Event-related Potential-based Brain-computer Interface Research Articles

Evaluation of Different Types of Stimuli in an Event-Related Potential-Based Brain-Computer Interface Speller under Rapid Serial Visual Presentation.

Rapid serial visual presentation (RSVP) is currently a suitable gaze-independent paradigm for controlling visual brain-computer interfaces (BCIs) based on event-related potentials (ERPs), especially for users with limited eye movement control. However, unlike gaze-dependent paradigms, gaze-independent ones have received less attention concerning the specific choice of visual stimuli that are used. In gaze-dependent BCIs, images of faces-particularly those tinted red-have been shown to be effective stimuli. This study aims to evaluate whether the colour of faces used as visual stimuli influences ERP-BCI performance under RSVP. Fifteen participants tested four conditions that varied only in the visual stimulus used: grey letters (GL), red famous faces with letters (RFF), green famous faces with letters (GFF), and blue famous faces with letters (BFF). The results indicated significant accuracy differences only between the GL and GFF conditions, unlike prior gaze-dependent studies. Additionally, GL achieved higher comfort ratings compared with other face-related conditions. This study highlights that the choice of stimulus type impacts both performance and user comfort, suggesting implications for future ERP-BCI designs for users requiring gaze-independent systems.

Dual stimuli interface with logical division using local move stimuli.

Improving information transfer rate is a key to prompt the speed of outputting instructions of the event-related potential-based brain-computer interface. Our previous study designed a dual-stimuli interface that simultaneously presents two types of different stimuli to improve the speed. While, adding more stimuli into this interface makes subject easily affected by "flanker effect" that decreases the accuracy of recognizing intention. To achieve high recognition accuracy with many stimuli, this study proposes a dual stimuli interface based on whole flash and local move (DS-WL) and two rules of stimulus arrangement to induce the brain signals. Twenty subjects participated in the experiment, and their signals are recognized by a back propagation neural network classifier. The local move induces larger and later signals of targets to help discriminate the two kinds of stimuli; the rules reduce the N200 and P300 amplitudes of non-target, which improves accuracy. This study demonstrates that the DS-WL is a useful way to shorten the instruction output cycle and speed up the instructions outputting by local move and rules.

A Robust 3D-Convolutional Neural Network-Based Electroencephalogram Decoding Model for the Intra-Individual Difference.

The convolutional neural network (CNN) has emerged as a powerful tool for decoding electroencephalogram (EEG), which owns the potential use in the event-related potential-based brain-computer interface (ERP-BCI). However, the intra-individual difference of ERP makes the traditional learning models trained on static EEG data hard to decode when the EEG features vary along the time, which limits the long-time performance of the model. Addressing this problem, this study proposes a three-dimension CNN (3D-CNN)-based model to decode the ERPs dynamically. As input, the EEG is transformed into a brain topographic map stream along time. Then the 3D-CNN applies three-dimension kernels to capture the dynamical characteristic of spatial feature at several time points. Ten subjects participated in a cross-time task for 6 or 12[Formula: see text]h. The 3D-CNN shows higher accuracies and shorter computational cost than the baseline models of the 2D-CNN, the long short term memory (LSTM), the back propagation (BP), and the fisher linear discriminant analysis (FLDA) when detecting the ERPs. In addition, four schemes of the 3D-CNN are compared to explore the influence of the structure on the performance. This result demonstrates advanced robustness of the 3D-CNN kernel to the intra-individual EEG difference, helping to launch a more practical EEG decoding model for a long-time use.

A fisher linear discriminant analysis classifier fused with naïve Bayes for simultaneous detection in an asynchronous brain-computer interface

BackgroundAn asynchronous event-related potential-based brain computer interface (ERP-BCI) permits the subjects to output intentions at their own pace, which provides a more free and practical communication pathways without the need for muscle activity. The core of constructing this type of system is to discriminate both the intentions and brain states. New methodsThis study proposes a fisher linear discriminant analysis classification algorithm fused with naïve Bayes (B-FLDA) for the ERP-BCI to simultaneous recognize the subjects’ intentions, working and idle states. This method uses the spectral characteristics of visual-evoked potential and the time-domain characteristics of ERP to simultaneously detect brain states and target stimulus, and obtain the final discrimination result through probability fusion. ResultsThe accuracy and the information transfer rate increase to 98.61% and 62.80 bits/min under 10 repetitions and 1 repetition, respectively. The three parameters of receiver operator characteristic curve have achieved better performance. Comparison with existing methodsTen subjects participate in this study with the proposed algorithms and two other control methods. The accuracy and information transfer rate of this algorithm are better than the other methods. ConclusionsIt indicates that the naïve Bayes-FLDA algorithm is able to improve the performance of an asynchronous BCI system by detecting the intentions and states simultaneously.

Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain–Computer Interface

A brain-computer interface (BCI) is a communication tool that analyzes neural activity and relays the translated commands to carry out actions. In recent years, semi-supervised learning (SSL) has attracted attention for visual event-related potential (ERP)-based BCIs and motor-imagery BCIs as an effective technique that can adapt to the variations in patterns among subjects and trials. The applications of the SSL techniques are expected to improve the performance of auditory ERP-based BCIs as well. However, there is no conclusive evidence supporting the positive effect of SSL techniques on auditory ERP-based BCIs. If the positive effect could be verified, it will be helpful for the BCI community. In this study, we assessed the effects of SSL techniques on two public auditory BCI datasets-AMUSE and PASS2D-using the following machine learning algorithms: step-wise linear discriminant analysis, shrinkage linear discriminant analysis, spatial temporal discriminant analysis, and least-squares support vector machine. These backbone classifiers were firstly trained by labeled data and incrementally updated by unlabeled data in every trial of testing data based on SSL approach. Although a few data of the datasets were negatively affected, most data were apparently improved by SSL in all cases. The overall accuracy was logarithmically increased with every additional unlabeled data. This study supports the positive effect of SSL techniques and encourages future researchers to apply them to auditory ERP-based BCIs.

Effect of the concreteness of robot motion visual stimulus on an event-related potential-based brain-computer interface

Event-related potential (ERP)-based brain-computer interface (BCI) has been widely used in robot control. Increasing the amplitude of the ERPs is key for improving the performance of ERP-based BCI. However, using images of robot motion as visual stimuli has not been studied widely. The aim of this study is to explore the concreteness of robot motion images on ERPs. Fifteen subjects used five kinds of visual spellers employing different images as visual stimuli: squares, arrows, a single kind of robot motion, multiple kinds of robot motions, and multiple kinds of robot motions with arrows. The three robot motion stimuli induced larger N200 and P300 potentials than non-robot motion stimuli. The topography shows that robot motion stimuli also evoke stronger negativities in the anterior and occipital areas. Concrete images provide more information to the subject about the robot motion, which might help the brain extract the meaning of the image more automatically. We use a support vector machine to detect the subject’s intentions. There is substantial improvement in the classification performance when using robot motion images as visual stimuli, which implies that concrete visual stimuli improve the performance of the ERP-based BCI.

Towards encoding shape features with visual event-related potential based brain–computer interface for generative design

This article will focus on abstracting and generalising a well-studied paradigm in visual, event-related potential based brain–computer interfaces, for the spelling of characters forming words, into the visually encoded discrimination of shape features forming design aggregates. After identifying typical technologies in neuroscience and neuropsychology of high interest for integrating fast cognitive responses into generative design and proposing the machine learning model of an ensemble of linear classifiers in order to tackle the challenging features that electroencephalography data carry, it will present experiments in encoding shape features for generative models by a mechanism of visual context updating and the computational implementation of vision as inverse graphics, to suggest that discriminative neural phenomena of event-related potentials such as P300 may be used in a visual articulation strategy for modelling in generative design.

A Novel Event-Related Potential-Based Brain–Computer Interface for Continuously Controlling Dynamic Systems

Using electroencephalograms (EEG) to continuously control dynamic systems (such as wheelchairs and vehicles) have important values in helping people improve their quality of life and independence. In this paper, we develop a novel event-related potential (ERP)-based brain-computer interface (BCI) to detect human intention for continuously controlling dynamic systems. First, channel selection is performed by using the improved forward floating search algorithm (ISFFS). Then, a convolutional neural network (CNN) is used for feature extraction and a linear discrimination analysis or support vector machine classifier is used to decode intention for each subject. The experimental results of eight subjects show that the proposed system based on the optimal channels selected by the ISFFS algorithm and features extracted by the CNN performs well. Compared to other similar BCI systems, the proposed one can issue a command more quickly and accurately. This work not only facilitates the research of brain-controlled dynamic systems, but it also provides some new insights into the research of BCIs.

A Dual Stimuli Approach Combined with Convolutional Neural Network to Improve Information Transfer Rate of Event-Related Potential-Based Brain-Computer Interface.

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

Performance improvement of ERP-based brain-computer interface via varied geometric patterns.

Recently, many studies have been focusing on optimizing the stimulus of an event-related potential (ERP)-based brain-computer interface (BCI). However, little is known about the effectiveness when increasing the stimulus unpredictability. We investigated a new stimulus type of varied geometric pattern where both complexity and unpredictability of the stimulus are increased. The proposed and classical paradigms were compared in within-subject experiments with 16 healthy participants. Results showed that the BCI performance was significantly improved for the proposed paradigm, with an average online written symbol rate increasing by 138% comparing with that of the classical paradigm. Amplitudes of primary ERP components, such as N1, P2a, P2b, N2, were also found to be significantly enhanced with the proposed paradigm. In this paper, a novel ERP BCI paradigm with a new stimulus type of varied geometric pattern is proposed. By jointly increasing the complexity and unpredictability of the stimulus, the performance of an ERP BCI could be considerably improved.

