FrD2.2
Proceedings of the 4th International IEEE EMBS Conference on Neural Engineering Antalya, Turkey, April 29 - May 2, 2009
Continuous EEG Classification for a Self-paced BCI Abdul Satti, Damien Coyle, Girijesh Prasad. Intelligent Systems Research Centre, Faculty of Computing and Engineering, University of Ulster Derry, Northern Ireland, UK
[email protected] Abstract— Transferring electroencephalogram(EEG)-based brain-computer interface (BCI) systems from synchronous laboratory conditions to real-world applications and situations demands the continuous detection of brain patterns in which the user is in control of the timing and pace of the BCI instead of the computer. A self-paced BCI requires continuous analysis of the continuing brain activity, however, not only the intentionalcontrol (IC) states have to be detected (e.g., motor imagery and imagination) but also the inactive periods, where the user is in a non-control state (NC). The nonstationary nature of the brain signals provides a rather unstable input resulting in uncertainty and complexity in the control. Intelligent processing algorithms adapted to the task at hand are a prerequisite for reliable selfpaced BCI applications. This work presents a novel intelligent processing strategy for the realization of an effective self-paced BCI which has the capability to reduce noise as well as adaptation to continuous online biasing. A Savitzki-Golay filter has been applied to remove spikes/outliers while preserving the feature set structure. An anti-bias system is introduced which readjusts the classification output based on the brain’s current and previous states. Furthermore, a multiple threshold algorithm is applied on the resultant unbiased classifier output for improved accuracy. These algorithms are tested on 4 real and 3 artificial datasets and results shown are considerably promising and demonstrate the significance of the proposed intelligent and adaptive algorithms.
ideal for a real time BCI application. In the case of an ideal asynchronous BCI system, no cue stimulus is used, and the subject can produce whenever she/he wishes, a specific mental activity. The ongoing brain signals have to be analyzed and classified continuously. Mental events have to be detected and discriminated from noise and nonevents and transformed into a control signal as quickly and accurately as possible. In this work we present novel adaptation methods to tackle the outliers/spikes, classification latency, classification biasing and multiple thresholds for reduced error rates. Furthermore, subject-specific frequency bands are selected to remove unwanted information using Particle Swarm Optimization (PSO) [6] and eigenvector filtration is applied to selected datasets for noise reduction [7]. The remainder of the paper is organized as follows. Section II contains details on the BCI Competition IV multichannel dataset I and acquisition procedure. Common Spatial Patterns (CSP) for logical data mapping, eigenvector filtration for noise reduction, PSO for subject specific frequency band selection, Savitzki-Golay filtering for removing the outliers and smoothing, an anti-biasing approach to remove classifier biasing and multiple thresholds for eventual decision are discussed in section III. Results are presented and discussed with a short conclusion in section IV.
Keywords; Self-paced BCI, multiple thresholding, anti-biasing.
I.
II.
INTRODUCTION
A brain-computer interface (BCI) transforms a user's intentions towards an external device or neural prosthesis or it may be used to control a functional electrical stimulation (FES) device, not requiring any muscular response outputs [1]. The goal of such an interface is to provide effective communication by accepting commands directly encoded in neurophysiological signals. The performance of a BCI system depends on the parallel interaction of two adaptive controllers, the user, who must maintain a brain state correlation between his or her objective, and the BCI, which must translate the user’s intensions into device commands [2]. A BCI system provides a mode of communication based on EEG, ECoG or MEG signals such as evoked or spontaneous EEG features (e.g. SCPs, P300, mu/beta rhythms) or cortical neuronal activity and can be divided into two basic systems working in self-paced or synchronized mode [2] - [4]. The majority of BCI systems work on synchronously recorded spontaneous EEG with cue stimulus information provided. These systems use the continuing EEG in predefined time windows in which the imagination or other mental activity has occurred and discard the information elsewhere. These asynchronous systems are not
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DATA AQUISITION
The dataset used in this analysis is dataset I provided for the 4th International BCI Competition [8]. The recording was made using BrainAmp MR plus amplifiers and an Ag/AgCl electrode cap. Signals from 59 EEG positions most densely distributed over sensorimotor areas were measured. Signals were band-pass filtered between 0.05 and 200 Hz and then digitized at 1000 Hz with 16 bit (0.1 uV) accuracy. [8][9] These data sets were recorded from healthy subjects. In the whole session motor imagery was performed without feedback. For each subject two classes of motor imagery were selected from the three classes left hand, right hand, and foot (side chosen by the subject; optionally also both feet) [9]. A. Calibration data In the first two runs, arrows pointing left, right, or down were presented as visual cues on a computer screen. Cues were displayed for a period of 4s during which the subject was instructed to perform the cued motor imagery task. These periods were interleaved with 2s of blank screen and 2s with a fixation cross shown in the center of the screen. The fixation cross was superimposed on the cues [9].
Figure 1.
Timing paradigm for training.
B. Evaluation data The next 4 runs were used for evaluation. Here, the motor imagery tasks were cued by soft acoustic stimuli (words left, right, and foot) for periods of varying length between 1.5 and 8 seconds. The end of the motor imagery period was indicated by the word stop [9]. III.
METHODOLOGY
CSP can be applied to raw EEG datasets to filter the original data using an optimally designed CSP filter and thus produce a surrogate data space for the discrimination of two populations [6][10]. The log of the variance in the surrogate space has been used as features which are classified using Linear Discriminant Analysis (LDA). For choosing subjectspecific frequency bands the efficient search algorithm Particle Swarm Optimization has been applied [6]. Furthermore, eigenvector filtration has been applied to subjects whose error rates are considerably high during cross-validation for noise suppression [7]. Furthermore, on the continuous classification output, Savitzki-Golay filtering is applied which smoothes the classifier output to remove spikes or outliers. Classification latency has been addressed using multiple adaptive thresholds which are chosen using a gradient based threshold switch. In addition, biasing has been removed making use of the past classifier outputs to remove the DC Bias and bring the instantaneous mean of the classifier output to zero. The following subsections give a brief description to each aspect of the above processing methods. A. Common Spatial Patterns CSP filtering involves linearly projecting the multichannel EEG data into a new data space by a weighted summation of the appropriate channels. This projection is based on the simultaneous diagonalization of the covariance matrices from both classes [6][10][11]. In Matlab this can be simply implemented as: W= eig ( ∑a, ∑a + ∑b )
(1)
where, ∑a and ∑b are the normalized covariance matrices of the two classes. The trial X can be projected using the mapping matrix W as: Z=W×X
(2)
By construction, the variance for a left movement imagination is largest in the first row of Z and decreases for the subsequent rows [10]. The opposite is the case for a trial with right motor imagery. The appropriate number of eigenvectors from both sides is chosen as filters; generally between 2 to 6 from either side of the eigenvector matrix is optimal [7]. For feature extraction, the log-variance of the windowed surrogate data is used as the feature vector for classification. V= log{(var( Z )}
(3)
Figure 2.
Eigenvector optimization of CSP Vector 1 for Subject 2
B. Eigenvector Filtration Eigenvector filtration is applied to subjects whose classification accuracy is significantly lower (
