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An auditory selective attention brain-computer interface system based on auditory steady-state response

APPLIED ACOUSTICS 2025年 228卷

作者： Wang, Yao Liu, Xin Cui, Hongyan Li, Zhaohui Chen, Xiaogang Tiangong Univ Sch Life Sci Dept Biomed Engn Tianjin 300387 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Inst Biomed Engn Tianjin 300192 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Inst Biomed Engn Tianjin 300192 Peoples R China

Auditory steady-state response (ASSR) is a brain steady-state response induced by periodic sound signals, which can be used to build an auditory brain-computer interface (BCI), thereby providing a pathway for visually impaired patients to communicate with the outside world. Most of existing ASSR-based BCI studies use linear discriminant classifier (LDA) and spatial coherence to detect ASSRs. Therefore, there is an urgent need for efficient electroencephalogram (EEG) decoding methods to improve the performance of ASSR-based BCI systems. In this study, we elicited ASSRs using sinusoidal amplitude modulated (SAM) tones that simultaneously delivered different modulation frequencies (i.e., 37 Hz for the left channel and 43 Hz for the right channel). Subjects were asked to focus their attention on the auditory stimulation on one side according to the auditory cue. Filter bank common spatial pattern (FBCSP) algorithm was innovatively introduced to detect the ASSRs. Offline results showed that the brain region with strong ASSRs was the central forehead area, and when subjects paid attention to the auditory stimulation at 37 Hz or 43 Hz, the ASSR response of 37 Hz or 43 Hz on the corresponding side would be enhanced compared to the no attention condition. Online results obtained from twelve healthy subjects showed that the mean recognition accuracy of the proposed ASSR-based BCI system achieved a mean accuracy of 82.22 +/- 3.11 %. Moreover, the present study further verified that weak auditory stimuli with low stimulus intensity (i.e., 40 dB SPL) could also be used to build ASSR-based BCIs, and achieved an online mean accuracy of 78.89 +/- 2.54 %. These results verified that the FBCSP algorithm could be used for detecting ASSRs and the feasibility of the proposed ASSR-based BCI system, providing a great idea for building a high-speed ASSR-based BCI system.

关键词： Auditory steady-state response brain-computer interface Electroencephalogram Sinusoidal amplitude modulation

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brain-computer interface: Applications to Speech Decoding and Synthesis to Augment Communication

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NEUROTHERAPEUTICS 2022年第1期19卷 263-273页

作者： Luo, Shiyu Rabbani, Qinwan Crone, Nathan E. Johns Hopkins Univ Sch Med Dept Biomed Engn Baltimore MD 21205 USA Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA Johns Hopkins Univ Sch Med Dept Neurol Baltimore MD 21205 USA

Damage or degeneration of motor pathways necessary for speech and other movements, as in brainstem strokes or amyotrophic lateral sclerosis (ALS), can interfere with efficient communication without affecting brain structures responsible for language or cognition. In the worst-case scenario, this can result in the locked in syndrome (LIS), a condition in which individuals cannot initiate communication and can only express themselves by answering yes/no questions with eye blinks or other rudimentary movements. Existing augmentative and alternative communication (AAC) devices that rely on eye tracking can improve the quality of life for people with this condition, but brain-computer interfaces (BCIs) are also increasingly being investigated as AAC devices, particularly when eye tracking is too slow or unreliable. Moreover, with recent and ongoing advances in machine learning and neural recording technologies, BCIs may offer the only means to go beyond cursor control and text generation on a computer, to allow real-time synthesis of speech, which would arguably offer the most efficient and expressive channel for communication. The potential for BCI speech synthesis has only recently been realized because of seminal studies of the neuroanatomical and neurophysiological underpinnings of speech production using intracranial electrocorticographic (ECoG) recordings in patients undergoing epilepsy surgery. These studies have shown that cortical areas responsible for vocalization and articulation are distributed over a large area of ventral sensorimotor cortex, and that it is possible to decode speech and reconstruct its acoustics from ECoG if these areas are recorded with sufficiently dense and comprehensive electrode arrays. In this article, we review these advances, including the latest neural decoding strategies that range from deep learning models to the direct concatenation of speech units. We also discuss state-of-the-art vocoders that are integral in constructing natur

关键词： Speech synthesis brain-computer interface Locked-in syndrome Electrocorticography ECoG

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brain-computer interface Training Based on brain Activity Can Induce Motor Recovery in Patients With Stroke: A Meta-Analysis

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NEUROREHABILITATION AND NEURAL REPAIR 2022年第2期36卷 83-96页

作者： Nojima, Ippei Sugata, Hisato Takeuchi, Hiroki Mima, Tatsuya Shinshu Univ Dept Phys Therapy Sch Hlth Sci 3-1-1 Asahi Matsumoto Nagano 3908621 Japan Oita Univ Fac Welf & Hlth Sci Oita Japan Natl Hosp Org Higashinagoya Natl Hosp Nagoya Aichi Japan Ritsumeikan Univ Grad Sch Core Eth & Frontier Sci Kyoto Japan

Background brain-computer interface (BCI) is a procedure involving brain activity in which neural status is provided to the participants for self-regulation. The current review aims to evaluate the effect sizes of clinical studies investigating the use of BCI-based rehabilitation interventions in restoring upper extremity function and effective methods to detect brain activity for motor recovery. Methods A computerized search of MEDLINE, CENTRAL, Web of Science, and PEDro was performed to identify relevant articles. We selected clinical trials that used BCI-based training for post-stroke patients and provided motor assessment scores before and after the intervention. The pooled standardized mean differences of BCI-based training were calculated using the random-effects model. Results We initially identified 655 potentially relevant articles;finally, 16 articles fulfilled the inclusion criteria, involving 382 participants. A significant effect of neurofeedback intervention for the paretic upper limb was observed (standardized mean difference = .48, [.16-.80], P = .006). However, the effect estimates were moderately heterogeneous among the studies (I-2 = 45%, P = .03). Subgroup analysis of the method of measurement of brain activity indicated the effectiveness of the algorithm focusing on sensorimotor rhythm. Conclusion This meta-analysis suggested that BCI-based training was superior to conventional interventions for motor recovery of the upper limbs in patients with stroke. However, the results are not conclusive because of a high risk of bias and a large degree of heterogeneity due to the differences in the BCI interventions and the participants;therefore, further studies involving larger cohorts are required to confirm these results.

关键词： brain-computer interface stroke brain activity motor function

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Automatic Feature Selection for Sensorimotor Rhythms brain-computer interface Fusing Expert and Data-Driven Knowledge

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 3422-3431页

作者： Sultana, Mushfika Perdikis, Serafeim Univ Essex Sch Comp Sci & Elect Engn Brain Comp Interface & Neural Engn Lab Colchester CO4 3SQ England

Early brain-computer interface (BCI) systems were mainly based on prior neurophysiological knowledge coupled with feedback training, while state-of-the-art interfaces rely on data-driven, machine learning (ML)-oriented methods. Despite the advances in BCI that ML can be credited with, the performance of BCI solutions is still not up to the mark, posing a major barrier to the widespread use of this technology. This paper proposes a novel, automatic feature selection method for BCI able to leverage both data-dependent and expert knowledge to suppress noisy features and highlight the most relevant ones thanks to a fuzzy logic (FL) system. Our approach exploits the capability of FL to increase the reliability of decision-making by fusing heterogeneous information channels while maintaining transparency and simplicity. We show that our method leads to significant improvement in classification accuracy, feature stability and class bias when applied to large motor imagery or attempt datasets including end-users with motor disabilities. We postulate that combining data-driven methods with knowledge derived from neuroscience literature through FL can enhance the performance, explainability, and learnability of BCIs.

关键词： Feature extraction Electroencephalography Fuzzy logic Motors Noise brain-computer interfaces Training brain-computer interface expert knowledge feature selection fuzzy logic motor imagery

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A hybrid capsule attention-based convolutional bi-GRU method for multi-class mental task classification based brain-computer interface

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computer METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2025年第1期28卷 90-106页

作者： Deepika, D. Rekha, G. Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Hyderabad 500075 Telangana India Mahatma Gandhi Inst Technol Dept Comp Sci & Engn Hyderabad 500075 Telangana India

Electroencephalography analysis is critical for brain computer interface research. The primary goal of brain-computer interface is to establish communication between impaired people and others via brain signals. The classification of multi-level mental activities using the brain-computer interface has recently become more difficult, which affects the accuracy of the classification. However, several deep learning-based techniques have attempted to identify mental tasks using multidimensional data. The hybrid capsule attention-based convolutional bidirectional gated recurrent unit model was introduced in this study as a hybrid deep learning technique for multi-class mental task categorization. Initially, the obtained electroencephalography data is pre-processed with a digital low-pass Butterworth filter and a discrete wavelet transform to remove disturbances. The spectrally adaptive common spatial pattern is used to extract characteristics from pre-processed electroencephalography data. The retrieved features were then loaded into the suggested classification model, which was used to extract the features deeply and classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using a dung beetle optimization approach. Finally, the proposed classifier is assessed for several types of mental task classification using the provided dataset. The simulation results are compared with the existing state-of-the-art techniques in terms of accuracy, precision, recall, etc. The accuracy obtained using the proposed approach is 97.87%, which is higher than that of the other existing methods.

