Challenges and Suggestions of Ethical Review on Clinical Research Involving Brain-Computer Interfaces

Brain-computer interface(BCI)technology is rapidly advancing in medical research and application.As an emerging biomedical engineering technology,it has garnered significant attention in the clinical research of brain disease diagnosis and treatment,neurological rehabilitation,and mental health.However,BCI also raises several challenges and ethical concerns in clinical research.In this article,the authors investigate and discuss three aspects of BCI in medicine and healthcare:the state of international ethical governance,multidimensional ethical challenges pertaining to BCI in clinical research,and suggestive concerns for ethical review.Despite the great potential of frontier BCI research and development in the field of medical care,the ethical challenges induced by itself and the complexities of clinical research and brain function have put forward new special fields for ethics in BCI.To ensure"responsible innovation"in BCI research in healthcare and medicine,the creation of an ethical global governance framework and system,along with special guidelines for cutting-edge BCI research in medicine,is suggested.

Double Deep Q-Network Decoder Based on EEG Brain-Computer Interface

Brain-computer interfaces(BCI)use neural activity as a control signal to enable direct communication between the human brain and external devices.The electrical signals generated by the brain are captured through electroencephalogram(EEG)and translated into neural intentions reflecting the user’s behavior.Correct decoding of the neural intentions then facilitates the control of external devices.Reinforcement learning-based BCIs enhance decoders to complete tasks based only on feedback signals(rewards)from the environment,building a general framework for dynamic mapping from neural intentions to actions that adapt to changing environments.However,using traditional reinforcement learning methods can have challenges such as the curse of dimensionality and poor generalization.Therefore,in this paper,we use deep reinforcement learning to construct decoders for the correct decoding of EEG signals,demonstrate its feasibility through experiments,and demonstrate its stronger generalization on motion imaging(MI)EEG data signals with high dynamic characteristics.

A Survey on Machine Learning Algorithms in Little-Labeled Data for Motor Imagery-Based Brain-Computer Interfaces

The Brain-Computer Interfaces(BCIs)had been proposed and used in therapeutics for decades.However,the need of time-consuming calibration phase and the lack of robustness,which are caused by little-labeled data,are restricting the advance and application of BCI,especially for the BCI based on motor imagery(MI).In this paper,we reviewed the recent development in the machine learning algorithm used in the MI-based BCI,which may provide potential solutions for addressing the issue.We classified these algorithms into two categories,namely,and enhancing the representation and expanding the training set.Specifically,these methods of enhancing the representation of features collected from few EEG trials are based on extracting features of multiple bands,regularization,and so on.The methods of expanding the training dataset include approaches of transfer learning(session to session transfer,subject to subject transfer)and generating artificial EEG data.The result of these techniques showed the resolution of the challenges to some extent.As a developing research area,the study of BCI algorithms in little-labeled data is increasingly requiring the advancement of human brain physiological structure research and more transfer learning algorithms research.

A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm

Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most of the existing schemes employ open-loop control,and thus are unable to track positional errors,resulting in failures in taking necessary online corrective actions.There are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position control.These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient control.Second,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state error.This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors.Other than the above,the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors.Experiments undertaken reveal that the steady-state error is reduced to 0.2%.The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.

Common Spatial Pattern Ensemble Classifier and Its Application in Brain-Computer Interface

Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble （CSPE） classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.

Image Segmentation-P300 Selector: A Brain–Computer Interface System for Target Selection

Brain–computer interface (BCI) systems, such as the P300 speller, enable patients to express intentions withoutnecessitating extensive training.However, the complexity of operational instructions and the slow pace of characterspelling pose challenges for some patients. In this paper, an image segmentation P300 selector based on YOLOv7-mask and DeepSORT is proposed. The proposed system utilizes a camera to capture real-world objects forclassification and tracking. By applying predefined stimulation rules and object-specificmasks, the proposed systemtriggers stimuli associated with the objects displayed on the screen, inducing the generation of P300 signals in thepatient’s brain. Its video processing mechanism enables the system to identify the target the patient is focusing oneven if the object is partially obscured, overlapped, moving, or changing in number. The system alters the target’scolor display, thereby conveying the patient’s intentions to caregivers. The data analysis revealed that the selfrecognitionaccuracy of the subjects using this method was between 92% and 100%, and the cross-subject P300recognition precision was 81.9%–92.1%. This means that simple instructions such as “Do not worry, just focuson what you desire” effectively discerned the patient’s intentions using the Image Segmentation-P300 selector. Thisapproach provides cost-effective support and allows patients with communication difficulties to easily express theirneeds.

Neurological rehabilitation of stroke patients via motor imaginary-based brain-computer interface technology

The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classification were significantly greater at 4 weeks after treatment （P 〈 0.01）, which suggested that motor imaginary-based brain-computer interface technology improved balance and walking in stroke patients.

Probabilistic Methods in Multi-Class Brain-Computer Interface

Abstract-Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface （BCI）： support vector machine （SVM） with posteriori probability （PSVM） and Bayesian linear discriminant analysis with probabilistic output （PBLDA）. A comparative evaluation of these two methods is conducted. The results shows that： 1） probabilistie information can improve the performance of BCI for subjects with high kappa coefficient, and 2） PSVM usually results in a stable kappa coefficient whereas PBLDA is more efficient in estimating the model parameters.

Application of Brain-Computer-Interface in Awareness Detection Using Machine Learning Methods

The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests,however,the misdiagnosis rates have been relatively high.In this study,we applied brain-computer interface(BCI)to awareness detection with a passive auditory stimulation paradigm.12 subjects with normal hearing were invited to collect electroencephalogram(EEG)based on a BCI communication system,in which EEG signals are transmitted wirelessly.After necessary preprocessing,RBF-SVM and EEGNet were used for algorithm realization and analysis.For a single subject,RBF-SVM can distinguish his(her)name stimuli awareness with classification accuracies ranging from 60-95%.EEGNet was used to learn all subjects'data and improved accuracy to 78.04%for characteristics finding and model generalization.Moreover,we completed the supplementary analysis work from the time domain and time-frequency domain.This study applied BCI communication to human awareness detection,proposed a passive auditory paradigm,and proved the effectiveness,which could be an inspiration for brain,mental or physical diseases diagnosis and detection.

Transfer Learning Algorithm Design for Feature Transfer Problem in Motor Imagery Brain-computer Interface

The non-stationary of the motor imagery electroencephalography(MI-EEG)signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI).The nonstationary of the MI-EEG signal and the changes of the experimental environment make the feature distribution of the testing set and training set deviates,which reduces the classification accuracy of MI-BCI.In this paper,we propose a Kullback–Leibler divergence(KL)-based transfer learning algorithm to solve the problem of feature transfer,the proposed algorithm uses KL to measure the similarity between the training set and the testing set,adds support vector machine(SVM)classification probability to classify and weight the covariance,and discards the poorly performing samples.The results show that the proposed algorithm can significantly improve the classification accuracy of the testing set compared with the traditional algorithms,especially for subjects with medium classification accuracy.Moreover,the algorithm based on transfer learning has the potential to improve the consistency of feature distribution that the traditional algorithms do not have,which is significant for the application of MI-BCI.

Performance and Implementations of Vibrotactile Brain-Computer Interface with Ipsilateral and Bilateral Stimuli

The tactile P300 brain-computer interface( BCI) is related to the somatosensory perception and response of the human brain,and is different from visual or audio BCIs. Recently,several studies focused on the tactile stimuli delivered to different parts of the human body. Most of these stimuli were symmetrically bilateral.Only a fewstudies explored the influence of tactile stimuli laterality.In the current study,we extensively tested the performance of a vibrotactile BCI system using ipsilateral stimuli and bilateral stimuli.Two vibrotactile P300-based paradigms were tested. The target stimuli were located on the left and right forearms for the left forearm and right forearm( LFRF) paradigm,and on the left forearm and calf for the left forearm and left calf( LFLC)paradigm. Ten healthy subjects participated in this study. Our experiments and analysis showed that the bilateral paradigm( LFRF) elicited larger P300 amplitude and achieved significantly higher classification accuracy than the ipsilateral paradigm( LFLC). However, both paradigms achieved classification accuracies higher than 70% after the completion of several trials on average,which was usually regarded as the minimum accuracy level required for BCI system to be deemed useful.

