Brain-computer Interface Research Articles

Non-voluntary BCI explantation: assessing possible neurorights violations in light of contrasting mental ontologies

In research involving patients with implantable brain–computer interfaces (BCIs), there is a regulatory gap concerning post-trial responsibilities and duties of sponsors and investigators towards implanted patients. In this article, we...

Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation

Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) systems. Most vigilance estimation methods rely on a large amount of labeled data to train a satisfactory model for the specific subject, which limits the practical application of the methods. This study aimed to build a reliable vigilance estimation method using a small amount of unlabeled calibration data. We conducted a vigilance experiment in the designed BCI-based cursor-control task. Electroencephalogram (EEG) signals of eighteen participants were recorded in two sessions on two different days. And, we proposed a contrastive fine-grained domain adaptation network (CFGDAN) for vigilance estimation. Here, an adaptive graph convolution network (GCN) was built to project the EEG data of different domains into a common space. The fine-grained feature alignment mechanism was designed to weight and align the feature distributions across domains at the EEG channel level, and the contrastive information preservation module was developed to preserve the useful target-specific information during the feature alignment. The experimental results show that the proposed CFGDAN outperforms the compared methods in our BCI vigilance dataset and SEED-VIG dataset. Moreover, the visualization results demonstrate the efficacy of the designed feature alignment mechanisms. These results indicate the effectiveness of our method for vigilance estimation. Our study is helpful for reducing calibration efforts and promoting the practical application potential of vigilance estimation methods.

Applications of 2D Nanomaterials in Neural Interface.

Neural interfaces are crucial conduits between neural tissues and external devices, enabling the recording and modulation of neural activity. However, with increasing demand, simple neural interfaces are no longer adequate to meet the requirements for precision, functionality, and safety. There are three main challenges in fabricating advanced neural interfaces: sensitivity, heat management, and biocompatibility. The electrical, chemical, and optical properties of 2D nanomaterials enhance the sensitivity of various types of neural interfaces, while the newly developed interfaces do not exhibit adverse reactions in terms of heat management and biocompatibility. Additionally, 2D nanomaterials can further improve the functionality of these interfaces, including magnetic resonance imaging (MRI) compatibility, stretchability, and drug delivery. In this review, we examine the recent applications of 2D nanomaterials in neural interfaces, focusing on their contributions to enhancing performance and functionality. Finally, we summarize the advantages and disadvantages of these nanomaterials, analyze the importance of biocompatibility testing for 2D nanomaterials, and propose that improving and developing composite material structures to enhance interface performance will continue to lead the forefront of this field.

To investigate the brain, think like a neuron

Axon-inspired brain-machine interface minimizes tissue damage from insertion and records high-quality electrical signals from the cerebellum.

A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression

In this study, the fundamentals of electroencephalography signals, their categorization into frequency sub-bands, the circuitry used for their acquisition, and the impact of noise interference on signal acquisition are examined. Additionally, design specifications for medical-grade and research-grade EEG circuits and a comprehensive analysis of various analog front-end architectures for electroencephalograph (EEG) circuit design are presented. Three distinct selected case studies are examined in terms of comparative evaluation with generic EEG circuit design templates. Moreover, a novel one-channel battery-supplied EEG analog front-end circuit designed to address the requirements of usage protocols containing strong compound muscle movements is introduced. Furthermore, a realistic input signal generator circuit is proposed that models the human body and the electromagnetic interference from its surroundings. Experimental simulations are conducted in 50 Hz and 60 Hz electrical grid environments to evaluate the performance of the novel design. The results demonstrate the efficacy of the proposed system, particularly in terms of bandwidth, portability, Common Mode Rejection Ratio, gain, suppression of muscle movement artifacts, electrostatic discharge and leakage current protection. Conclusively, the novel design is cost-effective and suitable for both commercial and research single-channel EEG applications. It can be easily incorporated in Brain–Computer Interfaces and neurofeedback training systems.

