Beyond Technologies of Electroencephalography-Based Brain-Computer Interfaces: A Systematic Review From Commercial and Ethical Aspects

Quantitative Electroencephalography for Predication of Neurological Dysfunction in Type A Aortic Dissection: A Prospective Observational Study

BACKGROUND

Type A aortic dissection presents challenges with postoperative cerebral complications, and this study evaluates the predictive value of quantitative electroencephalography for perioperative brain function prognosis.

METHODS AND RESULTS

Amplitude-integrated electroencephalography (aEEG) processes raw signals through filtering, amplitude integration, and time compression, displaying the data in a semilogarithmic format. Using this method, postoperative relative band power (post-RBP) α% and dynamic aEEG (ΔaEEG) grade were significantly associated with neurological dysfunction in univariate and multivariable analyses, with area under the receiver operating characteristic curve of 0.876 (95% CI, 0.825-0.926) for the combined model. Postoperative relative band power α% and ΔaEEG were significantly associated with adverse outcomes, with area under the receiver operating characteristic curve of 0.903 (95% CI, 0.835-0.971) for the combined model. Postoperative relative band power α% and ΔaEEG were significantly associated with transient neurological dysfunction and stroke, with areas under the receiver operating characteristic curve of 0.818 (95% CI, 0.760-0.876) and 0.868 (95% CI, 0.810-0.926) for transient neurological dysfunction, and 0.815 (95% CI, 0.743-0.886) and 0.831 (95% CI, 0.746-0.916) for stroke. Among 56 patients, the Alberta Stroke Program Early Computed Tomography score was superior to ΔaEEG in predicting neurological outcomes (area under the receiver operating characteristic curve of 0.872 versus 0.708 [95% CI, 0.633-0.783]; P<0.05).

CONCLUSIONS

Perioperative quantitative electroencephalography monitoring offers valuable insights into brain function changes in patients with type A aortic dissection. ∆aEEG grades can aid in early detection of adverse outcomes, while postoperative relative band power and ∆aEEG grades predict transient neurological dysfunction. Quantitative electroencephalography can assist cardiac surgeons in assessing brain function and improving outcomes in patients with type A aortic dissection.

REGISTRATION

URL: https://www.chictr.org.cn; Unique identifier: ChiCTR2200055980.

Trait impulsivity influences behavioural and physiological responses to threat in a virtual environment

Trait impulsivity represents a tendency to take action without forethought or consideration of consequences. This trait is multifaceted and can be decomposed into attentional, motor and non-planning subtypes of impulsivity. The purpose of the current study was to investigate how subtypes of trait impulsivity responded to different degrees of threat within room-scale virtual reality (VR) with respect to behaviour and level of physiological activation. Thirty-four participants were required to negotiate a virtual environment (VE) where they walked at height with the continuous threat of a virtual 'fall.' Behavioural measures related to the speed of movement, interaction frequency and risk were collected. Participants also wore ambulatory sensors to collect data from electrocardiogram (ECG) and electrodermal activity (EDA). Our results indicated that participants who scored highly on non-planning impulsivity exhibited riskier behaviour and higher skin conductance level (SCL). Participants with higher motor impulsivity interacted with more objects in the VE when threat was high, they also exhibited contradictory indicators of physiological activation. Attentional impulsivity was associated with a greater number of falls across the VE. The results demonstrate that subtypes of trait impulsivity respond to threats via different patterns of behaviour and levels of physiological activation, reinforcing the multifaceted nature of the trait.

Identifying Thematics in a Brain-Computer Interface Research

This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020-2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application.

Working with robotic process automation: User experience after 18 months of adoption

This paper reports a study of User Experience (UX) with Robotic Process Automation (RPA), in the perspective of workers of EdP Brazil, a large electric utility company that operates in Brazil. RPA are software solutions for automating business processes that find increased interest of companies because they are inserted in workgroups as a co-worker, emulating human workers operating on GUI interfaces. Although the technology promises to drive a new wave of productivity in service companies, its impact on co-workers' experience is still unexplored. Based on projective interviews using the AXE (Anticipated eXperience Evaluation) protocol, after the first 18 months of RPA operation, the analysis of workers' collaboration with the robots has evidenced multiple facets of UX, technology acceptance and innovation adoption. For this case, RPA has provided an overall positive user experience mainly due to the perceived utility of the spared time, the upgrade in career opportunities and the pride for actively participating in the innovation adoption. Negative experience comes mainly from the lack of visibility that hinders robot management for efficiency and improvement. The methodology used in the study was successful in capturing the multifaceted workers' experience and is potentially useful to support user research in new expansion RPA projects.

Evaluation of Machine Learning Algorithms for Classification of EEG Signals

In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices.