Improved Semisupervised Adaptation for a Small Training Dataset in the Brain–Computer Interface

Effective common spatial pattern (CSP) feature extraction for motor-imagery (MI) EEG recordings usually depends on the filter band selection to a large extent. However, the most proper filter band can hardly be determined manually due to its subject-specific property. This study introduces a sparse support vector machine (SSVM) approach to implement simultaneous CSP feature selection and classification for MI-based brain–computer interface (BCI). In SSVM, CSP features are first extracted on multiple signals that are filtered from raw EEG data at a set of overlapping subbands. SVM with l 1-norm regularization is then proposed to classify MI tasks with automatic selection of filter bands giving the significant CSP features. The effectiveness of SSVM for MI classification is demonstrated on the BCI Competition III dataset IVa, in comparison with several other competing methods. Experimental results indicate that the proposed SSVM method is promising for development of an improved MI-based BCI.

Motion recognition is an important application of electromyography (EMG) analysis. While discrete motions such as hand open, hand close and wrist pronation have been extensively investigated, studies on combined motions involving two or more degrees of freedom (DOFs) are relatively few and the classification accuracy of the combined motions reported in previous studies is barely satisfactory. To improve the accuracy of the combined motion recognition, common spatial pattern (CSP) was employed in this study to extract spatial features. 18 forearm motion classes, consisted of 8 discrete motions and 10 combined motions, were classified by the proposed method. Our results showed that the accuracy rate of CSP features was 96.3%, which outperformed the commonly used time-domain (TD) features by 2.4% and TD combined with auto-regression coefficients (TDAR) by 0.6%. Moreover, CSP features cost noticeable much less time than TDAR and quite less time than TD in testing. These results suggest that CSP features could be a better feature set for multi-DOF myoelectric control than conventional features.

Motor imagery-based brain–computer interface (MI-BCI) inefficiency phenomenon is one of the biggest challenges in MI-BCI research. BCI inefficiency subject is defined as the subject who cannot achieve classification accuracy higher than 70% since 70% is considered to be the minimum accuracy for communication by BCI. About 15–30% of the people are MI-BCI inefficiency according to the investigation. Most of the existing studies used common spatial patterns (CSP) to extract motor imagery feature and identify MI-BCI inefficiency subject based on the obtained classification accuracy. We think the MI-BCI performance maybe suppressed because CSP mainly extracts event-related desynchronization (ERD) feature, while the features generated by motor imagery are more than that. In this current work, we screened a total of 12 MI-BCI inefficiency subjects by CSP feature firstly, and recorded the motor imagery EEG data of them. Furthermore, we constructed a task-related brain network by calculating the coherence between EEG channels, the graph-based analysis showed that the node degree and clustering coefficient have intensity differences between left and right hand motor imagery. Finally, the two kinds of features were used to discriminate the two tasks. The results showed that both node degree and clustering coefficient features perform better than CSP, and the feature combination of brain network and CSP achieved higher accuracy than a single feature. In particular, a total of four subjects achieved accuracy higher than 70% by node degree and CSP features fusion. This work demonstrates that the accuracy of the MI-BCI inefficiency subject can be increased by using the brain network feature, but the accuracy gains are not high enough; it is worth to try other types of feature extraction algorithms for the MI-BCI inefficiency subject.

A brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin . An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected into the estimated signal subspace. Data recorded on three subjects were used to evaluate the proposed method. The results, which are presented using a Bayesian linear discriminant analysis classifier , show that the proposed method is efficient and accurate.

In this paper, to develop a mobile robot by using a BCI to issue a motion command including turning left and right and going forward, we propose a new P300 Brain-computer interface (BCI). Three kinds of P300 visual stimuli were first designed. And then we developed the corresponding BCI systems based on the three stimuli and compared their performance by using a linear discriminant analysis classifier with the features selected from EEG potentials via principal component analysis. Experimental results from three participants suggest that the BCIs with the last two kinds of stimuli can select a desired command with over 80% accuracy in about four seconds of selection time, showing that using the proposed BCI to control a robot is feasible.

Multimodal collaborative BCI system based on the improved CSP feature extraction algorithm

Objective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain–computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Approach: Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. Main results: The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. Significance: CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI inefficiency to one-fourth in comparison to previous non-adaptive paradigms.

One of the challenges of using brain-computer interfaces (BCIs) over extended periods of time is the variation of the users' performance from one experimental day to another. The goal of the current study is to propose a performance estimator for an electroencephalography-based motor imagery BCI by assessing the reliability of a command (i.e., predicting a 'short' or 'long' command delivery time, CDT). Using a short time window (<1.5 s, shorter than the delivery time) of the mental task execution and a linear discriminant analysis classifier, we could reliably differentiate between short and long CDT (Area under the sensitivity-specificity curve, AUC . 0.8) for 9 healthy subjects. Moreover, we assessed the feasibility of providing online adaptive assistance using the performance estimator in a BCI game by comparing two conditions: (i) allowing a 'fixed timeout' to deliver each command or (ii) providing 'adaptive assistance' by giving more time if the performance estimator detects a long CDT. The results revealed that providing adaptive assistance increases the ratio of correct commands significantly cantly (p <0.01). Moreover, the task load index (measured via the NASA TLX questionnaire) shows a significantly higher user acceptance in case of providing adaptive assistance (p <0.01). Furthermore, the results obtained in this study were used to simulate a robotic navigation scenario, which showed how adaptive assistance improved performance.

Current Brain Computer Interface (BCI) systems are still not commercialized due to their insufficient accuracies. Although Common Spatial Pattern (CSP) is an effective method for BCI applications, it does not consider the channels' co-activation information. This study is aimed at modifying CSP method by incorporating synchronization of channels in the CSP formula and then passes the values through a nonlinear kernel function to enhance the discrimination rate between the left and right imagery movements. Inserting the synchronization of every two channels into the kernel CSP formula is performed in two different ways. Experimental results on a set of EEG signals belonged to three subjects for discrimination of left and right hand imagery movements reveal that the proposed kernel synchronous CSP method outperformed the standard CSP, synchronous CSP and kernel CSP methods for all subjects.

Common spatial pattern (CSP) is a powerful algorithm for extracting discriminative brain patterns in motor imagery-based brain-computer interfaces (BCIs). However, its performance depends largely on the subject-specific frequency band and time segment. Accurate selection of most responsive frequency band and time segment remains a crucial problem. A novel evolutionary algorithm, the backtracking search optimization algorithm is used to find the optimal frequency band and the optimal combination of frequency band and time segment. The former is searched by a frequency window with changing width of which starting and ending points are selected by the backtracking optimization algorithm; the latter is searched by the same frequency window and an additional time window with fixed width. The three parameters, the starting and ending points of frequency window and the starting point of time window, are jointly optimized by the backtracking search optimization algorithm. Based on the chosen frequency band and fixed or chosen time segment, the same feature extraction is conducted by CSP and subsequent classification is carried out by Fisher discriminant analysis. The classification error rate is used as the objective function of the backtracking search optimization algorithm. The two methods, named BSA-F CSP and BSA-FT CSP, were evaluated on data set of BCI competition and compared with traditional wideband (8-30[Formula: see text]Hz) CSP. The classification results showed that backtracking search optimization algorithm can find much effective frequency band for EEG preprocessing compared to traditional broadband, substantially enhancing CSP performance in terms of classification accuracy. On the other hand, the backtracking search optimization algorithm for joint selection of frequency band and time segment can find their optimal combination, and thus can further improve classification rates.

