EEG and Eye-Tracking Error-Related Responses During Predictive Text Interactions: A BCI Case Study.

Brain-computer interfaces (BCIs) employ various paradigms which afford intuitive, augmented control for users to navigate digital technologies. In this study we explore the application of these BCI concepts to predictive text systems: commonplace interactive and assistive tools with variable usage contexts and user behaviors. We conducted an experiment to analyze user neurophysiological responses under these different usage scenarios and evaluate the feasibility of a closed-loop, adaptive BCI for use with such technologies.

Construction of Covalent Organic Cages with Aggregation-Induced Emission Characteristics from Metallacages for Mimicking Light-Harvesting Antenna.

A series of covalent organic cages built from fluorophores capable of aggregation-induced emission (AIE) were elegantly prepared through the reduction of preorganized M (L ) (L ) -type metallacages, simultaneously taking advantage of the synthetic accessibility and well-defined shapes and sizes of metallacages, the good chemical stability of the covalent cages as well as the bright emission of AIE fluorophores. Moreover, the covalent cages could be further post-synthetically modified into an amide-functionalized cage with a higher quantum yield. Furthermore, these presented covalent cages proved to be good energy donors and were used to construct light-harvesting systems employing Nile Red as an energy acceptor.

Switchable metallacycles and metallacages.

Two-dimensional metallacycles and three-dimensional metallacages constructed by coordination-driven self-assembly have attracted much attention because they exhibit unique structures and properties and are highly efficient to synthesize. Introduction of switching into supramolecular chemistry systems is a popular strategy, as switching can endow systems with reversible features that are triggered by different stimuli. Through this strategy, novel switchable metallacycles and metallacages were generated, which can be reversibly switched into different stable states with distinct characteristics by external stimuli.

AIE-Active Chiral [3]Rotaxanes with Switchable Circularly Polarized Luminescence.

The construction of circularly polarized luminescence (CPL) switches with multiple switchable emission states and high dissymmetry factors (g ) has attracted increasing attention due to their broad applications in diverse fields such as the development of smart devices and sensors. Herein, a new family of AIE-active chiral [3]rotaxanes were designed and synthesized, from which a novel CPL switching system was successfully constructed. The switching process was realized through the controlled motions of the chiral pillar[5]arene macrocycles along the axle through the addition or removal of the acetate anions, which not only modulated the chirality information transfer but also tuned the aggregations of the integrated [3]rotaxanes, thus resulting in reversible transformations between two emission states with both high photoluminescence quantum yields (PLQYs) and high dissymmetry factors (g ) values.

Facilitating Calibration in High-Speed BCI Spellers via Leveraging Cross-Device Shared Latent Responses.

Objective: This paper proposes a novel device-to-device transfer-learning algorithm for reducing the calibration cost in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) speller by leveraging electroencephalographic (EEG) data previously acquired by different EEG systems.

Methods: The transferring is done by projecting the scalp-channel EEG signals onto a shared latent domain across devices. Three spatial filtering techniques, including channel averaging, canonical correlation analysis (CCA), and task-related component analysis (TRCA), were employed to extract the shared responses from different devices.

Evaluating the Performance of Non-Hair SSVEP-Based BCIs Featuring Template-Based Decoding Methods.

Our previous study has demonstrated the feasibility of employing non-hair-bearing electrodes to build a Steadystate Visual Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) system, relaxing technical barriers in preparation time and offering an ease-of-use apparatus. The signal quality of the SSVEPs and the resultant performance of the non-hair BCI, however, did not close upon those reported in the state-of-the-art BCI studies based on the electroencephalogram (EEG) measured from the occipital regions. Recently, advanced decoding algorithms such as task-related component analysis have made a breakthrough in enhancing the signal quality of the occipital SSVEPs and the performance of SSVEP-based BCIs in a well-controlled laboratory environment.

Transferring Shared Responses Across Electrode Montages for Facilitating Calibration in High-Speed Brain Spellers.

Recent studies have shown that using the user's average steady-state visual evoked responses (SSVEPs) as the template to template-matching methods could significantly improve the accuracy and speed of the SSVEP-based brain- computer interface (BCI). However, collecting the pilot data for each individual can be time-consuming. To resolve this practical issue, this study aims to explore the feasibility of leveraging pre- recorded datasets from the same users by transferring common electroencephalogram (EEG) responses across different sessions with the same or different electrode montages.

A subject-transfer framework for obviating inter- and intra-subject variability in EEG-based drowsiness detection.

Inter- and intra-subject variability pose a major challenge to decoding human brain activity in brain-computer interfaces (BCIs) based on non-invasive electroencephalogram (EEG). Conventionally, a time-consuming and laborious training procedure is performed on each new user to collect sufficient individualized data, hindering the applications of BCIs on monitoring brain states (e.g.

Toward Drowsiness Detection Using Non-hair-Bearing EEG-Based Brain-Computer Interfaces.

Drowsy driving is one of the major causes that lead to fatal accidents worldwide. For the past two decades, many studies have explored the feasibility and practicality of drowsiness detection using electroencephalogram (EEG)-based brain-computer interface (BCI) systems. However, on the pathway of transitioning laboratory-oriented BCI into real-world environments, one chief challenge is to obtain high-quality EEG with convenience and long-term wearing comfort.

Independent component analysis-based spatial filtering improves template-based SSVEP detection.

This study proposes a new algorithm to detect steady-state visual evoked potentials (SSVEPs) based on a template-matching approach combined with independent component analysis (ICA)-based spatial filtering. In recent studies, the effectiveness of the template-based SSVEP detection has been demonstrated in a high-speed brain-computer interface (BCI). Since SSVEPs can be considered as electroencephalogram (EEG) signals generated from underlying brain sources independent from other activities and artifacts, ICA has great potential to enhance the signal-to-noise ratio (SNR) of SSVEPs by separating them from artifacts.