Customized Flexible Osteoid Electrode for Traumatic Brain Injury Induced Posttraumatic Epilepsy Monitoring

AbstractTraumatic brain injury (TBI)‐induced post‐traumatic epilepsy (PTE) is a serious brain disease that causes the loss of motor functions, cognitive impairments, and even death. Real‐time and long‐term electrophysiological signal monitoring has significant implication for the treatment of patient with TBI. Brain–computer interfaces (BCIs) utilizing flexible electrodes enable accurate epilepsy identification by electrocorticography (ECoG) signal monitoring. In this study, a customized flexible osteoid electrode is developed, which contributes to demands and personal differentiation among patients with TBI. The end product of osteoid electrode after full osseointegration will be a stable and firmly integrated structure within the bone tissue, providing a secure platform for continuous electrophysiological signal monitoring. The flexibility and conductivity of the osteoid electrode enables high‐quality and long‐term epilepsy monitoring. Biocompatibility of the osteoid electrode is also demonstrated by in vitro cell experiments. Simultaneously, the porous osteoid electrode also exhibits osseointegration ability based on potential long‐term ECoG signal monitoring, which suggests its potential for the comprehensive treatment of TBI patients. This work not only introduces a potential therapeutic scaffold for TBI‐related diseases, but also provides promising techniques for clinical ECoG monitoring in patients with TBI.

Neural Decoding of Spontaneous Overt and Intended Speech.

The aim of this study was to decode intended and overt speech from neuromagnetic signals while the participants performed spontaneous overt speech tasks without cues or prompts (stimuli). Magnetoencephalography (MEG), a noninvasive neuroimaging technique, was used to collect neural signals from seven healthy adult English speakers performing spontaneous, overt speech tasks. The participants randomly spoke the words yes or no at a self-paced rate without cues. Two machine learning models, namely, linear discriminant analysis (LDA) and one-dimensional convolutional neural network (1D CNN), were employed to classify the two words from the recorded MEG signals. LDA and 1D CNN achieved average decoding accuracies of 79.02% and 90.40%, respectively, in decoding overt speech, significantly surpassing the chance level (50%). The accuracy for decoding intended speech was 67.19% using 1D CNN. This study showcases the possibility of decoding spontaneous overt and intended speech directly from neural signals in the absence of perceptual interference. We believe that these findings make a steady step toward the future spontaneous speech-based brain-computer interface.

Nonlinear difference subspace method of motor imagery EEG classification in brain-computer interface

Noninvasive Motor Imagery (MI) electroencephalography (EEG) based brain-computer interface (BCI) systems are in high demand in both the medical and consumer sectors. However, the low signal-to-noise ratio of EEG and its nonlinear dynamics present challenges in processing. The effectiveness of information retrieval is directly related to the complexity of the algorithms, making BCI impractical for real-time applications. To enhance performance, this study introduced classification frameworks using nonlinear subspace and difference subspace methods, employing manifold learning techniques such as Kernel principal component analysis (KPCA) and Isometric feature mapping (Isomap). The optimal dimensions for the subspaces were selected using overlapping criteria in conjunction with the breadth-first-search (BFS) and depth-first-search (DFS) graph search methods. The proposed methods were evaluated on the BCI competition III and IV datasets using metrics including classification accuracy, Cohen's kappa coefficient, and F1-score. By exploiting the nonlinear characteristics of EEG and employing the geodesic distance between all pairs of trials, the modified Isomap called supervised discriminant Isomap projection (SD-ISoP) outperformed the KPCA-based methods and other recently proposed state-of-the-art methods. The results demonstrssate that the SD-ISoP-based difference subspace method achieved an average classification accuracy of 86.25% and 85.44% for BCI competition III and IV datasets, respectively.

Considerations and discussions on the clear definition and definite scope of brain-computer interfaces.

Brain-computer interface (BCI) is a revolutionizing human-computer interaction with potential applications in both medical and non-medical fields, emerging as a cutting-edge and trending research direction. Increasing numbers of groups are engaging in BCI research and development. However, in recent years, there has been some confusion regarding BCI, including misleading and hyped propaganda about BCI, and even non-BCI technologies being labeled as BCI. Therefore, a clear definition and a definite scope for BCI are thoroughly considered and discussed in the paper, based on the existing definitions of BCI, including the six key or essential components of BCI. In the review, different from previous definitions of BCI, BCI paradigms and neural coding are explicitly included in the clear definition of BCI provided, and the BCI user (the brain) is clearly identified as a key component of the BCI system. Different people may have different viewpoints on the definition and scope of BCI, as well as some related issues, which are discussed in the article. This review argues that a clear definition and definite scope of BCI will benefit future research and commercial applications. It is hoped that this review will reduce some of the confusion surrounding BCI and promote sustainable development in this field.

