Editorial: Breakthrough BCI Applications in Medicine

Advanced Electrode Technologies for Noninvasive Brain-Computer Interfaces

Brain-computer interfaces (BCIs) have garnered significant attention in recent years due to their potential applications in medical, assistive, and communication technologies. Building on this, noninvasive BCIs stand out as they provide a safe and user-friendly method for interacting with the human brain. In this work, we provide a comprehensive overview of the latest developments and advancements in material, design, and application of noninvasive BCIs electrode technology. We also explore the challenges and limitations currently faced by noninvasive BCI electrode technology and sketch out the technological roadmap from three dimensions: Materials and Design; Performances; Mode and Function. We aim to unite research efforts within the field of noninvasive BCI electrode technology, focusing on the consolidation of shared goals and fostering integrated development strategies among a diverse array of multidisciplinary researchers.

Generative Perturbation Network for Universal Adversarial Attacks on Brain-Computer Interfaces

Deep neural networks (DNNs) have successfully classified EEG-based brain-computer interface (BCI) systems. However, recent studies have found that well-designed input samples, known as adversarial examples, can easily fool well-performed deep neural networks model with minor perturbations undetectable by a human. This paper proposes an efficient generative model named generative perturbation network (GPN), which can generate universal adversarial examples with the same architecture for non-targeted and targeted attacks. Furthermore, the proposed model can be efficiently extended to conditionally or simultaneously generate perturbations for various targets and victim models. Our experimental evaluation demonstrates that perturbations generated by the proposed model outperform previous approaches for crafting signal-agnostic perturbations. We demonstrate that the extended network for signal-specific methods also significantly reduces generation time while performing similarly. The transferability across classification networks of the proposed method is superior to the other methods, which shows our perturbations' high level of generality.

Long-term functional outcomes improved with deep brain stimulation in patients with disorders of consciousness

BACKGROUND

Deep brain stimulation (DBS) has been preliminarily applied to treat patients with disorders of consciousness (DoCs). The study aimed to determine whether DBS was effective for treating patients with DoC and identify factors related to patients' outcomes.

METHODS

Data from 365 patients with DoCs who were consecutively admitted from 15 July 2011 to 31 December 2021 were retrospectively analysed. Multivariate regression and subgroup analysis were performed to adjust for potential confounders. The primary outcome was improvement in consciousness at 1 year.

RESULTS

An overall improvement in consciousness at 1 year was achieved in 32.4% (12/37) of the DBS group compared with 4.3% (14/328) of the conservative group. After full adjustment, DBS significantly improved consciousness at 1 year (adjusted OR 11.90, 95% CI 3.65-38.46, p<0.001). There was a significant treatment×follow up interaction (H=14.99, p<0.001). DBS had significantly better effects in patients with minimally conscious state (MCS) compared with patients with vegetative state/unresponsive wakefulness syndrome (p for interaction <0.001). A nomogram based on age, state of consciousness, pathogeny and duration of DoCs indicated excellent predictive performance (c-index=0.882).

CONCLUSIONS

DBS was associated with better outcomes in patients with DoC, and the effect was likely to be significantly greater in patients with MCS. DBS should be cautiously evaluated by nomogram preoperatively, and randomised controlled trials are still needed.

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI systems have emerged as non-invasive techniques for reading out signals from the brain and interpreting them into commands to help those people to perform diverse limb motor tasks. This paper proposes an innovative and improved machine learning-based BCI system that analyzes EEG signals obtained from motor imagery to distinguish among various limb motor tasks based on BCI competition III dataset IVa. The proposed framework pipeline for EEG signal processing performs the following major steps. The first step uses a meta-heuristic optimization technique, called the whale optimization algorithm (WOA), to select the optimal features for discriminating between neural activity patterns. The pipeline then uses machine learning models such as LDA, k-NN, DT, RF, and LR to analyze the chosen features to enhance the precision of EEG signal analysis. The proposed BCI system, which merges the WOA as a feature selection method and the optimized k-NN classification model, demonstrated an overall accuracy of 98.6%, outperforming other machine learning models and previous techniques on the BCI competition III dataset IVa. Additionally, the EEG feature contribution in the ML classification model is reported using Explainable AI (XAI) tools, which provide insights into the individual contributions of the features in the predictions made by the model. By incorporating XAI techniques, the results of this study offer greater transparency and understanding of the relationship between the EEG features and the model's predictions. The proposed method shows potential levels for better use in controlling diverse limb motor tasks to help people with limb impairments and support them while enhancing their quality of life.

Long-term functional prognosis and related factors of spinal cord stimulation in patients with disorders of consciousness

INTRODUCTION

The treatment of patients with disorders of consciousness (DoC) remains a challenging issue, and spinal cord stimulation (SCS) has been reported to be a promising treatment for DoC in some studies.

AIMS

This study explores the efficiency of SCS in treating patients with DoC at different consciousness levels, including the vegetative state/unresponsive wakefulness syndrome (VS/UWS) and the minimally conscious state (MCS) and summarizes and analyzes the long-term effect and related factors of SCS in patients with DoC.

RESULTS

An overall positive outcome was reached in 35 of 110 patients (31.8%). Among patients with positive outcomes, the MCS group improved 45.53% more than VS/UWS group, and this difference was statistically significant. In terms of the recommendation standard, positive outcomes occurred in 33 patients (94.3%) in the highly recommended group and 2 patients (5.7%) in the weakly recommended group (p < 0.001). After adjustment for potential covariables, young age (age ≤ 19 years old) (p = 0.045) and MCS (p < 0.001) were significantly correlated with positive outcome. A nomogram based on age, state of consciousness, and pathogeny showed good predictive performance, with a c-index of 0.794. The Hosmer-Lemeshow goodness-of-fit test showed that the model was well calibrated (χ²  = 3.846, p = 0.871).

CONCLUSIONS

SCS is one of the most feasible treatments for patients with DoC, especially for patients with MCS. Younger age is significantly associated with better outcomes and could therefore serve as a basis for preoperative screening. However, more evidence-based randomized controlled trials are needed to confirm the efficacy of the treatment.

[Applications, industrial transformation and commercial value of brain-computer interface technology]

Brain-computer interface (BCI) is a revolutionary human-computer interaction technology, which includes both BCI that can output instructions directly from the brain to external devices or machines without relying on the peripheral nerve and muscle system, and BCI that bypasses the peripheral nerve and muscle system and inputs electrical, magnetic, acoustic and optical stimuli or neural feedback directly to the brain from external devices or machines. With the development of BCI technology, it has potential application not only in medical field, but also in non-medical fields, such as education, military, finance, entertainment, smart home and so on. At present, there is little literature on the relevant application of BCI technology, the current situation of BCI industrialization at home and abroad and its commercial value. Therefore, this paper expounds and discusses the above contents, which are expected to provide valuable information for the public and organizations, BCI researchers, BCI industry translators and salespeople, and improve the cognitive level of BCI technology, further promote the application and industrial transformation of BCI technology and enhance the commercial value of BCI, so as to serve mankind better.