Performance enhancement of steady-state visual evoked field-based brain-computer interfaces using spatial distribution of synchronization index in MEG channel space.

The development of helmet-type magnetoencephalography (MEG) systems that do not require liquid helium (e.g., OPM-MEG) has sparked growing interest in steady-state visual evoked field (SSVEF)-based brain-computer interfaces (BCIs).

Performance Enhancement of Steady-State Visual Evoked Field-Based Brain-Computer Interfaces Incorporating MEG Source Imaging.

Recent advancements in helmet-type magneto-encephalography (MEG) systems that operate without liquid helium, such as optically pumped magnetometer (OPM)-based MEG, have increased interest in MEG-based brain-computer interfaces (BCIs). Among various BCI paradigms, steady-state visual evoked field (SSVEF)-based BCIs have been actively studied owing to their high information transfer rate (ITR) and low demand for calibration sessions. Although MEG provides excellent spatial resolution and whole-head coverage, conventional algorithms such as the filter bank-driven multivariate synchronization index (FBMSI) do not fully exploit these advantages.

Facial electromyogram-based emotion recognition for virtual reality applications using machine learning classifiers trained on posed expressions.

Recognition of human emotions holds great potential for various daily-life applications. With the increasing interest in virtual reality (VR) technologies, numerous studies have proposed new approaches to integrating emotion recognition into VR environments. However, despite recent advancements, camera-based emotion-recognition technology faces critical limitations due to the physical obstruction caused by head-mounted displays (HMDs).

Predicting antidepressant responsiveness in major depressive disorder patients via electroencephalography gamma-band dynamic functional connectivity in response to salient auditory stimuli.

Background: Heterogeneous pathophysiological characteristics in patients with major depressive disorder (MDD) lead to individually differentiated sensitivities to antidepressants. Based on the hypothesis that gamma-band dynamic fluctuations in cortical functional connectivity (FC) in response to salient stimuli are linked to pathophysiological characteristics, we conducted a classification analysis for antidepressant responsiveness prediction.

Methods: Biosignals and psychological measures were acquired from 47 patients with MDD prior to treatment.

Domain-generalized Deep Learning for Improved Subject-independent Emotion Recognition Based on Electroencephalography.

Electroencephalography (EEG) provides high temporal resolution and noninvasiveness for a range of practical applications, including emotion recognition. However, inherent variability across subjects poses significant challenges to model generalizability. In this study, we systematically evaluated twelve approaches by combining four domain generalization (DG) techniques, Deep CORAL, GroupDRO, VREx, and DANN, with three representative deep learning architectures (ShallowFBCSPNet, EEGNet, and TSception) to enable improved subject-independent EEG-based emotion recognition.

Functional Near-Infrared Spectroscopy-Based Computer-Aided Diagnosis of Major Depressive Disorder Using Convolutional Neural Network with a New Channel Embedding Layer Considering Inter-Hemispheric Asymmetry in Prefrontal Hemodynamic Responses.

Background: Functional near-infrared spectroscopy (fNIRS) is being extensively explored as a potential primary screening tool for major depressive disorder (MDD) because of its portability, cost-effectiveness, and low susceptibility to motion artifacts. However, the fNIRS-based computer-aided diagnosis (CAD) of MDD using deep learning methods has rarely been studied. In this study, we propose a novel deep learning framework based on a convolutional neural network (CNN) for the fNIRS-based CAD of MDD with high accuracy.

Motion artifact-controlled micro-brain sensors between hair follicles for persistent augmented reality brain-computer interfaces.

Modern brain-computer interfaces (BCI), utilizing electroencephalograms for bidirectional human-machine communication, face significant limitations from movement-vulnerable rigid sensors, inconsistent skin-electrode impedance, and bulky electronics, diminishing the system's continuous use and portability. Here, we introduce motion artifact-controlled micro-brain sensors between hair strands, enabling ultralow impedance density on skin contact for long-term usable, persistent BCI with augmented reality (AR). An array of low-profile microstructured electrodes with a highly conductive polymer is seamlessly inserted into the space between hair follicles, offering high-fidelity neural signal capture for up to 12 h while maintaining the lowest contact impedance density (0.