Evaluating the real-world usability of BCI control systems with augmented reality: a user study protocol.

Brain-computer interfaces (BCI) enable users to control devices through their brain activity. Motor imagery (MI), the neural activity resulting from an individual imagining performing a movement, is a common control paradigm. This study introduces a user-centric evaluation protocol for assessing the performance and user experience of an MI-based BCI control system utilizing augmented reality. Augmented reality is employed to enhance user interaction by displaying environment-aware actions, and guiding users on the necessary imagined movements for specific device commands. One of the major gaps in existing research is the lack of comprehensive evaluation methodologies, particularly in real-world conditions. To address this gap, our protocol combines quantitative and qualitative assessments across three phases. In the initial phase, the BCI prototype's technical robustness is validated. Subsequently, the second phase involves a performance assessment of the control system. The third phase introduces a comparative analysis between the prototype and an alternative approach, incorporating detailed user experience evaluations through questionnaires and comparisons with non-BCI control methods. Participants engage in various tasks, such as object sorting, picking and placing, and playing a board game using the BCI control system. The evaluation procedure is designed for versatility, intending applicability beyond the specific use case presented. Its adaptability enables easy customization to meet the specific user requirements of the investigated BCI control application. This user-centric evaluation protocol offers a comprehensive framework for iterative improvements to the BCI prototype, ensuring technical validation, performance assessment, and user experience evaluation in a systematic and user-focused manner.

EEG Analyses of visual cue effects on executed movements

BackgroundIn electroencephalographic (EEG) or electrocorticographic (ECoG) experiments, visual cues are commonly used for timing synchronization but may inadvertently induce neural activity and cognitive processing, posing challenges when decoding self-initiated tasks. New methodTo address this concern, we introduced four new visual cues (Fade, Rotation, Reference, and Star) and investigated their impact on brain signals. Our objective was to identify a cue that minimizes its influence on brain activity, facilitating cue-effect free classifier training for asynchronous applications, particularly aiding individuals with severe paralysis. Results22 able-bodied, right-handed participants aged 18–30 performed hand movements upon presentation of the visual cues. Analysis of time-variability between movement onset and cue-aligned data, grand average MRCP, and classification outcomes revealed significant differences among cues. Rotation and Reference cue exhibited favorable results in minimizing temporal variability, maintaining MRCP patterns, and achieving comparable accuracy to self-paced signals in classification. Comparison with existing methodsOur study contrasts with traditional cue-based paradigms by introducing novel visual cues designed to mitigate unintended neural activity. We demonstrate the effectiveness of Rotation and Reference cue in eliciting consistent and accurate MRCPs during motor tasks, surpassing previous methods in achieving precise timing and high discriminability for classifier training. ConclusionsPrecision in cue timing is crucial for training classifiers, where both Rotation and Reference cue demonstrate minimal variability and high discriminability, highlighting their potential for accurate classifications in online scenarios. These findings offer promising avenues for refining brain-computer interface systems, particularly for individuals with motor impairments, by enabling more reliable and intuitive control mechanisms.

ADVANCEMENTS IN BRAIN-COMPUTER INTERFACES: THE IMPACT OF AI AND GENERATIVE MODELS ON NEURAL DECODING AND APPLICATION

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices, offering transformative potential for individuals with neurological disorders and beyond. Recent advancements in artificial intelligence (AI), particularly in generative models, have significantly improved the accuracy and efficiency of neural decoding in BCIs. This paper provides a comprehensive overview of these advancements, focusing on the role of AI and generative models in enhancing BCI performance. I discuss the contributions of leading companies like Neuralink, Synchron, and Paradromics, and analyze key research findings on signal processing, machine learning algorithms, and ethical considerations. My synthesis of current literature and industry insights highlights the transformative potential of AI-driven BCIs while addressing the technical and ethical challenges that need to be overcome to fully realize their potential. I emphasize the importance of continued research in refining generative AI models, enhancing real-time processing capabilities, and establishing ethical frameworks to guide the development and application of BCIs.