Detecting Glaucoma With a Portable Brain-Computer Interface for Objective Assessment of Visual Function Loss.

Importance: The current assessment of visual field loss in diseases such as glaucoma is affected by the subjectivity of patient responses and the lack of portability of standard perimeters.

Objective: To describe the development and initial validation of a portable brain-computer interface (BCI) for objectively assessing visual function loss.

Design, Setting, And Participants: This case-control study involved 62 eyes of 33 patients with glaucoma and 30 eyes of 17 healthy participants.

Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis.

Objective: This study proposes and evaluates a novel data-driven spatial filtering approach for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high-speed brain-computer interface (BCI) speller.

Methods: Task-related component analysis (TRCA), which can enhance reproducibility of SSVEPs across multiple trials, was employed to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. An ensemble method was further developed to integrate TRCA filters corresponding to multiple stimulation frequencies.

Transfer learning with large-scale data in brain-computer interfaces.

Human variability in electroencephalogram (EEG) poses significant challenges for developing practical real-world applications of brain-computer interfaces (BCIs). The intuitive solution of collecting sufficient user-specific training/calibration data can be very labor-intensive and time-consuming, hindering the practicability of BCIs. To address this problem, transfer learning (TL), which leverages existing data from other sessions or subjects, has recently been adopted by the BCI community to build a BCI for a new user with limited calibration data.

Fast detection of covert visuospatial attention using hybrid N2pc and SSVEP features.

Objective: Detecting the shift of covert visuospatial attention (CVSA) is vital for gaze-independent brain-computer interfaces (BCIs), which might be the only communication approach for severely disabled patients who cannot move their eyes. Although previous studies had demonstrated that it is feasible to use CVSA-related electroencephalography (EEG) features to control a BCI system, the communication speed remains very low. This study aims to improve the speed and accuracy of CVSA detection by fusing EEG features of N2pc and steady-state visual evoked potential (SSVEP).

An Online Brain-Computer Interface Based on SSVEPs Measured From Non-Hair-Bearing Areas.

Steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has gained a lot of attention due to its robustness and high information transfer rate (ITR). However, transitioning well-controlled laboratory-oriented BCI demonstrations to real-world applications poses severe challenges for this exciting field. For instance, conducting BCI experiments usually requires skilled technicians to abrade the area of skin underneath each electrode and apply an electrolytic gel or paste to acquire high-quality SSVEPs from hair-covered areas.

Toward non-hair-bearing brain-computer interfaces for neurocognitive lapse detection.

Recent advances in mobile electroencephalogram (EEG) acquisition based on dry electrodes have started moving Brain-Computer Interface (BCI) applications from well-controlled laboratory settings to real-world environments. However, the application mechanisms and high impedance of dry electrodes over the hair-covered areas remain challenging for everyday use of BCI. In addition, whole-scalp recordings are not always necessary or applicable due to various practical constrains.

Developing an online steady-state visual evoked potential-based brain-computer interface system using EarEEG.

The purpose of this study is to demonstrate an online steady-state visual evoked potential (SSVEP)-based BCI system using EarEEG. EarEEG is a novel recording concept where electrodes are embedded on the surface of earpieces customized to the individual anatomical shape of users' ear. It has been shown that the EarEEG can be used to record SSVEPs in previous studies.

A dynamic stopping method for improving performance of steady-state visual evoked potential based brain-computer interfaces.

The performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has been drastically improved in the past few years. In conventional SSVEP-based BCIs, the speed of a selection is fixed towards high performance based on preliminary offline analysis. However, due to inter-trial variability, the optimal selection time to achieve sufficient accuracy is different for each trial.

A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials.

Canonical correlation analysis (CCA) has been widely used in the detection of the steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The standard CCA method, which uses sinusoidal signals as reference signals, was first proposed for SSVEP detection without calibration. However, the detection performance can be deteriorated by the interference from the spontaneous EEG activities.

Enhancing unsupervised canonical correlation analysis-based frequency detection of SSVEPs by incorporating background EEG.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to provide a fast communication channel between human brain and external devices. In SSVEP-based BCIs, Canonical Correlation Analysis (CCA) has been widely used to detect frequency-coded SSVEPs due to its high efficiency and robustness. However, the detectability of SSVEPs differs among frequencies due to a power-law distribution of the power spectra of spontaneous electroencephalogram (EEG) signals.

Enhancing detection of steady-state visual evoked potentials using individual training data.

Although the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has improved gradually in the past decades, it still does not meet the requirement of a high communication speed in many applications. A major challenge is the interference of spontaneous background EEG activities in discriminating SSVEPs. An SSVEP BCI using frequency coding typically does not have a calibration procedure since the frequency of SSVEPs can be recognized by power spectrum density analysis (PSDA).

Developing an EEG-based on-line closed-loop lapse detection and mitigation system.

In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway.

A high-speed brain speller using steady-state visual evoked potentials.

Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8-15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°).

Generating visual flickers for eliciting robust steady-state visual evoked potentials at flexible frequencies using monitor refresh rate.

In the study of steady-state visual evoked potentials (SSVEPs), it remains a challenge to present visual flickers at flexible frequencies using monitor refresh rate. For example, in an SSVEP-based brain-computer interface (BCI), it is difficult to present a large number of visual flickers simultaneously on a monitor. This study aims to explore whether or how a newly proposed frequency approximation approach changes signal characteristics of SSVEPs.

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology.

EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms.