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brain-computer interface and Electrochemical Sensor Based on Boron-Nitrogen Co-Doped Graphene-Diamond Microelectrode for EEG and Dopamine Detection

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ACS SENSORS 2025年第2期10卷 868-880页

作者： Chen, Shiming Jiang, Daolian Li, Mingji Xuan, Xiuwei Li, Hongji Tianjin Univ Technol Sch Integrated Circuit Sci & Engn Tianjin Key Lab Film Elect & Commun Devices Tianjin 300384 Peoples R China Tianjin Univ Technol Sch Chem & Chem Engn Tianjin Key Lab Organ Solar Cells & Photochem Conv Tianjin 300384 Peoples R China

The simultaneous detection of electroencephalography (EEG) signals and neurotransmitter levels plays an important role as biomarkers for the assessment and monitoring of emotions and cognition. This paper describes the development of boron and nitrogen codoped graphene-diamond (BNGrD) microelectrodes with a diameter of only 200 mu m for sensing EEG signals and dopamine (DA) levels, which have been developed for the first time. The optimized BNGrD microelectrode responded sensitively to both EEG and DA signals, with a signal-to-noise ratio of 9 dB for spontaneous EEG signals and a limit of detection as low as 124 nM for DA. Furthermore, the BNGrD microelectrodes demonstrate excellent repeatability, reproducibility, and stability for the detection of EEG and dopamine. These results indicate that the BNGrD microelectrode creates suitable conditions for establishing a correlation between the EEG signals and neurotransmitters. A flexible printed circuit board with BNGrD microelectrodes for an eight-channel EEG headband, portable EEG collector, and light stimulation glasses are designed. The self-designed EEG collector adopts a split design strategy of digital and analog signal modules and uses miniaturized impedance-matched BNGrD microelectrodes, which effectively reduce the noise of the electrophysiological signals. The BNGrD microelectrode-based portable EEG/electrochemical analysis system detects EEG signals and DA levels in a noninvasive and minimally invasive manner and has application prospects in remote online diagnosis and treatment of patients with emotional and cognition-related diseases.

关键词： boron and nitrogen codoped graphene-diamond microelectrode brain-computer interface electroencephalography dopamine

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brain-computer interface relieves chronic chemotherapy-induced peripheral neuropathy: A randomized, double-blind, placebo-controlled trial

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CANCER 2024年第2期130卷 300-311页

作者： Prinsloo, Sarah Kaptchuk, Ted J. De Ridder, Dirk Lyle, Randall Bruera, Eduardo Novy, Diane Barcenas, Carlos H. Cohen, Lorenzo G. Univ Texas MD Anderson Canc Ctr Dept Palliat Rehabil & Integrat Med Houston TX USA Univ Texas MD Anderson Canc Ctr Dept Neurosurg Houston TX USA Harvard Med Sch Beth Israel Deaconess Med Ctr Boston MA USA Univ Otago Dept Surg Sci Sect Neurosurg Dunedin New Zealand Mt Mercy Univ Cedar Rapids IA USA Univ Texas MD Anderson Canc Ctr Dept Pain Med Houston TX USA Univ Texas MD Anderson Canc Ctr Breast Med Oncol Houston TX USA Univ Texas MD Anderson Canc Ctr 1515 Holcombe BlvdUnit 1414Fac Ctr Tower 5-6006 Houston TX 77030 USA

BackgroundChemotherapy-induced peripheral neuropathy (CIPN) includes negative sensations that remain a major chronic problem for cancer survivors. Previous research demonstrated that neurofeedback (a closed-loop brain-computer interface [BCI]) was effective at treating CIPN versus a waitlist control (WLC). The authors' a priori hypothesis was that BCI would be superior to placebo feedback (placebo control [PLC]) and to WLC in alleviating CIPN and that changes in brain activity would predict symptom *** to one of three conditions occurred between November 2014 and November 2018. Breast cancer survivors no longer in treatment were assessed at baseline, at the end of 20 treatment sessions, and 1 month later. Auditory and visual rewards were given over 20 sessions based on each patient's ability to modify their own electroencephalographic signals. The Pain Quality Assessment Scale (PQAS) at the end of treatment was the primary outcome, and changes in electroencephalographic signals and 1-month data also were *** BCI and PLC groups reported significant symptom reduction. The BCI group demonstrated larger effect size differences from the WLC group than the PLC group (mean change score: BCI vs. WLC, -2.60 vs. 0.38;95% confidence interval, -3.67, -1.46 [p = .000;effect size, 1.07];PLC, -2.26;95% confidence interval, -3.33, -1.19 [p = .001 vs. WLC;effect size, 0.9]). At 1 month, symptoms continued to improve only for the BCI group. Targeted brain changes at the end of treatment predicted symptoms at 1 month for the BCI group *** is a promising treatment for CIPN and may have a longer lasting effect than placebo (nonspecific BCI), which is an important consideration for long-term symptom relief. Although scientifically interesting, the ability to separate real from placebo treatment may not be as important as understanding the placebo effects differently from effects of the *** Language SummaryChemotherapy

