. Additionally, we proposed a cross-correlation task-related component analysis based on simulated annealing (SA-xTRCA), which can increase the signal-to-noise ratio (SNR) and detection accuracy by aligning SSVEP trials. The results of an offline experiment showed that the background and stimulus colors had a significant interaction effect that can impact system performance. A possible nonlinear relationship between CCR value and SSVEP detection accuracy exists. Online experiment results demonstrated that the system performed best with polychromatic stimulus on the colored background. The proposed SA-xTRCA significantly outperformed the other four traditional algorithms. The online average information transfer rate (ITR) achieved 57.58 +/- 5.31 bits/min. This study proved that system performance can be effectively enhanced by optimizing stimulus color based on background color. In MR environments, CCR can be used as a quantitative criterion for choosing stimulus colors in BCI system design.

关键词： Image color analysis Visualization Electroencephalography Color Virtual reality System performance Liquid crystal displays brain-computer interfaces Mixed reality Light emitting diodes brain-computer interface (BCI) mixed reality (MR) stimulus color latency calibration

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Comparisons of stimulus paradigms for SSVEP-based brain-computer interfaces

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JOURNAL OF NEURAL ENGINEERING 2025年第2期22卷

作者： Zhao, Deyu Dong, Guoya Pei, Weihua Gao, Xiaorong Wang, Yijun Hebei Univ Technol Sch Hlth Sci & Biomed Engn State Key Lab Reliabil & Intelligence Elect Equipm Hebei Key Lab Bioelectromagnet & Neural Engn Tianjin 300130 Peoples R China Chinese Acad Sci Inst Semicond Lab Solid State Optoelect Informat Technol Beijing 100083 Peoples R China Univ Chinese Acad Sci Sch Future Technol Beijing Peoples R China Tsinghua Univ Dept Biomed Engn Beijing Peoples R China Chinese Inst Brain Res Beijing Peoples R China

Objective. With the recent development of visual evoked potential (VEP) based brain-computer interfaces (BCIs), the stimulus paradigm has been continuously innovated, in which the pursuit of higher BCI performance and better user experience has become indispensable. Approach. To optimize the stimulus paradigm, a 12-target online BCI system was designed in this study by adopting flicker for steady-state VEPs, Newton's ring for steady-state motion VEP, and frame rate based video stimulus, respectively. The signal characteristics of VEP, classification accuracy, and user experience of the three stimulus paradigms were quantitatively evaluated and compared. Main results. The online information transfer rates for the three stimulus paradigms were 53.77 bits min-1, 51.41 +/- 3.55 bits min-1, and 52.07 +/- 3.09 bits min-1, respectively. The video stimulus had a significantly better user experience, while the flicker stimulus showed the worst. Significance. These results demonstrate the advantage of the proposed video stimulus paradigm and have significant theoretical and applied implications for developing VEP-based BCI systems.

关键词： brain-computer interfaces electroencephalogram steady-state visual evoked potentials steady-state motion visual evoked potential video stimulus task-discriminant component analysis

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Amplitude Modulation Depth Coding Method for SSVEP-Based brain-computer interfaces

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2025年 33卷 391-403页

作者： Li, Ruxue Wang, Zhenyu Zhao, Xi Xu, Guiying Hu, Honglin Zhou, Ting Xu, Tianheng Chinese Acad Sci Shanghai Adv Res Inst Shanghai 200050 Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 200050 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Shanghai Univ Sch Microelect Shanghai 201210 Peoples R China

In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the limited availability of frequency resources inherently constrains the scale of the instruction set, presenting a substantial challenge for efficient communication. As the number of stimuli increases, the comfort level of the stimulus interface also becomes increasingly demanding due to the expanded flickering area. To address these issues, we proposed a novel amplitude modulation depth coding (AMDC) method that employs Amplitude Shift Keying (ASK) technique to modulate the luminance level of stimuli dynamically. Each stimulus with a single carrier frequency was assigned a specific binary sequence to operate two modulation depths. Two experiments were conducted to comprehensively assess the effectiveness of this approach. In Experiment 1, the time-frequency responses at two modulation depths across different frequencies were examined. A 36-target paradigm based on AMDC strategy was designed and evaluated in terms of user experience and classification performance in Experiment 2. The results show that the proposed paradigm obtains an average classification accuracy of 81.7 +/- 12.6 % with an average information transfer rate (ITR) of 45.4 +/- 11.5 bits/min. Moreover, it significantly reduces flicker perception and improves comfort level compared to traditional SSVEP stimuli with uniform modulation depth. Given its capability to improve coding efficiency for a single frequency and improve user experience, this method shows promising potential for application in large-scale command SSVEP-based BCI systems.

