Journal of neural engineering最新文献

Objective. Vision restoration approaches, such as prosthetics and optogenetics, provide visual perception to blind individuals in clinical settings. Yet their effectiveness in daily life remains a challenge. Stereotyped quantitative tests used in clinical trials often fail to translate into practical, everyday applications. On the one hand, assessing real-life benefits during clinical trials is complicated by environmental complexity, reproducibility issues, and safety concerns. On the other hand, predicting behavioral benefits of restorative therapies in naturalistic environments may be a crucial step before starting clinical trials to minimize patient discomfort and unmet expectations.Approach. To address this, we leverage advancements in virtual reality technology to conduct a fully immersive and ecologically valid task within a physical artificial street environment. As a case study, we assess the impact of the visual field size in simulated artificial vision for common outdoor tasks.Main results. We show that a wide visual angle (45°) enhances participants' ability to navigate and solve tasks more effectively, safely, and efficiently. Moreover, it promotes their learning and generalization capability. Concurrently, it changes the visual exploration behavior and facilitates a more accurate mental representation of the environment. Further increasing the visual angle beyond this value does not yield significant additional improvements in most metrics.Significance. We present a methodology combining augmented reality with a naturalistic environment, enabling participants to perceive the world as patients with retinal implants would and to interact physically with it. Combining augmented reality in naturalistic environments is a valuable framework for low vision and vision restoration research.

Reactive Brain-Computer Interfaces (rBCIs) typically rely on repetitive visual stimuli, which can strain the eyes and cause attentional distraction. To address these challenges, we propose a novel approach rooted in visual neuroscience to design visual Stimuli for Augmented Response (StAR). The StAR stimuli consist of small, randomly-oriented Gabor or Ricker patches that optimize foveal neural response while reducing peripheral distraction. Methods: In a factorial design study, 24 participants equipped with an 8-dry electrode EEG system focused on series of target flickers presented under three formats: traditional Plain flickers, Gabor-based, or Ricker-based flickers. These flickers were part of a five-class Code Visually Evoked Potentials (c-VEP) paradigm featuring low-frequency, short, and aperiodic visual flashes. Results: Subjective ratings revealed that Gabor and Ricker stimuli were visually comfortable and nearly invisible in peripheral vision compared to plain flickers. Moreover, Gabor and Ricker-based textures achieved higher accuracy (93.6% and 96.3%, respectively) with only 88 seconds of calibration data, compared to plain flickers (65.6%). A follow-up online implementation of this experiment was conducted to validate our findings in naturalistic operations. During this trial, remarkable accuracies of 97.5% in a cued task and 94.3% in an asynchronous digicode task were achieved, with a mean decoding time as low as 1.68 seconds. Conclusion: This work demonstrates the potential to expand BCI applications beyond the lab by integrating visually unobtrusive systems with gel-free, low-density EEG technology, thereby making BCIs more accessible and efficient. The datasets, algorithms, and BCI implementations are shared through open-access repositories.

Objective. Combining magnetic resonance imaging (MRI) and electroencephalography (EEG) provides a powerful tool for investigating brain function at varying spatial and temporal scales. Simultaneous acquisition of both modalities can provide unique information that a single modality alone cannot reveal. However, current simultaneous EEG-fMRI studies are limited to a small set of MRI sequences due to the image quality and safety limitations of commercially available MR-conditional EEG nets. We tested whether the Inknet2, a high-resistance polymer thick film based EEG net that uses conductive ink, could enable the acquisition of a variety of MR image modalities with minimal artifacts by reducing the radiofrequency-shielding caused by traditional MR-conditional nets.Approach. We first performed simulations to model the effect of the EEG nets on the magnetic field and image quality. We then performed phantom scans to test image quality with a conventional copper EEG net, with the new Inknet2, and without any EEG net. Finally, we scanned five human subjects at 3 Tesla (3 T) and three human subjects at 7 Tesla (7 T) with and without the Inknet2 to assess structural and functional MRI image quality.Main results. Across these simulations, phantom scans, and human studies, the Inknet2 induced fewer artifacts than the conventional net and produced image quality similar to scans with no net present.Significance. Our results demonstrate that high-quality structural and functional multimodal imaging across a variety of MRI pulse sequences at both 3 T and 7 T is achievable with an EEG net made with conductive ink and polymer thick film technology.