Possibility to Realize the Brain-Computer Interface from the Quantum Brain Model Based on Superluminal Particles

R. Penrose and S. Hameroff have proposed an idea that the brain can attain high efficient quantum computation by functioning of microtubular structure of neurons in the cytoskelton of biological cells, including neurons of the brain. But Tegmark estimated the duration of coherence of a quantum state in a warm wet brain to be on the order of 10>–13

Real-Time Detection of Human Drowsiness via a Portable Brain-Computer Interface

In this paper, we proposed a new concept: depth of drowsiness, which can more precisely describe the drowsiness than existing binary description. A set of effective markers for drowsiness: normalized band norm was successfully developed. These markers are invariant from voltage amplitude of brain waves, eliminating the need for calibrating the voltage output of the brain-computer interface devices. A new polling algorithm was designed and implemented for computing the depth of drowsiness. The time cost of data acquisition and processing for each estimate is about one second, which is well suited for real-time applications. Test results with a portable brain-computer interface device show that the depth of drowsiness computed by the method in this paper is generally invariant from ages of test subjects and sensor channels (P3 and C4). The comparison between experiment and computing results indicate that the new method is noticeably better than one of the recent methods in terms of accuracy for predicting the drowsiness.

On Similarities and Differences of Invasive and Non-Invasive Electrical Brain Signals in Brain-Computer Interfacing

We perceive that some Brain-Computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although invasive and non-invasive BCI signals are different, the underlying origin of electrical BCIs signals is the same.

Design of an EEG Preamplifier for Brain-Computer Interface

As a non-invasive neurophysiologieal index for brain-computer interface （BCI）, electroencephalogram （EEG） attracts much attention at present. In order to have a portable BCI, a simple and efficient pre-amplifier is crucial in practice. In this work, a preamplifier based on the characteristics of EEG signals is designed, which consists of a highly symmetrical input stage, low-pass filter, 50 Hz notch filter and a post amplifier. A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment. The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.

Advances in electrode interface materials and modification technologies for brain-computer interfaces

Recent advances in neuroelectrode interface materials and modification technologies are reviewed. Brain-computer interface is the new method of human-computer interaction, which not only can realise the exchange of information between the human brain and external devices, but also provides a brand-new means for the diagnosis and treatment of brain-related diseases. The neural electrode interface part of brain-computer interface is an important area for electrical, optical and chemical signal transmission between brain tissue system and external electronic devices, which determines the performance of brain-computer interface. In order to solve the problems of insufficient flexibility, insufficient signal recognition ability and insufficient biocompatibility of traditional rigid electrodes, researchers have carried out extensive studies on the neuroelectrode interface in terms of materials and modification techniques. This paper introduces the biological reactions that occur in neuroelectrodes after implantation into brain tissue and the decisive role of the electrode interface for electrode function. Following this, the latest research progress on neuroelectrode materials and interface materials is reviewed from the aspects of neuroelectrode materials and modification technologies, firstly taking materials as a clue, and then focusing on the preparation process of neuroelectrode coatings and the design scheme of functionalised structures.

Intelligent Interfaces: Pedagogical Agents and Virtual Humans

Little by little, we are entering the new era, intelligent interfaces are absorbing us more and more every day, and artificial intelligence makes its presence in a stealthy way. Virtual humans that represent an evolution of autonomous virtual agents;they are computer programs and in the future capable of carrying out different activities in certain environments. They will give the illusion of being human;they will have a body, and they will be immersed in an environment. They will have a set of senses that will allow them: 1) Sensations and therefore associated expressions;2) Communication;3) Learning;4) Remembering events, among others. By integrating the above, they will have a personality and autonomy, so they will be able to plan with respect to objectives;allowing them to decide and take actions with their body, in other words, they will count on awareness. The applications will be focused on environments that they will inhabit, or as interfaces that will interact with other systems. The application domains will be multiple;one of them being education. This article shows the design of OANNA like an avatar with the role of pedagogical agent. It was modeled as an affective-cognitive structure related to the teaching-learning process linked to a pedagogical agent that represents the interface of an artilect. OANNA, has the necessary animations for intervention within the teaching-learning process.