关键词： brain-computer interfaces electroencephalography steady-state visual evoked potentials amplitude modulation depth coding

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Prospectively identifying risks and controls for advanced brain-computer interfaces: A Networked Hazard Analysis and Risk Management System (Net-HARMS) approach

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APPLIED ERGONOMICS 2025年 122卷 104382页

作者： King, Brandon J. Read, Gemma J. M. Salmon, Paul M. Univ Sunshine Coast Ctr Human Factors & Sociotech Syst ML 47Locked Bag 4 Maroochydore Qld 4558 Australia Univ Sunshine Coast Sch Hlth Maroochydore Qld Australia

The introduction of advanced digital technologies continues to increase system complexity and introduce risks, which must be proactively identified and managed to support system resilience. brain-computer interfaces (BCIs) are one such technology;however, the risks arising from broad societal use of the technology have yet to be identified and controlled. This study applied a structured systems thinking-based risk assessment method to prospectively identify risks and risk controls for a hypothetical future BCI system lifecycle. The application of the Networked Hazard Analysis and Risk Management System (Net-HARMS) method identified over 800 risks throughout the BCI system lifecycle, from BCI development and regulation through to BCI use, maintenance, and decommissioning. High-criticality risk themes include the implantation and degradation of unsafe BCIs, unsolicited brain stimulation, incorrect signals being sent to safety-critical technologies, and insufficiently supported BCI users. Over 600 risk controls were identified that could be implemented to support system safety and performance resilience. Overall, many highly-impactful BCI system safety and performance risks may arise throughout the BCI system lifecycle and will require collaborative efforts from a wide range of BCI stakeholders to adequately control. Whilst some of the identified controls are practical, work is required to develop a more systematic set of controls to best support the design of a resilient sociotechnical BCI system.

关键词： brain-computer interfaces risk assessment System modelling Resilience engineering Net-HARMS

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An Efficient brain-Switch for Asynchronous brain-computer interfaces

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2025年第1期19卷 130-141页

作者： Valencia, Daniel Mercier, Patrick P. Alimohammad, Amir San Diego State Univ Dept Elect & Comp Engn San Diego CA 92182 USA Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

Intracortical brain computer interfaces (iBCIs) utilizing extracellular recordings mainly employ in vivo signal processing application-specific integrated circuits (ASICs) to detect action potentials (spikes). Conventionally, "brain-switches" based on spiking activity have been employed to realize asynchronous (self-paced) iBCIs, estimating when the user involves in the underlying BCI task. Several studies have demonstrated that local field potentials (LFPs) can effectively replace action potentials, drastically reducing the power consumption and processing requirements of in vivo ASICs. This article presents the first LFP-based brain-switch design and implementation using gated recurrent neural networks (RNNs). Compared to the previously reported brain-switches, our design requires no exhaustive learning phase for the estimation of optimal recording channels or frequency band selection, making it more applicable to practical asynchronous iBCIs. The synthesized ASIC of the designed in vivo LFP-based feature extraction unit, in a standard 180-nm CMOS process, occupies only 0.09 mm2 of silicon area, and the post place-and-route synthesis results indicate that it consumes 91.87 nW of power while operating at 2 kHz. Compared to the previously published ASICs, the proposed LFP-based brain-switch consumes the least power for in vivo digital signal processing and achieves comparable state estimation performance to that of spike-based brain-switches.

关键词： Neurons Electrodes Decoding Silicon Recording Extracellular Feature extraction Application-specific integrated circuits brain-computer interfaces local field potentials neural signal processing

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Advances in clinical brain-computer interfaces for assistive substitution and rehabilitation: A rapid scoping review

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SURGICAL PRACTICE 2025年第1期29卷 35-49页

作者： Ho, Robert Tsi-Lok Xue, William See, Michael Ka-Wing Chan, David Yuen-Chung Tsang, Anderson Chun-On Mak, Calvin Hoi-Kwan Wong, Sui-To Lee, Michael Wing-Yan Univ Hong Kong Li Ka Shing Fac Med Hong Kong Peoples R China Queen Elizabeth Hosp Dept Neurosurg Hong Kong Peoples R China Hong Kong Neurosurg Soc Hong Kong Peoples R China Prince Wales Hosp Dept Neurosurg Hong Kong Peoples R China SOUTHAMPTON GEN HOSP SOUTHAMPTON England Univ Southampton Southampton England Tuen Mun Hosp Dept Neurosurg Hong Kong Peoples R China Pamela Youde Nethersole Eastern Hosp Dept Neurosurg Hong Kong Peoples R China