Objective.The quality of electroencephalogram (EEG) signals directly impacts the performance of brain-computer interface (BCI) tasks. Many methods have been proposed to eliminate noise from EEG signals, but most of these methods focus solely on signal denoising itself, disregarding the impact on subsequent tasks, which deviates from the original intention of EEG denoising. The main objective of this study is to optimize EEG denoising models with a purpose of improving the performance of BCI tasks.Approach.To this end, we proposed an innovative task-oriented EEG denoising generative adversarial network (TOED-GAN) method. This network utilizes the generator of GAN to decompose and reconstruct clean signals from the raw EEG signals, and the discriminator to learn to distinguish the generated signals from the true clean signals, resulting in a remarkable increase of the signal-to-noise ratio by simultaneously enhancing task-related components and removing task-irrelevant noise from the original contaminated signals.Main results.We evaluated the performance of the model on a public dataset and a self-collected dataset respectively, with canonical correlation analysis classification tasks of the steady-state visual evoked potential (SSVEP) based BCI. Experimental results demonstrate that TOED-GAN exhibits excellent performance in removing EEG noise and improving performance for SSVEP-BCI, with accuracy improvement rates reaching 18.47% and 21.33% in contrast to the baseline methods of convolutional neural networks, respectively.Significance.This work proves that the proposed TOED-GAN, as an EEG denoising method tailored for SSVEP tasks, contributes to enhancing the performance of BCIs in practical application scenarios.

Objective.While Electroencephalography (EEG)-based driver fatigue state classification models have demonstrated effectiveness, their real-world application remains uncertain. The substantial variability in EEG signals among individuals poses a challenge in developing a universal model, often necessitating retraining with the introduction of new subjects. However, obtaining sufficient data for retraining, especially fatigue data for new subjects, is impractical in real-world settings.Approach.In response to these challenges, this paper introduces a hybrid solution for fatigue detection that combines clustering with classification. Unsupervised clustering groups subjects based on their EEG functional connectivity (FC) in an alert state, and classification models are subsequently applied to each cluster for predicting alert and fatigue states.Main results. Results indicate that classification on clusters achieves higher accuracy than scenarios without clustering, suggesting successful grouping of subjects with similar FC characteristics through clustering, thereby enhancing the classification process.Significance.Furthermore, the proposed hybrid method ensures a practical and realistic retraining process, improving the adaptability and effectiveness of the fatigue detection system in real-world applications.

Objective: Non-invasive brain stimulation (NIBS) methodologies, such as transcranial electric (tES) are increasingly employed for therapeutic, diagnostic, or research purposes. The concurrent presence of active/passive implants can pose safety risks, affect the NIBS delivery, or generate confounding signals. A systematic investigation is required to understand the interaction mechanisms, quantify exposure, assess risks, and establish guidance for NIBS applications.

Approach: We used measurements, simplified generic, and detailed anatomical modeling to: (i) systematically analyze exposure conditions with passive and active implants, considering local field enhancement, exposure dosimetry, tissue heating and neuromodulation, capacitive lead current injection, low-impedance pathways between electrode contacts, and insulation damage; (ii) identify risk metrics and efficient prediction strategies; (iii) quantify these metrics in relevant exposure cases and (iv) identify worst case conditions. Various aspects including implant design, positioning, scar tissue formation, anisotropy, and frequency were investigated.

Results: At typical tES frequencies, local enhancement of dosimetric exposure quantities can reach up to one order of magnitude for deep brain stimulation (DBS) and stereoelectroencephalography implants (more for elongated passive implants), potentially resulting in unwanted neuromodulation that can confound results but is still 2-3 orders of magnitude lower than active DBS. Under worst-case conditions, capacitive current injection in the active implants' lead can produce local exposures of similar magnitude as the passive field enhancement, while capacitive pathways between contacts are negligible. Above 10 kHz, applied current magnitudes increase, necessitating consideration of tissue heating. Furthermore, capacitive effects become more prominent, leading to current injection that can reach DBS-like levels. Adverse effects from abandoned/damaged leads in direct electrode vicinity cannot be excluded.

Significance: Safety related concerns of tES application in the presence of implants are systematically identified and explored, resulting in specific and quantitative guidance and establishing basis for safety standards. Furthermore,several methods for reducing risks are suggested while acknowledging the limitations(see Sec. 4.5).

