Optimizing a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear.

Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has the characteristics of fast communication speed, high stability, and wide applicability, thus it has been widely studied. With the rapid development in paradigm, algorithm, and system design, SSVEP-BCI is gradually applied in clinical and real-life scenarios. In order to improve the ease of use of the SSVEP-BCI system, many studies have been focusing on developing it on the hairless area, but due to the lack of redesigning the stimulation paradigm to better adapt to the new area, the electroencephalography response in the hairless area is worse than occipital region.

Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis.

A brain-computer interface (BCI) provides a direct communication channel between a brain and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has received increasing attention due to its high information transfer rate, which is accomplished by individual calibration for frequency recognition. Task-related component analysis (TRCA) is a recent and state-of-the-art method for individually calibrated SSVEP-BCIs.

Align and Pool for EEG Headset Domain Adaptation (ALPHA) to Facilitate Dry Electrode Based SSVEP-BCI.

Objective: The steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) implemented in dry electrodes is a promising paradigm for alternative and augmentative communication in real-world applications. To improve its performance and reduce the calibration effort for dry-electrode systems, we utilize cross-device transfer learning by exploiting auxiliary individual wet-electrode electroencephalogram (EEG).

Methods: We proposed a novel transfer learning framework named ALign and Pool for EEG Headset domain Adaptation (ALPHA), which aligns the spatial pattern and the covariance for domain adaptation.

Interface, interaction, and intelligence in generalized brain-computer interfaces.

A brain-computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view.

Optimizing a dual-frequency and phase modulation method for SSVEP-based BCIs.

Objective: The design of the stimulation paradigm plays an important role in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) studies. Among various stimulation designs, the dual-frequency paradigm in which two frequencies are used to encode one target is of importance and interest. However, because the number of possible frequency combinations is huge, the existing dual-frequency modulation paradigms failed to optimize the encoding towards the best combinations.

The Spatio-Temporal Equalization for Evoked or Event-Related Potential Detection in Multichannel EEG Data.

Goal: Evoked or Event-Related Potential (EP/ERP) detection is a major area of interest within the domain of EEG (electroencephalography) signal processing. While traditional methods of EEG processing have mostly focused on enhancing signal components, few have explored background noise suppression techniques. Optimizing the suppression of background noise can play a critical role in improving the performance of EP/ERP detection.

A novel system of SSVEP-based human-robot coordination.

Objective: Human-robot coordination (HRC) aims to enable human and robot to form a tightly coupled system to accomplish a task. One of its important application prospects is to improve the physical function of the disabled. However, the low level of the coordination between human and robot and the limited potential users still hamper the efficiency of such systems.

A Dynamic Window Recognition Algorithm for SSVEP-Based Brain-Computer Interfaces Using a Spatio-Temporal Equalizer.

The past decade has witnessed rapid development in the field of brain-computer interfaces (BCIs). While the performance is no longer the biggest bottleneck in the BCI application, the tedious training process and the poor ease-of-use have become the most significant challenges. In this study, a spatio-temporal equalization dynamic window (STE-DW) recognition algorithm is proposed for steady-state visual evoked potential (SSVEP)-based BCIs.

A study on dynamic model of steady-state visual evoked potentials.

Objective: Significant progress has been made in the past two decades to considerably improve the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). However, there are still some unsolved problems that may help us to improve BCI performance, one of which is that our understanding of the dynamic process of SSVEP is still superficial, especially for the transient-state response.

Approach: This study introduced an antiphase stimulation method (antiphase: phase [Formula: see text]), which can simultaneously separate and extract SSVEP and event-related potential (ERP) signals from EEG, and eliminate the interference of ERP to SSVEP.

Exploring the temporal dynamics of sustained and transient spatial attention using steady-state visual evoked potentials.

While the behavioral dynamics as well as the functional network of sustained and transient attention have extensively been studied, their underlying neural mechanisms have most often been investigated in separate experiments. In the present study, participants were instructed to perform an audio-visual spatial attention task. They were asked to attend to either the left or the right hemifield and to respond to deviant transient either auditory or visual stimuli.

A novel stimulation method for multi-class SSVEP-BCI using intermodulation frequencies.

Objective: Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has been widely investigated because of its easy system configuration, high information transfer rate (ITR) and little user training. However, due to the limitations of brain responses and the refresh rate of a monitor, the available stimulation frequencies for practical BCI application are generally restricted.

Approach: This study introduced a novel stimulation method using intermodulation frequencies for SSVEP-BCIs that had targets flickering at the same frequency but with different additional modulation frequencies.

A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces.

This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset acquired with a 40-target brain- computer interface (BCI) speller. The dataset consists of 64-channel Electroencephalogram (EEG) data from 35 healthy subjects (8 experienced and 27 naïve) while they performed a cue-guided target selecting task. The virtual keyboard of the speller was composed of 40 visual flickers, which were coded using a joint frequency and phase modulation (JFPM) approach.

An online hybrid BCI system based on SSVEP and EMG.

