Simple adaptive sparse representation based classification schemes for EEG based brain–computer interface applications

Abstract

One of the main problems related to electroencephalogram (EEG) based brain–computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation.

Introduction

Brain–computer interface (BCI) systems provide a new communication and control channel between human brain and an external device without any muscle movements [1]. Due to the convenient usability and high temporal resolution compared to other brain imaging equipment such as functional magnetic resonance imaging (fMRI) and magnetoencephalogram (MEG), research of noninvasive electroencephalogram (EEG) based brain–computer interface (BCI) systems is continuously progressed [1], [2], [3].

In the beginning of BCI research, BCI systems have been developed mostly to provide alternative communication means to people who have severe motor disabilities [2], [4], [5]. Recently, much research effort focused on development of portable BCI systems for normal person by using headset shaped scalp electrodes [6], [7] and also dry electrodes which do not need conductive gel for preparation of EEG recording [8], [9]. In addition, with the progress of portable BCI systems and EEG sensor technologies, many BCI applications are developed for general public [9], [10]. However, for the BCI systems going beyond laboratory researches, the most important issue is stable classification performance.

Normally, EEG based BCI experiment can be categorized as a training (calibration) stage and a real time testing (feedback) stage. In the training stage, translation algorithm such as classification is designed using collected training signals. Then, an application device such as neural prosthesis is controlled by using the classification algorithm in real time testing stage. However, EEG signals have inherent non-stationary characteristics and there exist significant day-to-day and even session-to-session variability [12], [27], [29]. Thus, features of experimental EEG signals are changed from the offline training sessions to online testing sessions [11]. Due to this, classification performance is unavoidably deteriorated in BCI experiment with time. In addition, the training session (15–35 min) is conventionally carried out every time before using the BCI systems even for experienced subjects [12]. These are major obstacles of real-time online BCI applications.

To overcome the performance decrease caused by the non-stationarity of EEG signals, many adaptive signal processing methods are proposed. In [27], [28], [29], adaptive feature extraction methods are proposed for the motor imagery based BCI systems. For the adaptive classification scheme, in [13], mean and covariance matrix of a statistical classifier are iteratively updated using each class data. The study [11] proposes a bias adaptation scheme of linear discriminant analysis (LDA) classification using class labels of several test trials. They have shown that simple bias adaptation is effective for online test data. In [14], they propose an expectation-maximization (EM) algorithm based unsupervised adaptive classification method. Using EM algorithm, common spatial pattern (CSP) features are re-extracted and parameters of Bayes classifier are updated in each iteration step. Similarly, [15] suggest unsupervised bias adaptation of LDA without using class label information. Previous studies for adaptive classification method need classifier re-adjustment (training) such as parameters and bias adaptation for new test trials. However, for this re-training, additional computation is needed in each update (adjustment) step.

Recently, with much progress of L1 minimization technique in compressive sensing field [21], [22], sparse representation has received a lot of attention in signal processing and pattern recognition fields. Especially, sparse representation based classification (SRC) has shown an increased interest [16], [23], [24]. SRC framework is first introduced by Huang et al [16]. A test data from one class is predominantly represented by the same class training data from dictionary. The dictionary is composed by all class training data and usually underdetermined. Sparse representation of the test data using the dictionary can effectively be solved by the L1 minimization tool, and the classification is performed by comparing the representation error for each class.

SRC have been also studied for EEG signal classification [17], [18], [25]. In [18] and [25], SRC scheme is applied to vigilance detection and epileptic seizure detection problem respectively. In addition, SRC scheme is first introduced for motor imagery based BCI application in [17]. They have shown that the SRC exhibits better classification performance than the conventional LDA method using two experimental datasets. Another study [31] also revealed that the SRC shows better classification accuracy and noise robustness than the well-known SVM method. However, no research has been studied for adaptive SRC scheme for online BCI applications.

Compared to other fixed decision rule based classification method such as linear discriminant analysis (LDA) and support vector machine (SVM), in the SRC, the sparse representation is adaptively performed for each test data by utilizing all training data in the dictionary. Along with this inherent adaptive characteristic of the SRC, in this study, we propose simple adaptive SRC schemes for real-time BCI applications. We suggest a dictionary update rule and an incoherence based dictionary modification (IDM) method. For the dictionary update rule, supervised and unsupervised adaptive schemes and also accumulated and fixed update rules are considered. Proposed dictionary update methods are very simple and additional computation for adaptation is not needed. In the part of IDM method, our aim is to create a maximally incoherent dictionary via an incoherence measure of training data. This method is applied to the training data before performing the sparse representation. Using online motor imagery based BCI experimental datasets, we evaluate classification performance of the proposed adaptive method by comparing with the conventional SRC and other adaptive classification methods.

This paper is organized as follows. In Section 2, our experiment and dataset are explained. In Section 3, technical methods such as feature extraction, sparse representation based classification (SRC) method and proposed adaptive SRC schemes are introduced. We explain experimental evaluation strategy and results in Section 4. In Section 5, we discuss some experimental results. Finally, we conclude the paper in Section 6.