Performance Enhancement of an SSVEP-Based Brain-Computer Interface in Augmented Reality through Adaptive Color Adjustment of Visual Stimuli for Optimal Background Contrast.

The aim of this study is to develop a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system with enhanced performance in an augmented reality (AR) environment by dynamically adjusting colors of visual stimuli to contrast with the background seen through the transparent display. Our proposed method extracts the average color value from the area surrounding the visual stimulus location. It then calculates the contrast value using the HSV color model and applies this to the stimulus color.

Association between the functional brain network and antidepressant responsiveness in patients with major depressive disorders: a resting-state EEG study.

Background: Recent neuroimaging studies have demonstrated that the heterogeneous antidepressant responsiveness in patients with major depressive disorder (MDD) is associated with diverse resting-state functional brain network (rsFBN) topology; however, only limited studies have explored the rsFBN using electroencephalography (EEG). In this study, we aimed to identify EEG-derived rsFBN-based biomarkers to predict pharmacotherapeutic responsiveness.

Methods: The resting-state EEG signals were acquired for demography-matched three groups: 98 patients with treatment-refractory MDD (trMDD), 269 those with good-responding MDD (grMDD), and 131 healthy controls (HCs).

New user authentication method based on eye-writing patterns identified from electrooculography for virtual reality applications.

Demand for user authentication in virtual reality (VR) applications is increasing such as in-app payments, password manager, and access to private data. Traditionally, hand controllers have been widely used for the user authentication in VR environment, with which the users can typewrite a password or draw a pre-registered pattern; however, the conventional approaches are generally inconvenient and time-consuming. In this study, we proposed a new user authentication method based on eye-writing patterns identified using electrooculogram (EOG) recorded from four locations around the eyes in contact with the face-pad of a VR headset.

Efficacy of brain-computer interface training with motor imagery-contingent feedback in improving upper limb function and neuroplasticity among persons with chronic stroke: a double-blinded, parallel-group, randomized controlled trial.

Background: Brain-computer interface (BCI) technology can enhance neural plasticity and motor recovery in persons with stroke. However, the effects of BCI training with motor imagery (MI)-contingent feedback versus MI-independent feedback remain unclear. This study aimed to investigate whether the contingent connection between MI-induced brain activity and feedback influences functional and neural plasticity outcomes.

Prediction of pharmacological treatment efficacy using electroencephalography-based salience network in patients with major depressive disorder.

Introduction: Recent resting-state electroencephalogram (EEG) studies have consistently reported an association between aberrant functional brain networks (FBNs) and treatment-resistant traits in patients with major depressive disorder (MDD). However, little is known about the changes in FBNs in response to external stimuli in these patients. This study investigates whether changes in the salience network (SN) could predict responsiveness to pharmacological treatment in resting-state and external stimuli conditions.

Machine-Learning-Based Prediction of Photobiomodulation Effects on Older Adults With Cognitive Decline Using Functional Near-Infrared Spectroscopy.

Transcranial photobiomodulation (tPBM) has been widely studied for its potential to enhance cognitive functions of the elderly. However, its efficacy varies, with some individuals exhibiting no significant response to the treatment. Considering these inconsistencies, we introduce a machine learning approach aimed at distinguishing between individuals that respond and do not respond to tPBM treatment based on functional near-infrared spectroscopy (fNIRS) acquired before the treatment.

Speech synthesis from three-axis accelerometer signals using conformer-based deep neural network.

Silent speech interfaces (SSIs) have emerged as innovative non-acoustic communication methods, and our previous study demonstrated the significant potential of three-axis accelerometer-based SSIs to identify silently spoken words with high classification accuracy. The developed accelerometer-based SSI with only four accelerometers and a small training dataset outperformed a conventional surface electromyography (sEMG)-based SSI. In this study, motivated by the promising initial results, we investigated the feasibility of synthesizing spoken speech from three-axis accelerometer signals.

HiRENet: Novel convolutional neural network architecture using Hilbert-transformed and raw electroencephalogram (EEG) for subject-independent emotion classification.

Background And Objectives: Convolutional neural networks (CNNs) are the most widely used deep-learning framework for decoding electroencephalograms (EEGs) due to their exceptional ability to extract hierarchical features from high-dimensional EEG data. Traditionally, CNNs have primarily utilized multi-channel raw EEG data as the input tensor; however, the performance of CNN-based EEG decoding may be enhanced by incorporating phase information alongside amplitude information.