关键词： brain-computer interface electroencephalograph (EEG) neuromodulation neuropathy pain placebo

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brain-computer interface: trend, challenges, and threats

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brain INFORMATICS 2023年第1期10卷 20页

作者： Maiseli, Baraka Abdalla, Abdi T. Massawe, Libe V. Mbise, Mercy Mkocha, Khadija Nassor, Nassor Ally Ismail, Moses Michael, James Kimambo, Samwel Univ Dar es Salaam Coll Informat & Commun Technol Dept Elect & Telecommun Engn Dar Es Salaam 14113 Tanzania Univ Dar es Salaam Coll Informat & Commun Technol Dept Comp Sci & Engn Dar Es Salaam 14113 Tanzania

brain-computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onwards, exceeding those from the United States that started to decline during the same period. Implications and reasons for this trend are discussed. Furthermore, we have extensively discussed challenges and threats limiting exploitation of BCI capabilities. A typical BCI architecture is hypothesized to address two prominent BCI threats, privacy and security, as an attempt to make the technology commercially viable to the society.

关键词： brain-computer interface brain activity Machine learning Neurological disease Signal processing Augmented reality

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brain-computer interface Through the Prism of Modern Age 16th

Brain-Computer Interface Through the Prism of Modern Age

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Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON) and International Conference on Medical and Biological Engineering (CMBEBIH)

作者： Radoncic, Amina Hadzic, Semina Lakovic, Jasmina Int Burch Univ Fac Engn & Nat Sci Dept Genet & Bioengn Sarajevo Bosnia & Herceg

ISBN: (纸本)9783031490675;9783031490682

brain-computer interface is on the rise within different areas of medical diagnostics and rehabilitation. BCI devices are currently in clinical use for neurodegenerative disorder treatments, mental health treatments, and cognitive and behavioral rehabilitation. Gaming and marketing universes have also grabbed their share of novel technologies to take both virtual reality and advertisement strategies to another level. The future of BCI technology will eventually yield to the needs in areas of medicine, gaming, and marketing, and find its way toward our homes. As engineers and scientists work hard to make these devices affordable for all, we can say that BCI could soon fulfill all the needs of modern rehabilitation and influence healthy individuals' lives. Interpretation of BCI devices may vary in certain ways, as the need for a modern approach in rehabilitation. Whether it is virtual reality or cognitive behavior therapy, BCI technology is surely making its way through the golden age of scientific innovations.

关键词： brain-computer interface Neurostimulation Signal acquisition Diagnostics Neurocrime

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A Survey on brain-computer interface-Inspired Communications: Opportunities and Challenges

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2025年第1期27卷 108-139页

作者： Hu, Honglin Wang, Zhenyu Zhao, Xi Li, Ruxue Li, Ang Si, Yuan Wang, Jiaheng Zhou, Ting Xu, Tianheng Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China Shanghai Univ Sch Microelect Shanghai 200444 Peoples R China

brain-computer interfaces (BCIs) aim to directly bridge the human brain and the outside world through acquiring and processing the brain signals in real time. In recent two decades, tremendous progress has been made in terms of BCI paradigms and decoding algorithms. The information transfer rate of the BCI systems has been significantly enhanced, and the communication community are considering including the BCI technologies into the 6G networks and beyond. However, some critical problems and challenges remain. To some extent, the BCI systems resemble the wireless communication systems, as brain signals are generated and transmitted by the neurons and received by the sensors, propagating through multiple brain tissues which cause inevitable attenuation and mixing effects. Therefore, by following this idea, a systematic review of the BCI technologies is provided through comparing it with wireless communications. The main challenges of BCI lie in brain channel modelling, modulation, signal processing and detection. The latest and some classic researches are covered. In addition, typical applications of BCIs such as the Quality-of-Experience (QoE) metric and Internet-of-brains (IoB) are predicted. Then, we present a prospect of BCIs being integrated with future wireless communication systems. Also, the technical challenges regarding communication and computation to fulfill the prospect are discussed, as well as privacy, security and ethical concerns. Therefore, this review aims to comprehensively overview BCI-inspired communication on two levels, the BCI itself being a communication system and future integration of BCI and communication as a whole system.