Objective: This scoping review explores the rapidly evolving field of brain-computer interface (BCI) technologies, with a particular emphasis on the fundamental concepts, advances made, and prospective applications. Following the 2022 Annual Scientific Meeting of the Hong Kong Neurosurgical Society themed 'Neuromodulation and brain-computer Interface', this article represents the first comprehensive review of BCI technologies from a Hong Kong perspective, providing a balanced viewpoint that reflects both academic and practical clinical insights for surgeons considering the implementation of these emerging technologies. Methodology: A rapid scoping review was conducted to clarify key concepts and trends in current BCI technologies, including signal acquisition methods, effectors, applications, and ethical issues. Key developments, particularly those relevant to Hong Kong, were identified and analysed. Results: We summarise various modalities for the acquisition of central nervous system signals, introducing the techniques and steps involved in the data processing pipeline. We highlight two major arms of BCI applications and their promises in advancing patient care: assistive communication or substitution and closed-loop rehabilitation or neuromodulation. The exciting frontier also invites a host of ethical questions which must be thoroughly discussed. Conclusions: The growing body of knowledge in BCIs offers new treatment options for patients requiring assistive substitution and rehabilitation. The review hopes to provide a rigorous foundation for future research, invite subsequent discussions and translational studies, and support the incorporation of BCI technologies into local healthcare.

关键词： artificial intelligence brain-computer interfaces neuromodulation neuroprosthetics rehabilitation

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Using transient, effector-specific neural responses to gate decoding for brain-computer interfaces

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JOURNAL OF NEURAL ENGINEERING 2025年第1期22卷 2025 Jan 14页

作者： Dekleva, Brian M. Collinger, Jennifer L. Univ Pittsburgh Rehab Neural Engn Labs Pittsburgh PA 15260 USA Univ Pittsburgh Phys Med & Rehabil Pittsburgh PA 15260 USA Univ Pittsburgh Bioengn Pittsburgh PA USA Ctr Neural Basis Cognit Pittsburgh PA 15213 USA Carnegie Mellon Univ Biomed Engn Dept Pittsburgh PA USA

Objective. Real-world implementation of brain-computer interfaces (BCIs) for continuous control of devices should ideally rely on fully asynchronous decoding approaches. That is, the decoding algorithm should continuously update its output by estimating the user's intended actions from real-time neural activity, without the need for any temporal alignment to an external cue. This kind of open-ended temporal flexibility is necessary to achieve naturalistic and intuitive control. However, the relation between cortical activity and behavior is not stationary: neural responses that appear related to a certain aspect of behavior (e.g. grasp force) in one context will exhibit a relationship to something else in another context (e.g. reach speed). This presents a challenge for generalizable decoding, since the applicability of a decoder for a given parameter changes over time. Approach. We developed a method to simplify the problem of continuous decoding that uses transient, end effector-specific neural responses to identify periods of relevant effector engagement. Specifically, we use transient responses in the population response observed at the onset and offset of all hand-related actions to signal the applicability of hand-related feature decoders (e.g. digit movement or force). By using this transient-based gating approach, specific feature decoding models can be simpler (owing to local linearities) and are less sensitive to interference from cross-effector interference such as combined reaching and grasping actions. Main results. The transient-based decoding approach enabled high-quality online decoding of grasp force and individual finger control in multiple behavioral paradigms. The benefits of the gated approach are most evident in tasks that require both hand and arm control, for which standard continuous decoding approaches exhibit high output variability. Significance. The approach proposed here addresses the challenge of decoder generalization across contexts.

关键词： brain-computer interfaces decoding transients grasp

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Explainable multi-frequency and multi-region fusion model for affective brain-computer interfaces

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INFORMATION FUSION 2025年 118卷

作者： Wang, Tao Mao, Rui Liu, Shuang Cambria, Erik Ming, Dong Tianjin Univ Acad Med Engn & Translat Med Tianjin 300072 Peoples R China State Key Lab Adv Med Mat & Devices Tianjin 300072 Peoples R China Haihe Lab Brain Comp Interact & Human Machine Inte Tianjin 300384 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore

An affective brain-computer interface (aBCI) has demonstrated great potential in the field of emotion recognition. However, existing aBCI models encounter significant challenges in explainability and the effective fusion of multi-frequency and multi-region features, which greatly limits their practical applicability. To address these issues, this paper proposes an explainable multi-frequency and multi-region fusion network (MFMR-FN), consisting of multi-frequency encoding and multi-region decoding networks. Specifically, in the encoding network, we leverage spectral graph theory and symmetric positive definite (SPD) matrix learning to adaptively encode EEG data into functional connectivity (FC) matrices with neurobiological information. Furthermore, a multi-frequency fusion algorithm, based on Riemannian geometry is designed to guide the network in achieving cross-frequency feature fusion. In the decoding network, we introduce a multi-region selection mechanism and a multi-scale Riemannian network, fusing brain network features from the whole brain, hemispheres, and local regions for coarse-to-fine emotion decoding. We conducted extensive experiments on the emotion recognition dataset (SEED) and the depression detection dataset (MODMA). The results show that MFMR-FN outperforms existing methods in multiple metrics and provides explainable FC features that reveal brain network patterns under different emotions and abnormal connectivity in depression. The proposed MFMR-FN is expected to improve the practicality and reliability of aBCIs in real-world and clinical applications.

关键词： Explainable AI Emotion recognition brain-computer interfaces Functional connectivity EEG

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When brain-computer interfaces meet the metaverse: Landscape, demonstrator, trends, challenges, and concerns

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NEUROCOMPUTING 2025年 625卷

作者： Bernal, Sergio Lopez Perez, Mario Quiles Beltran, Enrique Tomas Martinez Perez, Gregorio Martinez Celdran, Alberto Huertas Univ Murcia Dept Informat & Commun Engn Murcia 30100 Spain Univ Zurich UZH Dept Informat IfI Commun Syst Grp CSG CH-8050 Zurich Switzerland

The metaverse has gained tremendous popularity in recent years, allowing the interconnection of users worldwide. However, current systems in metaverse scenarios, such as virtual reality glasses, offer a partial immersive experience. In this context, brain-computer interfaces (BCIs) can introduce a revolution in the metaverse, although a study of the applicability and implications of BCIs in these virtual scenarios is required. Based on a limited number of publications, this work reviews the applicability of BCIs in the metaverse, analyzing the current status of this integration based on different categories related to virtual worlds and the evolution of BCIs in these scenarios in the medium and long term. This work also proposes the design and implementation of a general framework that integrates BCIs with different data sources from sensors and actuators (e.g., VR glasses) based on a modular design to be easily extended. This manuscript also validates the framework in a demonstrator consisting of driving a car within a metaverse, using a BCI for neural data acquisition, a VR headset to provide realism, and a steering wheel and pedals. Four use cases (UCs) are selected, focusing on cognitive and emotional assessment of the driver, detection of drowsiness, and driver authentication while using the vehicle. The results demonstrate the applicability of BCIs to metaverse scenarios using the proposed framework, achieving over 80% F1-score for all UCs, with performance close to 100% for detecting emotions and authenticating users. Moreover, this manuscript offers an analysis of BCI trends in the metaverse, also identifying future challenges that the intersection of these technologies will face. Finally, it reviews the concerns that using BCIs in virtual world applications could generate according to different categories: accessibility, user inclusion, privacy, cybersecurity, physical safety, and ethics.

关键词： Metaverse brain-computer interfaces Trends Challenges Concerns

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Integration of brain-computer interfaces with sacral nerve stimulation: a vision for closed-loop, volitional control of bladder function in neurogenic patients through real-time cortical signal modulation and peripheral neuro-stimulation

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World journal of urology 2025年第1期43卷 301页

作者： Ali Aamir Munaim Siddiqui Department for Internal Medicine Dr. Ruth K.M. Pfau Civil Hospital Karachi Pakistan. aliaamirkhi@***. Department for Internal Medicine DOW University of Health Sciences Karachi Pakistan. aliaamirkhi@***. Department for Internal Medicine Dr. Ruth K.M. Pfau Civil Hospital Karachi Pakistan. Department for Cardiology Dr Ruth KM Pfau Civil Hospital Karachi Pakistan.

Sacral nerve stimulation (SNS) and brain-computer interfaces (BCI) are emerging neuromodulation therapies that offer innovative solutions for chronic neurological disorders. SNS, primarily used in the management of conditions such as urinary incontinence and chronic pelvic pain, demonstrates significant therapeutic potential. In contrast, BCIs are rapidly advancing in their ability to restore lost motor functions and improve the quality of life of patients with severe neurological impairments, such as spinal cord injury and stroke. The integration of SNS and BCI technologies presents a promising avenue for enhancing neuromodulation outcomes by leveraging the potential of both systems. This article explores the combined operation of SNS and BCI, addressing current challenges, future directions, and the potential for these combined therapies to revolutionise the field of functional neuromodulation.

关键词： brain-computer interfaces Neuromodulation Neurostimulation therapy Sacral nerve stimulation

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