Functional near-infrared spectroscopy in non-invasive neuromodulation

Non-invasive cerebral neuromodulation technologies are essential for the reorganization of cerebral neural networks,which have been widely applied in the field of central neurological diseases,such as stroke,Parkinson’s disease,and mental disorders.Although significant advances have been made in neuromodulation technologies,the identification of optimal neurostimulation paramete rs including the co rtical target,duration,and inhibition or excitation pattern is still limited due to the lack of guidance for neural circuits.Moreove r,the neural mechanism unde rlying neuromodulation for improved behavioral performance remains poorly understood.Recently,advancements in neuroimaging have provided insight into neuromodulation techniques.Functional near-infrared spectroscopy,as a novel non-invasive optical brain imaging method,can detect brain activity by measuring cerebral hemodynamics with the advantages of portability,high motion tole rance,and anti-electromagnetic interference.Coupling functional near-infra red spectroscopy with neuromodulation technologies offe rs an opportunity to monitor the cortical response,provide realtime feedbac k,and establish a closed-loop strategy integrating evaluation,feedbac k,and intervention for neurostimulation,which provides a theoretical basis for development of individualized precise neuro rehabilitation.We aimed to summarize the advantages of functional near-infra red spectroscopy and provide an ove rview of the current research on functional near-infrared spectroscopy in transcranial magnetic stimulation,transcranial electrical stimulation,neurofeedback,and braincomputer interfaces.Furthermore,the future perspectives and directions for the application of functional near-infrared spectroscopy in neuromodulation are summarized.In conclusion,functional near-infrared spectroscopy combined with neuromodulation may promote the optimization of central pellral reorganization to achieve better functional recovery form central nervous system diseases.

情感化社交触觉界面的交互原型开发工具设计研究

目的触觉交互是自然人机交互中的热点方向,触觉交互界面具备支持用户之间或人机之间情感交流的潜力。本研究旨在通过开发一种流程化、低门槛、轻成本且可体验的触觉界面原型工具,帮助设计师克服在触觉交互界面设计中的挑战,包括触觉生理学及触觉交互原理等知识基础、设计流程引导和技术开发支持,从而实现更有效的用户实验和设计深化。方法首先,利用参与式设计方法,确定交互设计师在触觉交互界面设计中的主要挑战和核心需求;其次,以此为基础构建触觉交互设计流程引导及情感触觉信号编码界面设计的必要元素,开发支持情感触觉交互原型迭代的硬件工具,并完成原型开发工具的系统设计;最后,通过用户实验中的场景化测试验证初步的气动微流控柔性薄膜阵列的基础情感交互效果,并建立了基础的情感-触觉映射参考效果库。结论本研究开发了触觉界面原型工具,通过系统化的设计流程和简易的操作方式,有助于触觉交互设计师更加灵活便捷地进行设计研究和实践。该工具创新性地提升了触觉界面的设计效率,为情感触觉交互体验设计提供了丰富的可能性。

A review of artificial intelligence for EEG-based brain-computer interfaces and applications

The advancement in neuroscience and computer science promotes the ability of the human brain to communicate and interact with the environment,making brain–computer interface(BCI)top interdisciplinary research.Furthermore,with the modern technology advancement in artificial intelligence(AI),including machine learning(ML)and deep learning(DL)methods,there is vast growing interest in the electroencephalogram(EEG)-based BCIs for AI-related visual,literal,and motion applications.In this review study,the literature on mainstreams of AI for the EEG-based BCI applications is investigated to fill gaps in the interdisciplinary BCI field.Specifically,the EEG signals and their main applications in BCI are first briefly introduced.Next,the latest AI technologies,including the ML and DL models,are presented to monitor and feedback human cognitive states.Finally,some BCI-inspired AI applications,including computer vision,natural language processing,and robotic control applications,are presented.The future research directions of the EEG-based BCI are highlighted in line with the AI technologies and applications.