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.

Effects of looming audio on transcranial magnetic stimulation-induced phosphene perception.

Looming sounds are known to influence visual function in the brain, even as early as the primary visual cortex. However, despite evidence that looming sounds have a larger impact on cortical excitability than stationary sounds, the influence of varying looming strengths on visual ability remains unclear. Here, we aim to understand how these signals influence low-level visual function.

Spatio-Temporal Explanation of 3D-EEGNet for Motor Imagery EEG Classification Using Permutation and Saliency.

Recently, convolutional neural network (CNN)-based classification models have shown good performance for motor imagery (MI) brain-computer interfaces (BCI) using electroencephalogram (EEG) in end-to-end learning. Although a few explainable artificial intelligence (XAI) techniques have been developed, it is still challenging to interpret the CNN models for EEG-based BCI classification effectively. In this research, we propose 3D-EEGNet as a 3D CNN model to improve both the explainability and performance of MI EEG classification.

Corrigendum: Review of public motor imagery and execution datasets in brain-computer interfaces.

[This corrects the article DOI: 10.3389/fnhum.2023.

Can vibrotactile stimulation and tDCS help inefficient BCI users?

Brain-computer interface (BCI) has helped people by allowing them to control a computer or machine through brain activity without actual body movement. Despite this advantage, BCI cannot be used widely because some people cannot achieve controllable performance. To solve this problem, researchers have proposed stimulation methods to modulate relevant brain activity to improve BCI performance.

Review of public motor imagery and execution datasets in brain-computer interfaces.

The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already.

Culture and gender modulate dlPFC integration in the emotional brain: evidence from dynamic causal modeling.

Past research has recognized culture and gender variation in the experience of emotion, yet this has not been examined on a level of effective connectivity. To determine culture and gender differences in effective connectivity during emotional experiences, we applied dynamic causal modeling (DCM) to electroencephalography (EEG) measures of brain activity obtained from Chinese and American participants while they watched emotion-evoking images. Relative to US participants, Chinese participants favored a model bearing a more integrated dorsolateral prefrontal cortex (dlPFC) during fear v.

Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study.

Introduction: Alzheimer's disease (AD) affects the whole brain from the cellular level to the entire brain network structure. The causal relationship among brain regions concerning the different AD stages is not yet investigated. This study used Dynamic Causal Modeling (DCM) method to assess effective connectivity (EC) and investigate the changes that accompany AD progression.

Numerical and experimental evaluation of low-intensity transcranial focused ultrasound wave propagation using human skulls for brain neuromodulation.

Background: Low-intensity transcranial focused ultrasound (tFUS) has gained considerable attention as a promising noninvasive neuromodulatory technique for human brains. However, the complex morphology of the skull hinders scholars from precisely predicting the acoustic energy transmitted and the region of the brain impacted during the sonication. This is due to the fact that different ultrasound frequencies and skull morphology variations greatly affect wave propagation through the skull.

Low-cost brain computer interface for everyday use.

With the growth in electroencephalography (EEG) based applications the demand for affordable consumer solutions is increasing. Here we describe a compact, low-cost EEG device suitable for daily use. The data are transferred from the device to a personal server using the TCP-IP protocol, allowing for wireless operation and a decent range of motion for the user.

Emotion depends on context, culture and their interaction: evidence from effective connectivity.

Situated models of emotion hypothesize that emotions are optimized for the context at hand, but most neuroimaging approaches ignore context. For the first time, we applied Granger causality (GC) analysis to determine how an emotion is affected by a person's cultural background and situation. Electroencephalographic recordings were obtained from mainland Chinese (CHN) and US participants as they viewed and rated fearful and neutral images displaying either social or non-social contexts.

Dual Task Effects on Speed and Accuracy During Cognitive and Upper Limb Motor Tasks in Adults With Stroke Hemiparesis.

Background: Adults with stroke need to perform cognitive-motor dual tasks during their day-to-day activities. However, they face several challenges owing to their impaired motor and cognitive functions.

Objective: This case-controlled pilot study investigates the speed and accuracy tradeoffs in adults with stroke while performing cognitive-upper limb motor dual tasks.

Direct Communication Between Brains: A Systematic PRISMA Review of Brain-To-Brain Interface.

This paper aims to review the current state of brain-to-brain interface (B2BI) technology and its potential. B2BIs function via a brain-computer interface (BCI) to read a sender's brain activity and a computer-brain interface (CBI) to write a pattern to a receiving brain, transmitting information. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to systematically review current literature related to B2BI, resulting in 15 relevant publications.

Effect of Low Intensity Transcranial Ultrasound Stimulation on Neuromodulation in Animals and Humans: An Updated Systematic Review.

Although low-intensity transcranial ultrasound stimulation (LI-TUS) has received more recognition for its neuromodulation potential, there remains a crucial knowledge gap regarding the neuromodulatory effects of LI-TUS and its potential for translation as a therapeutic tool in humans. In this review, we summarized the findings reported by recently published studies regarding the effect of LI-TUS on neuromodulation in both animals and humans. We also aim to identify challenges and opportunities for the translation process.