Brain computer interface (BCI) is known as a good way to communicate between brain and computer or other device. There are many kinds of physiological signal can operate BCI systems. Motor imagery (MI) has been demonstrated to be a good way to operate a BCI system. In some recent studies about MI based BCI systems, low accuracy rate and time consuming are common problems. In this thesis, a novel motor imagery algorithm is proposed to improve the accuracy rate and computational efficiency at the same time. The architecture of many BCI system is quite complex and they involve time consuming processing. The electroencephalography (EEG) signal is the most commonly used inputs for BCI applications but EEG is often contaminated with noise. To overcome such drawbacks, in this paper we use the common spatial pattern (CSP) for feature extraction from EEG and the linear discriminant analysis (LDA) for motor imagery classification. In this study, CSP and LDA have been used to reduce the artifact and classify Mi-based EEG signal. We have used two-level cross validation scheme to determine the subject specific best time window and number of CSP features. We have compared the performance of our system with BCI competition results. This novel algorithm with high accuracy rate and efficiency can be applied to real time BCI system in real-life applications.

Applications based on electroencephalography (EEG) signals suffer from the mutual contradiction of high classification performance vs. low cost. The nature of this contradiction makes EEG signal reconstruction with high sampling rates and sensitivity challenging. Conventional reconstruction algorithms lead to loss of the representative details of brain activity and suffer from remaining artifacts because such algorithms only aim to minimize the temporal mean-squared-error (MSE) under generic penalties. Instead of using temporal MSE according to conventional mathematical models, this paper introduces a novel reconstruction algorithm based on generative adversarial networks with the Wasserstein distance (WGAN) and a temporal-spatial-frequency (TSF-MSE) loss function. The carefully designed TSF-MSE-based loss function reconstructs signals by computing the MSE from time-series features, common spatial pattern features, and power spectral density features. Promising reconstruction and classification results are obtained from three motor-related EEG signal datasets with different sampling rates and sensitivities. Our proposed method significantly improves classification performances of EEG signals reconstructions with the same sensitivity and the average classification accuracy improvements of EEG signals reconstruction with different sensitivities. By introducing the WGAN reconstruction model with TSF-MSE loss function, the proposed method is beneficial for the requirements of high classification performance and low cost and is convenient for the design of high-performance brain computer interface systems.

Motor imagery (MI) is the most used paradigm in Brain-Computer Interface (BCI). As in other BCI paradigms, it is divided into feature extraction and classification. In this study, we implemented two feature extraction methods, one based on the well-known Common Spatial Pattern (CSP) algorithm and the other based on a not so well-known electroencephalogram (EEG) source localization technique called Low-Resolution Brain Electromagnetic Tomography (LORETA). We evaluated 30 right-handed subjects from an EEG data set made publicly available through Giga Science, where participants performed MI of left- and right-hand movements. After feature extraction with CSP and LORETA, MI tasks classification were carried out using the Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naïve Bayes (NB) and Multi-Layer Perceptron (MLP) algorithms. Finally, we evaluated classification performance with all possible combination of classifiers and feature extraction methods. For all classifiers, the CSP feature extraction method performed better than LORETA. The best classification accuracy for the LORETA method was 71.2% and for the CSP method was 94.2%, both achieved with SVM.

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

The purpose of this study is to motivate the use of the simpler Linear Discriminant (LD) classifier as compared to the commonly used Multilayer-perceptron-backpropagation (MLP-BP) neural network for Brain Computer Interface (BCI) design. We investigated the performances of MLP-BP and LD classifiers for mental task based BCI design. In the experimental study, EEG signals from five mental tasks were recorded from four subjects and the classification performances of different combinations of two mental tasks were studied for each subject. Two different AR models were used to compute the features from the electroencephalogram signals: Burg's algorithm (ARB) and Least Square algorithm (ARLS). The results showed that in most cases, LD classifier gave superior classification performance as compared to MLP-BP, with reduced computational complexity. However, the best mental tasks for each subject were the same using both classifiers. ARLS gave the best performance (93.10%) using MLP-BP and (97.00%) using LD. As the best mental task combinations varied between subjects, we conclude that for different subjects, proper selection of mental tasks and feature extraction methods would be essential for a BCI design.