Methods: This study introduces a novel CNN architecture called the Hilbert-transformed (HT) and raw EEG network (HiRENet), which incorporates both raw and HT EEG as inputs.

Multi-physics simulations for investigating the effect of electrode conditions on transscleral ocular iontophoresis for particulate drug delivery into ocular tissues.

Purpose: Transscleral ocular iontophoresis has been proposed to deliver charged particulate drugs to ocular tissues effectively by transmitting a weak electrical current through the sclera. The electric fields formed are influenced by the electrode conditions, thus affecting the amount of particulate drugs delivered to the ocular tissues via iontophoresis. Computational simulation is widely used to simulate drug concentrations in the eye; therefore, reflecting the characteristics of the drugs in living tissues to the simulations is important for a more precise estimation of drug concentration.

In-Car Environment Control Using an SSVEP-Based Brain-Computer Interface with Visual Stimuli Presented on Head-Up Display: Performance Comparison with a Button-Press Interface.

Controlling the in-car environment, including temperature and ventilation, is necessary for a comfortable driving experience. However, it often distracts the driver's attention, potentially causing critical car accidents. In the present study, we implemented an in-car environment control system utilizing a brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP).

The Importance of Low-frequency Alpha (8-10 Hz) Waves and Default Mode Network in Behavioral Inhibition.

Objective: : Alpha wave of electroencephalography (EEG) is known to be related to behavioral inhibition. Both the alpha wave and default mode network (DMN) are predominantly activated during resting-state. To study the mechanisms of the trait inhibition, this research investigating the relations among alpha wave, DMN and behavioral inhibition in resting-state.

AR-Enabled Persistent Human-Machine Interfaces via a Scalable Soft Electrode Array.

Augmented reality (AR) is a computer graphics technique that creates a seamless interface between the real and virtual worlds. AR usage rapidly spreads across diverse areas, such as healthcare, education, and entertainment. Despite its immense potential, AR interface controls rely on an external joystick, a smartphone, or a fixed camera system susceptible to lighting.

Effect of temporal interference electrical stimulation on phasic dopamine release in the striatum.

Background: Temporal interference stimulation (TIS) is a neuromodulation technique that could stimulate deep brain regions by inducing interfering electrical signals based on high-frequency electrical stimulations of multiple electrode pairs from outside the brain. Despite numerous TIS studies, however, there has been limited investigation into the neurochemical effects of TIS.

Objective: We performed two experiments to investigate the effect of TIS on the medial forebrain bundle (MFB)-evoked phasic dopamine (DA) response.

Enhancement of dynamic visual acuity using transcranial alternating current stimulation with gamma burst entrained on alpha wave troughs.

Background: Cross-frequency phase-amplitude coupling (PAC) of cortical oscillations is observed within and across cortical regions during higher-order cognitive processes. Particularly, the PAC of alpha and gamma waves in the occipital cortex is closely associated with visual perception. In theory, gamma oscillation is a neuronal representation of visual stimuli, which drives the duty cycle of visual perception together with alpha oscillation.

Transcranial alternating current stimulation over multiple brain areas with non-zero phase delays other than 180 degrees modulates visuospatial working memory performance.

While zero-phase lag synchronization between multiple brain regions has been widely observed, relatively recent reports indicate that systematic phase delays between cortical regions reflect the direction of communications between cortical regions. For example, it has been suggested that a non-zero phase delay of electroencephalography (EEG) signals at the gamma frequency band between the bilateral parietal areas may reflect the direction of communication between these areas. We hypothesized that the direction of communication between distant brain areas might be modulated by multi-site transcranial alternating current stimulation (tACS) with specific phase delays other than 0° and 180°.

Facial electromyogram-based facial gesture recognition for hands-free control of an AR/VR environment: optimal gesture set selection and validation of feasibility as an assistive technology.

Unlabelled: The rapid expansion of virtual reality (VR) and augmented reality (AR) into various applications has increased the demand for hands-free input interfaces when traditional control methods are inapplicable (e.g., for paralyzed individuals who cannot move their hands).