EEG-VTTCNet: A loss joint training model based on the vision transformer and the temporal convolution network for EEG-based motor imagery classification

Brain-computer interface (BCI) is a technology that directly connects signals between the human brain and a computer or other external device. Motor imagery electroencephalographic (MI-EEG) signals are considered a promising paradigm for BCI systems, with a wide range of potential applications in medical rehabilitation, human–computer interaction, and virtual reality. Accurate decoding of MI-EEG signals poses a significant challenge due to issues related to the quality of the collected EEG data and subject variability. Therefore, developing an efficient MI-EEG decoding network is crucial and warrants research. This paper proposes a loss joint training model based on the vision transformer (VIT) and the temporal convolutional network (EEG-VTTCNet) to classify MI-EEG signals. To take advantage of multiple modules together, the EEG-VTTCNet adopts a shared convolution strategy and a dual-branching strategy. The dual-branching modules perform complementary learning and jointly train shared convolutional modules with better performance. We conducted experiments on the BCI Competition IV-2a and IV-2b datasets, and the proposed network outperformed the current state-of-the-art techniques with an accuracy of 84.58% and 90.94%, respectively, for the subject-dependent mode. In addition, we used t-SNE to visualize the features extracted by the proposed network, further demonstrating the effectiveness of the feature extraction framework. We also conducted extensive ablation and hyperparameter tuning experiments to construct a robust network architecture that can be well generalized.

Feature Extraction and Classification of Motor Imagery EEG Signals in Motor Imagery for Sustainable Brain–Computer Interfaces

Feature Extraction and Classification of Motor Imagery EEG Signals in Motor Imagery for Sustainable Brain–Computer Interfaces

Design of an fNIRS-EEG hybrid terminal for wearable BCI systems.

The importance of brain-computer interfaces (BCI) is increasing, and various methods have been developed. Among the developed BCI methods, functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) are favored due to their non-invasive feature and compact device sizes. EEG monitors the electrical potentials generated by the activation of neurons, and fNIRS monitors the blood flow also generated by neurons, resulting in signals with different properties between the two methods. As the two BCI methods greatly differ in the characteristics of the acquired neural activity signals, for cases of estimating the intention or thought of a subject by BCI, it has been proven that further accurate information may be extracted by utilizing both methods simultaneously. Both systems are powered by electricity, and as EEG systems are greatly sensitive to electrical noises, application of two separate fNIRS and EEG systems together may result in electrical interference as the systems are required to be in contact with the skin and stray currents from the fNIRS system may flow along the surface of the skin into the EEG system. This research proposes a wearable fNIRS-EEG hybrid BCI system, where a single terminal is capable of operating both as a continuous wave fNIRS emitter and as a detector, and also as an EEG electrode. The system has been designed such that the fNIRS and EEG components are electrically separated to avoid electrical interference between each other. It is expected that by utilizing the developed fNIRS-EEG hybrid terminals, the development of BCI analysis may be further accelerated in various fields.

Revolutionizing Human Augmentation: The Future of Neuroprosthetics and Brain-Computer Interface Technologies

The future of neuroprosthesis and brain-computer interface (BCI) technologies is poised to transform medical treatments, cognitive development, and human-machine interaction. Neuroprosthetics, which integrate artificial devices with the nervous system to restore or enhance sensory and motor functions, have already improved the lives of people with disabilities. Advances in materials science and neural interfacing are taking these technologies even further, enabling more seamless integration and better control of prosthetic limbs and sensory devices. As these technologies improve, they may soon be able to restore complex functions such as speech, vision, and memory. At the same time, BCIs go beyond traditional applications for neurological disorders, opening up possibilities for cognitive enhancement, neural data transfer, and immersive virtual experiences. With advances in machine learning and neuroimaging, BCIs can enhance cognitive abilities, treat neurological diseases, and redefine human-computer interaction. However, these technologies also raise ethical, social, and security concerns. The risk of misuse, such as unauthorized access to neural data, and the potential creation of cognitive hierarchies emphasize the need for robust regulation. Ensuring accessibility and equity is also essential to ensure that the benefits of these advances are shared equitably. The development of neuroprostheses and BCIs has had a positive impact.

Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks

In brain-computer interface (BCI), building accurate electroencephalogram (EEG) classifiers for specific mental tasks is critical for BCI performance. The classifiers are developed by machine learning (ML) and deep learning (DL) techniques, requiring a large dataset for training to build reliable and accurate models. However, collecting large enough EEG datasets is difficult due to intra-/inter-subject variabilities and experimental costs. This leads to the data scarcity problem, which causes overfitting issues to training samples, resulting in reducing generalization performance. To solve the EEG data scarcity problem and improve the performance of the EEG classifiers, we propose a novel EEG data augmentation (DA) framework using conditional generative adversarial networks (cGANs). An experimental study is implemented with two public EEG datasets, including motor imagery (MI) tasks (BCI competition IV IIa and III IVa), to validate the effectiveness of the proposed EEG DA method for the EEG classifiers. To evaluate the proposed cGAN-based DA method, we tested eight EEG classifiers for the experiment, including traditional MLs and state-of-the-art DLs with three existing EEG DA methods. Experimental results showed that most DA methods with proper DA proportion in the training dataset had higher classification performances than without DA. Moreover, applying the proposed DA method showed superior classification performance improvement than the other DA methods. This shows that the proposed method is a promising EEG DA method for enhancing the performances of the EEG classifiers in MI-based BCIs.

NeuroRoots, a bio-inspired, seamless brain machine interface for long-term recording in delicate brain regions.

Scalable electronic brain implants with long-term stability and low biological perturbation are crucial technologies for high-quality brain-machine interfaces that can seamlessly access delicate and hard-to-reach regions of the brain. Here, we created "NeuroRoots," a biomimetic multi-channel implant with similar dimensions (7 μm wide and 1.5 μm thick), mechanical compliance, and spatial distribution as axons in the brain. Unlike planar shank implants, these devices consist of a number of individual electrode "roots," each tendril independent from the other. A simple microscale delivery approach based on commercially available apparatus minimally perturbs existing neural architectures during surgery. NeuroRoots enables high density single unit recording from the cerebellum in vitro and in vivo. NeuroRoots also reliably recorded action potentials in various brain regions for at least 7 weeks during behavioral experiments in freely-moving rats, without adjustment of electrode position. This minimally invasive axon-like implant design is an important step toward improving the integration and stability of brain-machine interfacing.

Benchmarking brain–computer interface algorithms: Riemannian approaches vs convolutional neural networks

Objective.To date, a comprehensive comparison of Riemannian decoding methods with deep convolutional neural networks for EEG-based brain-computer interfaces remains absent from published work. We address this research gap by using MOABB, The Mother Of All BCI Benchmarks, to compare novel convolutional neural networks to state-of-the-art Riemannian approaches across a broad range of EEG datasets, including motor imagery, P300, and steady-state visual evoked potentials paradigms.Approach.We systematically evaluated the performance of convolutional neural networks, specifically EEGNet, shallow ConvNet, and deep ConvNet, against well-established Riemannian decoding methods using MOABB processing pipelines. This evaluation included within-session, cross-session, and cross-subject methods, to provide a practical analysis of model effectiveness and to find an overall solution that performs well across different experimental settings.Main results.We find no significant differences in decoding performance between convolutional neural networks and Riemannian methods for within-session, cross-session, and cross-subject analyses.Significance.The results show that, when using traditional Brain-Computer Interface paradigms, the choice between CNNs and Riemannian methods may not heavily impact decoding performances in many experimental settings. These findings provide researchers with flexibility in choosing decoding approaches based on factors such as ease of implementation, computational efficiency or individual preferences.

Riemannian Locality Preserving Method for Transfer Learning With Applications on Brain-Computer Interface.

Brain-computer interfaces (BCIs) have been widely focused and extensively studied in recent years for their huge prospect of medical rehabilitation and commercial applications. Transfer learning exploits the information in the source domain and applies in another different but related domain (target domain), and is therefore introduced into the BCIs to figure out the inter-subject variances of electroencephalography (EEG) signals. In this article, a novel transfer learning method is proposed to preserve the Riemannian locality of data structure in both the source and target domains and simultaneously realize the joint distribution adaptation of both domains to enhance the effectiveness of transfer learning. Specifically, a Riemannian graph is first defined and constructed based on the Riemannian distance to represent the Riemannian geometry information. To simultaneously align the marginal and conditional distribution of source and target domains and preserve the Riemannian locality of data structure in both domains, the Riemannian graph is embedded in the joint distribution adaptation (JDA) framework and forms the proposed Riemannian locality preserving-based transfer learning (RLPTL). To validate the effect of the proposed method, it is compared with several existing methods on two open motor imagery datasets, and both multi-source domains (MSD) and single-source domains (SSD) experiments are considered. Experimental results show that the proposed method achieves the highest accuracies in MSD and SSD experiments on three datasets and outperforms eight baseline methods, which demonstrates that the proposed method creates a feasible and efficient way to realize transfer learning.