Influence of P300 latency jitter on event related potential-based brain–computer interface performance

Objective. Several ERP-based brain–computer interfaces (BCIs) that can be controlled even without eye movements (covert attention) have been recently proposed. However, when compared to similar systems based on overt attention, they displayed significantly lower accuracy. In the current interpretation, this is ascribed to the absence of the contribution of short-latency visual evoked potentials (VEPs) in the tasks performed in the covert attention modality. This study aims to investigate if this decrement (i) is fully explained by the lack of VEP contribution to the classification accuracy; (ii) correlates with lower temporal stability of the single-trial P300 potentials elicited in the covert attention modality. Approach. We evaluated the latency jitter of P300 evoked potentials in three BCI interfaces exploiting either overt or covert attention modalities in 20 healthy subjects. The effect of attention modality on the P300 jitter, and the relative contribution of VEPs and P300 jitter to the classification accuracy have been analyzed. Main results. The P300 jitter is higher when the BCI is controlled in covert attention. Classification accuracy negatively correlates with jitter. Even disregarding short-latency VEPs, overt-attention BCI yields better accuracy than covert. When the latency jitter is compensated offline, the difference between accuracies is not significant. Significance. The lower temporal stability of the P300 evoked potential generated during the tasks performed in covert attention modality should be regarded as the main contributing explanation of lower accuracy of covert-attention ERP-based BCIs.

Asynchronous gaze-independent event-related potential-based brain–computer interface

In this study a gaze independent event related potential (ERP)-based brain computer interface (BCI) for communication purpose was combined with an asynchronous classifier endowed with dynamical stopping feature. The aim was to evaluate if and how the performance of such asynchronous system could be negatively affected in terms of communication efficiency and robustness to false positives during the intentional no-control state. The proposed system was validated with the participation of 9 healthy subjects. A comparison was performed between asynchronous and synchronous classification technique outputs while users were controlling the same gaze independent BCI interface. The performance of both classification techniques were assessed both off-line and on-line by means of the efficiency metric introduced by Bianchi et al. (2007). This latter metric allows to set a different misclassification cost for wrong classifications and abstentions. Robustness was evaluated as the rate of false positives occurring during voluntary no-control states. The asynchronous classifier did not exhibited significantly higher accuracy or lower error rate with respect to the synchronous classifier (accuracy: 74.66% versus 87.96%, error rate: 7.11% versus 12.04% respectively). However, the on-line and off-line analysis revealed that the communication efficiency was significantly improved (p<.05) with the asynchronous classification modality as compared with the synchronous. Furthermore, the asynchronous classifier proved to be robust to false positives during intentional no-control state which occur during the ongoing visual stimulation (less than 1 false positive every 6min). As such, the proposed ERP-BCI system which combines an asynchronous classifier with a gaze independent interface is a promising solution to be further explored in order to increase the general usability of ERP-based BCI systems designed for severely disabled people with an impairment of the voluntary control of eye movements. In fact, the asynchronous classifier can improve communication efficiency automatically adapting the number of stimulus repetitions to the current user's state and suspending the control if he/she does not intend to select an item.

A Unified Probabilistic Approach to Improve Spelling in an Event-Related Potential-Based Brain–Computer Interface

In recent years, in an attempt to maximize performance, machine learning approaches for event-related potential (ERP) spelling have become more and more complex. In this paper, we have taken a step back as we wanted to improve the performance without building an overly complex model, that cannot be used by the community. Our research resulted in a unified probabilistic model for ERP spelling, which is based on only three assumptions and incorporates language information. On top of that, the probabilistic nature of our classifier yields a natural dynamic stopping strategy. Furthermore, our method uses the same parameters across 25 subjects from three different datasets. We show that our classifier, when enhanced with language models and dynamic stopping, improves the spelling speed and accuracy drastically. Additionally, we would like to point out that as our model is entirely probabilistic, it can easily be used as the foundation for complex systems in future work. All our experiments are executed on publicly available datasets to allow for future comparison with similar techniques.

A generative model approach for decoding in the visual event-related potential-based brain–computer interface speller

There is a strong tendency towards discriminative approaches in brain–computer interface (BCI) research. We argue that generative model-based approaches are worth pursuing and propose a simple generative model for the visual ERP-based BCI speller which incorporates prior knowledge about the brain signals. We show that the proposed generative method needs less training data to reach a given letter prediction performance than the state of the art discriminative approaches.