Evaluating Effective Connectivity of Trust in Human-Automation Interaction: A Dynamic Causal Modeling (DCM) Study.

Objective: Using dynamic causal modeling (DCM), we examined how credibility and reliability affected the way brain regions exert causal influence over each other-effective connectivity (EC)-in the context of trust in automation.

Background: Multiple brain regions of the central executive network (CEN) and default mode network (DMN) have been implicated in trust judgment. However, the neural correlates of trust judgment are still relatively unexplored in terms of the directed information flow between brain regions.

A Functional Model for Unifying Brain Computer Interface Terminology.

Brain Computer Interface (BCI) technology is a critical area both for researchers and clinical practitioners. The IEEE P2731 working group is developing a comprehensive BCI lexicography and a functional model of BCI. The glossary and the functional model are inextricably intertwined.

Functional Electrical Stimulation Controlled by Motor Imagery Brain-Computer Interface for Rehabilitation.

Sensorimotor rhythm (SMR)-based brain-computer interface (BCI) controlled Functional Electrical Stimulation (FES) has gained importance in recent years for the rehabilitation of motor deficits. However, there still remain many research questions to be addressed, such as unstructured Motor Imagery (MI) training procedures; a lack of methods to classify different MI tasks in a single hand, such as grasping and opening; and difficulty in decoding voluntary MI-evoked SMRs compared to FES-driven passive-movement-evoked SMRs. To address these issues, a study that is composed of two phases was conducted to develop and validate an SMR-based BCI-FES system with 2-class MI tasks in a single hand (Phase 1), and investigate the feasibility of the system with stroke and traumatic brain injury (TBI) patients (Phase 2).

Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach.

The current research proposed and tested a structural equation model (SEM) that describes hypothesized relationships among factors affecting trust in human-robot interaction (HRI) such as trustworthiness, human-likeness, intelligence, perfect automation schema (PAS), and affect. A video stimulus depicting an autonomous guard robot interacting with humans was employed as a stimulus via Amazon's Mechanical Turk to recruit 233 participants. Human-related and robot-related metrics were found to affect trustworthiness that subsequently affected trust.

Trusting Autonomous Security Robots: The Role of Reliability and Stated Social Intent.

Objective: This research examined the effects of reliability and stated social intent on trust, trustworthiness, and one's willingness to endorse use of an autonomous security robot (ASR).

Background: Human-robot interactions in the domain of security is plausible, yet we know very little about what drives acceptance of ASRs. Past research has used static images and game-based simulations to depict the robots versus actual humans interacting with actual robots.

Neural Correlates of Workload Transition in Multitasking: An ACT-R Model of Hysteresis Effect.

This study investigated the effect of task demand transitions at multiple levels of analysis including behavioral performance, subjective rating, and brain effective connectivity, while comparing human data to Adaptive Control of Thought-Rational (ACT-R) simulated data. Three stages of task demand were designed and performed sequentially (Low-High-Low) during AF-MATB tasks, and the differences in neural connectivity during workload transition were identified. The NASA Task Load Index (NASA-TLX) and the Instantaneous Self-Assessment (ISA) were used to measure the subjective mental workload that accompanies the hysteresis effect in the task demand transitions.

Prediction of Physiological Response over Varying Forecast Lengths with a Wearable Health Monitoring Platform.

The goal of this study is to characterize the accuracy of prediction of physiological responses for varying forecast lengths using multi-modal data streams from wearable health monitoring platforms. We specifically focus on predicting breathing rate due to its significance in medical and exercise physiology research. We implement a nonlinear support vector machine regression model for accurate prediction of future values of these physiological signals with forecast windows of up to one minute long.

Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI.

Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain-computer interface (BCI) application. The effectiveness of CSP is highly affected by the frequency band and time window of EEG segments. Although numerous algorithms have been designed to optimize the spectral bands of CSP, most of them selected the time window in a heuristic way.

Immersion of virtual reality for rehabilitation - Review.

Virtual reality (VR) shows promise in the application of healthcare and because it presents patients an immersive, often entertaining, approach to accomplish the goal of improvement in performance. Eighteen studies were reviewed to understand human performance and health outcomes after utilizing VR rehabilitation systems. We aimed to understand: (1) the influence of immersion in VR performance and health outcomes; (2) the relationship between enjoyment and potential patient adherence to VR rehabilitation routine; and (3) the influence of haptic feedback on performance in VR.

Behavioral and Neural Correlates of Executive Function: Interplay between Inhibition and Updating Processes.

This study investigated the interaction between two executive function processes, inhibition and updating, through analyses of behavioral, neurophysiological, and effective connectivity metrics. Although, many studies have focused on behavioral effects of executive function processes individually, few studies have examined the dynamic causal interactions between these two functions. A total of twenty participants from a local university performed a dual task combing flanker and n-back experimental paradigms, and completed the Operation Span Task designed to measure working memory capacity.