关键词： Electroencephalography brain-computer interface capsule network attention mechanism convolutional neural network Bi-GRU Discrete wavelet transform

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Hands-Free Crowdsensing of Accessibility Barriers in Sidewalk Infrastructure: A brain-computer interface Approach

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JOURNAL OF INFRASTRUCTURE SYSTEMS 2025年第2期31卷

作者： Zhou, Xiaoshan Menassa, Carol C. Kamat, Vineet R. Univ Michigan Dept Civil & Environm Engn Ann Arbor MI 48109 USA

Crowdsourced human sensing provides a critical complement to urban physical sensing networks to detect city infrastructure disrepairs and inform strategized maintenance. However, motivating public participation remains challenging due to the additional efforts required to engage with third-party platforms. This study builds upon the "citizen as sensor" notion but emphasizes an intuitive and effortless approach for people to contribute their observations and experiences regarding accessibility barriers in the physical world to cyber data systems. We develop a wearable electroencephalogram (EEG)-based brain-computer interface application to predict the presence or absence of environmental barriers and individuals' reaction and categorization to various accessibility barrier conditions. To provide proof-of-concept, we first characterized the mental model for identifying environmental barriers based on Bayesian inference. A structured experiment was conducted, collecting empirical EEG data from 12 participants as they performed a sidewalk accessibility evaluation task. Based on correlations between extracted EEG power features and cognitive discriminability characterized by signal detection theory, we validated that EEG measures can reflect internal representations when individuals perceived a scenario as "inaccessible." We then derived the decision threshold above which an accessibility barrier is reported from elicited EEG power patterns and based, on this threshold, estimated participants' task performance using maximum a posteriori estimates. The accuracy of these estimates was confirmed by comparing them against actual performance data. Machine learning classifiers were then leveraged to enhance automatic detection and categorization of environmental features in terms of accessibility from individuals' brain signals. Theoretically, theta activity in the temporal cortical region was found to dominate a memory retrieval-based cognitive process for identifying accessi

关键词： Infrastructure maintenance Citizen as sensors Signal detection theory Electroencephalogram (EEG) brain-computer interface Sidewalk accessibility

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Learning-based EEG rhythm analysis for the enhancement of motor imagery-based brain-computer interface performance

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 102卷

作者： Wu, Xiaopei Ouyang, Rui Zhang, Chao Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

One of the major challenges in achieving stability and adaptability in motor imagery brain-computer interfaces (MI-BCI) is the individual variability in motion-related neural activity. This paper presents a novel approach to motor imagery electroencephalogram (MI-EEG) data analysis and MI-BCI optimization, making the following contributions: (1) We introduce EhythmNet, a compact one-dimensional convolutional neural network (CNN) designed to capture the inherent frequency features in MI-EEG data. (2) We propose using kernel coefficients derived from EhythmNet to construct a temporal bandpass filter within the conventional MI-BCI framework, where independent component analysis (ICA) is employed as the spatial filter. Experimental results consistently show that replacing the conventional bandpass filter (CBF) with a kernel-based bandpass filter (KBF) significantly improves stability and MI classification performance (KBF-ICA: 82.3 +/- 2.7 %, CBF-ICA: 72.4 +/- 7.5 %, EEGnet: 74.5 +/- 6.3). (3) To optimize MI-BCI, we advocate for integrating KBF with the ICA spatial filter. Our experimental findings clearly demonstrate that the KBF-ICA approach outperforms traditional methods, such as common spatial pattern (CSP) and EEGnet, particularly in cross-subject testing scenarios (KBF-ICA: 70.8 %, KBFCSP: 50.6 %, EEGnet: 45.1 %). Furthermore, with optimized EEG channel selection, the recognition rate of KBFICA can reach 91.8 %, whereas the other two methods, CSP and EEGnet, do not exhibit significant performance improvement through channel optimization. In conclusion, we highlight the potential of combining traditional MI-BCI decoding models with deep learning techniques to address current performance limitations in MI-BCIs. This integration holds significant theoretical and practical implications, providing a promising direction for future advancements in MI-BCI technology.

关键词： EEG rhythm brain-computer interface Motor imagery Independent component analysis Transferability

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The role of eye movement signals in non-invasive brain-computer interface typing system

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2024年第7期62卷 1981-1990页

作者： Liu, Xi Hu, Bingliang Si, Yang Wang, Quan Chinese Acad Sci Xian Inst Opt & Precis Mech Key Lab Spectral Imaging Technol Xian 710119 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Key Lab Biomed Spect Xian Xian 710119 Peoples R China Sichuan Acad Med Sci & Sichuan Prov Peoples Hosp Dept Neurol Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Chengdu 611731 Peoples R China

brain-computer interfaces (BCIs) have shown great potential in providing communication and control for individuals with severe motor disabilities. However, traditional BCIs that rely on electroencephalography (EEG) signals suffer from low information transfer rates and high variability across users. Recently, eye movement signals have emerged as a promising alternative due to their high accuracy and robustness. Eye movement signals are the electrical or mechanical signals generated by the movements and behaviors of the eyes, serving to denote the diverse forms of eye movements, such as fixations, smooth pursuit, and other oculomotor activities like blinking. This article presents a review of recent studies on the development of BCI typing systems that incorporate eye movement signals. We first discuss the basic principles of BCI and the recent advancements in text entry. Then, we provide a comprehensive summary of the latest advancements in BCI typing systems that leverage eye movement signals. This includes an in-depth analysis of hybrid BCIs that are built upon the integration of electrooculography (EOG) and eye tracking technology, aiming to enhance the performance and functionality of the system. Moreover, we highlight the advantages and limitations of different approaches, as well as potential future directions. Overall, eye movement signals hold great potential for enhancing the usability and accessibility of BCI typing systems, and further research in this area could lead to more effective communication and control for individuals with motor *** AbstractThis article delves into three pivotal components of the BCI typing system: data, algorithms, and interaction. The system leverages eye movement and EEG data as inputs, which are processed through algorithms for data fusion, feature extraction, and classification to yield output results. Furthermore, it facilitates real-time interaction by providing visual feedback via an efficient user inte

关键词： brain-computer interface Speller EEG Eye movement Text input

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Evaluation of consumer-grade wireless EEG systems for brain-computer interface applications

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BIOMEDICAL ENGINEERING LETTERS 2024年第6期14卷 1433-1443页

作者： Lee, Seungchan Kim, Misung Ahn, Minkyu Korea Inst Machinery & Mat KIMM Dept Med Device Daegu 42994 South Korea Handong Global Univ Sch Comp Sci & Elect Engn Pohang 37554 South Korea

With the growing popularity of consumer-grade electroencephalogram (EEG) devices for health, entertainment, and cognitive research, assessing their signal quality is essential. In this study, we evaluated four consumer-grade wireless and dry-electrode EEG systems widely used for brain-computer interface (BCI) research and applications, comparing them with a research-grade system. We designed an EEG phantom method that reproduced mu V-level amplitude EEG signals and evaluated the five devices based on their spectral responses, temporal patterns of event-related potential (ERP), and spectral patterns of resting-state EEG. We discovered that the consumer-grade devices had limited bandwidth compared with the research-grade device. A late component (e.g., P300) was detectable in the consumer-grade devices, but the overall ERP temporal pattern was distorted. Only one device showed an ERP temporal pattern comparable to that of the research-grade device. On the other hand, we confirmed that the activation of the alpha rhythm was observable in all devices. The results provide valuable insights for researchers and developers when it comes to selecting suitable EEG devices for BCI research and applications.

关键词： brain-computer interface Electroencephalogram Consumer-grade EEG device Signal quality

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A Human-Centric Metaverse Enabled by brain-computer interface: A Survey

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2024年第3期26卷 2120-2145页

作者： Zhu, Howe Yuan Hieu, Nguyen Quang Hoang, Dinh Thai Nguyen, Diep N. Lin, Chin-Teng Univ Technol Sydney Sch Comp Sci Sydney NSW 2007 Australia Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia Univ Technol Sydney Australian AI Inst Fac Engn & IT Human Centr AI CtrCIBCI Lab Ultimo NSW 2007 Australia

The growing interest in the Metaverse has generated momentum for members of academia and industry to innovate toward realizing the Metaverse world. The Metaverse is a unique, continuous, and shared virtual world where humans embody a digital form within an online platform. Through a digital avatar, Metaverse users should have a perceptual presence within the environment and can interact and control the virtual world around them. Thus, a human-centric design is a crucial element of the Metaverse. The human users are not only the central entity but also the source of multi-sensory data that can be used to enrich the Metaverse ecosystem. In this survey, we study the potential applications of brain-computer interface (BCI) technologies that can enhance the experience of Metaverse users. By directly communicating with the human brain, the most complex organ in the human body, BCI technologies hold the potential for the most intuitive human-machine system operating at the speed of thought. BCI technologies can enable various innovative applications for the Metaverse through this neural pathway, such as user cognitive state monitoring, digital avatar control, virtual interactions, and imagined speech communications. This survey first outlines the fundamental background of the Metaverse and BCI technologies. We then discuss the current challenges of the Metaverse that can potentially be addressed by BCI, such as motion sickness when users experience virtual environments or the negative emotional states of users in immersive virtual applications. After that, we propose and discuss a new research direction called Human Digital Twin, in which digital twins can create an intelligent and interactable avatar from the user's brain signals. We also present the challenges and potential solutions in synchronizing and communicating between virtual and physical entities in the Metaverse. Finally, we highlight the challenges, open issues, and future research directions for BCI-enabled M

关键词： Metaverse Sensors Surveys Robot sensing systems Avatars Temperature sensors Tutorials brain-computer interface human digital twin non-invasive BCI computer vision AI IoT sensors VR machine learning

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