关键词： Wireless communication Communication systems 6G mobile communication Electroencephalography Reviews Sensors Decoding brain-computer interface brain-computer communication biomedical communication

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brain-computer interface for amyotrophic lateral sclerosis patients using deep learning network

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NEURAL COMPUTING & APPLICATIONS 2022年第16期34卷 13439-13453页

作者： Ramakrishnan, Jayabrabu Mavaluru, Dinesh Sivasakthivel, Ramkumar Alqahtani, Abdulrahman Saad Mubarakali, Azath Retnadhas, Mervin Jazan Univ Coll Comp Sci & Informat Technol Jazan Saudi Arabia Saudi Elect Univ Coll Comp & Informat Riyadh Saudi Arabia Kalasalingam Acad Res & Educ Sch Comp Krishnankoil India Najran Univ Coll Comp Sci & Informat Syst Najran Saudi Arabia King Khalid Univ Coll Comp Sci Abha Saudi Arabia

Individuals with Motor Neuron Disease were unable to move from one place to another because it gradually reduced all the voluntarily movement due to the degeneration of upper and lower motors neurons. The solution to this problem was to develop rehabilitating devices using biosignals. In this study, we have designed and developed electrooculogram-based wheelchair control using Cross Power Spectral Density. The convolution neural network to verify the performance and recognition accuracy of the wheelchair navigation in the indoor environment by using four trained users and four untrained users between the different age-groups and obtained the accuracy of 91.18% and 86.88% by using four fundamental tasks. From the indoor performance, the subject S4 from trained users outperforms all the trained subjects with an average classification accuracy of 93.51%. To verify the recognition accuracy, we conducted the online performance from the online performances subject S4 from trained subjects outperforms remaining trained subjects at the same time the subject S6 from untrained subjects outperforms all the untrained subjects. From the entire study, we analyzed that classification accuracy of subjects S4 was appreciated compared to other subjects. Through the research, we confirmed that the entire trained subject's performance was maximum compared to the untrained subjects in all the circumstances.

关键词： Electrooculography Amyotrophic lateral sclerosis Convolution neural network Cross power spectral density Human-computer interface brain-computer interface

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Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2025年第5期63卷 1367-1381页

作者： Fu, Rongrong Niu, Shaoxiong Feng, Xiaolei Shi, Ye Jia, Chengcheng Zhao, Jing Wen, Guilin Yanshan Univ Dept Elect Engn Measurement Technol & Instrumentat Key Lab Hebei P Qinhuangdao Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao Peoples R China Yanshan Univ Key Lab Intelligent Rehabil & Neromodulat Hebei Pr Qinhuangdao Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada Yanshan Univ Sch Mech Engn Qinhuangdao Peoples R China

This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance the performance both in quality and reliability. The proposed MVMD-MSI algorithm combines the advantages of multivariate variational mode decomposition (MVMD) and multivariate synchronization index (MSI). Compared to widely used algorithms, the novelty of this method is its capability of decomposing nonlinear and non-stationary EEG signals into intrinsic mode functions (IMF) across different frequency bands with the best center frequency and bandwidth. Therefore, SSVEP decoding performance can be improved by this method, and the effectiveness of MVMD-MSI is evaluated by the robot with 6 degrees-of-freedom. Offline experiments were conducted to optimize the algorithm's parameters, resulting in significant improvements. Additionally, the algorithm showed good performance even with fewer channels and shorter data lengths. In online experiments, the algorithm achieved an average accuracy of 98.31% at 1.8 s, confirming its feasibility and effectiveness for real-time SSVEP BCI-based robotic arm applications. The MVMD-MSI algorithm, as proposed, represents a significant advancement in SSVEP analysis for robotic control systems. It enhances decoding performance and shows promise for practical application in this field.

关键词： Steady-state visual evoked potential brain-computer interface Robotic arm Multivariate variational mode decomposition Multivariate synchronization index

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brain-computer interface using Directional Auditory Perception 23

Brain-Computer Interface using Directional Auditory Percepti...

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4th Augmented Humans International Conference (AHs)

作者： Koike, Yuto Hiroi, Yuichi Itoh, Yuta Rekimoto, Jun Univ Tokyo Tokyo Japan Univ Tokyo RIKEN AIP Tokyo Japan Univ Tokyo Sony CSL Kyoto Tokyo Kyoto Japan

ISBN: (纸本)9781450399845

We investigate the potential of brain-computer interface (BCI) using electroencephalogram (EEG) induced by listening (or recalling) auditory stimuli of different directions. In the initial attempt, we apply a time series classification model based on deep learning to the EEG to demonstrate whether each EEG can be classified by recognizing binary (left or right) auditory directions. The results showed high classification accuracy when trained and tested on the same users. Discussion is provided to further explore this topic.

关键词： brain-computer interface BCI brain-machine interface BMI Electroencephalography Electroencephalogram EEG Directional Auditory Sensation

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brain-computer interface Controlled Functional Electrical Stimulation: Evaluation With Healthy Subjects and Spinal Cord Injury Patients

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IEEE ACCESS 2022年 10卷 46834-46852页

作者： Hernandez-Rojas, Luis G. Cantillo-Negrete, Jessica Mendoza-Montoya, Omar Carino-Escobar, Ruben, I Leyva-Martinez, Ismael Aguirre-Guemez, Ana, V Barrera-Ortiz, Aida Carrillo-Mora, Paul Antelis, Javier M. Tecnol Monterrey Escuela Ingn & Ciencias Monterrey 64849 Nuevo Leon Mexico Inst Nacl Rehabil Luis Guillermo Ibarra Ibarra Div Res Med Engn Mexico City 14389 DF Mexico Ctr Rehabil DIF Zapata Gaby Brimmer Mexico City 03320 DF Mexico Inst Nacl Rehabil Luis Guillermo Ibarra Ibarra Div Neurol Rehabil Mexico City 14389 DF Mexico Inst Nacl Rehabil Luis Guillermo Ibarra Ibarra Div Neurosci Mexico City 14389 DF Mexico Univ Maryland Sch Dent Dept Neural & Pain Sci Baltimore MD 21201 USA

This work presents the design, implementation, and feasibility evaluation of a Motor Imagery (MI) based brain-computer interface (BCI) developed to control a Functional Electrical Stimulation (FES) device. The aim of this system is to assist the upper limb motor recovery of patients with spinal cord injury (SCI). With this BCI-controlled FES system, the user performs open and close MI with either the left or right hand, which if detected is used to provide visual feedback and electroestimulation to muscles in the forearm to perform the corresponding grasping movement. The system was evaluated with seven healthy subjects (HS group) and two SCI patients (SC group) in several experimental sessions across different days. Each experimental session consisted of a training routine devoted to collect calibration EEG data to train the BCI machine learning model, and of a validation routine devoted to validate system in online operation. The online system validation showed an accuracy of the recognition of the MI task that ranged between 78% and 81% for HS participants and between 63% and 93% for SCI participants. Additionally, the time taken by the BCI system to trigger the FES device ranged between 7.05 and 7.29 s for HS participants and between 8.43 s and 13.91 s for SCI participants. Finally, significant negative correlations were observed (r = -0.418, p = 0.024 and r = -0.437, p = 0.018 for left and right hand MI conditions, respectively) between the online BCI performance with a quantitative EEG parameter based on event-related desynchronization/synchronization analysis. The results of this work indicate the feasibility of the proposed BCI coupled to a FES device to be used for SCI patients with a moderate level of disability and provides evidence of the functionality of the proposed BCI system in a motor rehabilitation context.

关键词： brain-computer interface spinal cord injury motor rehabilitation functional electrical stimulation motor imagery electroencephalography

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Population-based evolutionary search for joint hyperparameter and architecture optimization in brain-computer interface

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 264卷

作者： Shin, Dong-Hee Lee, Deok-Joong Han, Ji-Wung Son, Young-Han Kam, Tae-Eui Korea Univ Dept Artificial Intelligence Seoul 02841 South Korea

In recent years, deep learning (DL)-based models have become the de facto standard for motor imagery brain-computer interface (MI-BCI) systems due to their notable performance. However, these models often require extensive hyperparameter optimization process to achieve optimal results. To tackle this challenge, recent studies have proposed various methods to automate this process. Despite promising results, these methods overlook the architecture elements, which are crucial factors for MI-BCI system performance and are highly intertwined with hyperparameter settings. To overcome this limitation, we propose a joint optimization framework that uses a population-based evolutionary search to optimize both hyperparameters and architectures. Our framework adopts a two-stage optimization approach that alternates between hyperparameter and architecture optimization to effectively manage the complexity of the joint search process. Furthermore, we introduce a novel ensemble method that leverages diverse promising configurations to enhance generalization and robustness. Evaluations on two public MI-BCI datasets show that our framework consistently outperforms competing methods across a range of backbone models, demonstrating its effectiveness and versatility.

关键词： Hyperparameter optimization Architecture optimization Evolutionary search Joint optimization brain-computer interface

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