Objective: This study investigated the potential of estimating various mental workload levels during two different tasks using a commercial in-ear electroencephalography (EEG) system, the IDUN "Guardian". Approach: Participants performed versions of two classical workload tasks: an n-back task and a mental arithmetic task. Both in-ear and conventional EEG data were simultaneously collected during these tasks. In an effort to facilitate a more comprehensive comparison, the complexity of the tasks was intentionally increased beyond typical levels. Special emphasis was also placed on understanding the significance of gamma band activity in workload estimations. Therefore, each signal was analyzed across low frequency (1-35 Hz) and high frequency (1-100 Hz) ranges. Additionally, surrogate in-ear EEG measures, derived from the conventional EEG recordings, were extracted and examined. Main results: Workload estimation using in-ear EEG yielded statistically significant performance levels, surpassing chance levels with 44.1% for four classes and 68.4% for two classes in the n-back task and was better than a naive predictor for the mental arithmetic task. Conventional EEG exhibited significantly higher performance compared to in-ear EEG, achieving 80.3% and 92.9% accuracy for the respective tasks, along with lower error rates than the naive predictor. The developed surrogate measures achieved improved results, reaching accuracies of 57.5% and 85.5%, thus providing insights for enhancing future in-ear systems. Notably, most high frequency range signals outperformed their low frequency counterparts in terms of accuracy validating that high frequency gamma band features can improve workload estimation. Significance: The application of EEG-based Brain-Computer Interfaces (BCIs) beyond laboratory settings is often hindered by practical limitations. In-ear EEG systems offer a promising solution to this problem, potentially enabling everyday use. This study evaluates the performance of a commercial in-ear headset and provides guidelines for increased effectiveness.

Transcranial magnetic stimulation (TMS) modulates neuronal activity, but the efficacy of an open-loop approach is limited due to the brain state's dynamic nature. Real-time integration with electroencephalography (EEG) increases experimental reliability and offers personalized neuromodulation therapy by using immediate brain states as biomarkers. Here, we review brain state-controlled TMS-EEG studies since the first publication several years ago. A summary of experiments on the sensorimotor mu rhythm (8-13 Hz) shows increased cortical excitability due to TMS pulse at the trough and decreased excitability at the peak of the oscillation. Pre-TMS pulse mu power also affects excitability. Further, there is emerging evidence that the oscillation phase in theta and beta frequency bands modulates neural excitability. Here, we provide a guide for real-time TMS-EEG application and discuss experimental and technical considerations. We consider the effects of hardware choice, signal quality, spatial and temporal filtering, and neural characteristics of the targeted brain oscillation. Finally, we speculate on how closed-loop TMS-EEG potentially could improve the treatment of neurological and mental disorders such as depression, Alzheimer's, Parkinson's, schizophrenia, and stroke.

In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial interest.Objective: This review synthesizes empirical findings from a collection of articles using deep representation learning techniques for BCI decoding, to provide a comprehensive analysis of the current state-of-the-art.Approach: Each article was scrutinized based on three criteria: (1) the deep representation learning technique employed, (2) the underlying motivation for its utilization, and (3) the approaches adopted for characterizing the learned representations.Main results: Among the 81 articles finally reviewed in depth, our analysis reveals a predominance of 31 articles using autoencoders. We identified 13 studies employing self-supervised learning (SSL) techniques, among which ten were published in 2022 or later, attesting to the relative youth of the field. However, at the time being, none of these have led to standard foundation models that are picked up by the BCI community. Likewise, only a few studies have introspected their learned representations. We observed that the motivation in most studies for using representation learning techniques is for solving transfer learning tasks, but we also found more specific motivations such as to learn robustness or invariances, as an algorithmic bridge, or finally to uncover the structure of the data.Significance: Given the potential of foundation models to effectively tackle these challenges, we advocate for a continued dedication to the advancement of foundation models specifically designed for EEG signal decoding by using SSL techniques. We also underline the imperative of establishing specialized benchmarks and datasets to facilitate the development and continuous improvement of such foundation models.

Objective.Current intracortical brain-computer interfaces (iBCIs) rely predominantly on threshold crossings ('spikes') for decoding neural activity into a control signal for an external device. Spiking data can yield high accuracy online control during complex behaviors; however, its dependence on high-sampling-rate data collection can pose challenges. An alternative signal for iBCI decoding is the local field potential (LFP), a continuous-valued signal that can be acquired simultaneously with spiking activity. However, LFPs are seldom used alone for online iBCI control as their decoding performance has yet to achieve parity with spikes.Approach.Here, we present a strategy to improve the performance of LFP-based decoders by first training a neural dynamics model to use LFPs to reconstruct the firing rates underlying spiking data, and then decoding from the estimated rates. We test these models on previously-collected macaque data during center-out and random-target reaching tasks as well as data collected from a human iBCI participant during attempted speech.Main results.In all cases, training models from LFPs enables firing rate reconstruction with accuracy comparable to spiking-based dynamics models. In addition, LFP-based dynamics models enable decoding performance exceeding that of LFPs alone and approaching that of spiking-based models. In all applications except speech, LFP-based dynamics models also facilitate decoding accuracy exceeding that of direct decoding from spikes.Significance.Because LFP-based dynamics models operate on lower bandwidth and with lower sampling rate than spiking models, our findings indicate that iBCI devices can be designed to operate with lower power requirements than devices dependent on recorded spiking activity, without sacrificing high-accuracy decoding.