Objective: A hybrid brain-computer interface (BCI) is a device combined with at least one other communication system that takes advantage of both parts to build a link between humans and machines. To increase the number of targets and the information transfer rate (ITR), electromyogram (EMG) and steady-state visual evoked potential (SSVEP) were combined to implement a hybrid BCI. A multi-choice selection method based on EMG was developed to enhance the system performance.

High-speed spelling with a noninvasive brain-computer interface.

The past 20 years have witnessed unprecedented progress in brain-computer interfaces (BCIs). However, low communication rates remain key obstacles to BCI-based communication in humans. This study presents an electroencephalogram-based BCI speller that can achieve information transfer rates (ITRs) up to 5.

Space distribution of EEG responses to hanoi-moving visual and auditory stimulation with Fourier Independent Component Analysis.

Background And Objective: The relationship between EEG source signals and action-related visual and auditory stimulation is still not well-understood. The objective of this study was to identify EEG source signals and their associated action-related visual and auditory responses, especially independent components of EEG.

Methods: A hand-moving-Hanoi video paradigm was used to study neural correlates of the action-related visual and auditory information processing determined by mu rhythm (8-12 Hz) in 16 healthy young subjects.

Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface.

Objective: Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established.

Approach: This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs.

Enhancing performances of SSVEP-based brain-computer interfaces via exploiting inter-subject information.

Objective: A new training-free framework was proposed for target detection in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) using joint frequency-phase coding.

Approach: The key idea is to transfer SSVEP templates from the existing subjects to a new subject to enhance the detection of SSVEPs. Under this framework, transfer template-based canonical correlation analysis (tt-CCA) methods were developed for single-channel and multi-channel conditions respectively.

Probabilistic Common Spatial Patterns for Multichannel EEG Analysis.

Common spatial patterns (CSP) is a well-known spatial filtering algorithm for multichannel electroencephalogram (EEG) analysis. In this paper, we cast the CSP algorithm in a probabilistic modeling setting. Specifically, probabilistic CSP (P-CSP) is proposed as a generic EEG spatio-temporal modeling framework that subsumes the CSP and regularized CSP algorithms.

Performance measurement for brain-computer or brain-machine interfaces: a tutorial.

Objective: Brain-computer interfaces (BCIs) have the potential to be valuable clinical tools. However, the varied nature of BCIs, combined with the large number of laboratories participating in BCI research, makes uniform performance reporting difficult. To address this situation, we present a tutorial on performance measurement in BCI research.

Visual and auditory brain-computer interfaces.

Over the past several decades, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have attracted attention from researchers in the field of neuroscience, neural engineering, and clinical rehabilitation. While the performance of BCI systems has improved, they do not yet support widespread usage. Recently, visual and auditory BCI systems have become popular because of their high communication speeds, little user training, and low user variation.

Bayesian estimation of ERP components from multicondition and multichannel EEG.

Extraction and separation of functionally different event-related potentials (ERPs) from electroencephalography (EEG) is a long-standing problem in cognitive neuroscience. In this paper, we propose a Bayesian spatio-temporal model for estimating ERP components from multichannel EEG recorded under multiple experimental conditions. The model isolates the spatially and temporally overlapping ERP components by utilizing their phase-locking structure and the inter-condition non-stationarity structure of their amplitudes and latencies.

Brain-computer interface based on intermodulation frequency.

Objective: Most recent steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems have used a single frequency for each target, so that a large number of targets require a large number of stimulus frequencies and therefore a wider frequency band. However, human beings show good SSVEP responses only in a limited range of frequencies. Furthermore, this issue is especially problematic if the SSVEP-based BCI takes a PC monitor as a stimulator, which is only capable of generating a limited range of frequencies.

Fast presurgical functional mapping using task-related intracranial high gamma activity.

Object: Electrocorticography (ECoG) is a powerful tool for presurgical functional mapping. Power increase in the high gamma band has been observed from ECoG electrodes on the surface of the sensory motor cortex during the execution of body movements. In this study the authors aim to validate the clinical usage of high gamma activity in presurgical mapping by comparing ECoG mapping with traditional direct electrical cortical stimulation (ECS) and functional MRI (fMRI) mapping.

A study of the existing problems of estimating the information transfer rate in online brain-computer interfaces.

Objective: Today, the brain-computer interface (BCI) community lacks a standard method to evaluate an online BCI's performance. Even the most commonly used metric, the information transfer rate (ITR), is often reported differently, even incorrectly, in many papers, which is not conducive to BCI research. This paper aims to point out many of the existing problems and give some suggestions and methods to overcome these problems.

Characteristics and classification of hippocampal θ rhythm induced by passive translational displacement.

Theta rhythms in the hippocampus are believed to be the "metric" relating to various behavior patterns for free roaming rats. In this study, the theta rhythms were studied while rats either walked or were passively translated by a toy car on a linear track (referred to as WALK and TRANS respectively). For the similar running speeds in WALK and TRANS conditions, theta frequency and amplitude were both reduced during TRANS.