Using Coherence for Robust Online Brain-Computer Interface (BCI ...

Using Coherence for Robust Online Brain-Computer Interface (BCI) Control Martin Sp¨ uler1 , Wolfgang Rosenstiel1 , and Martin Bogdan1,2 1

2

Wilhelm-Schickard-Institute for Computer Science, University of T¨ ubingen, Germany Computer Engineering, University of Leipzig, Germany

Abstract. A Brain-Computer Interface (BCI) enables the user to control a computer by brain activity only. In this paper we investigated the use of different brain connectivity methods to control a Magnetoencephalography (MEG)-based Brain-Computer Interface (BCI). We compared the use of coherence, phase synchronisation and a widely used method for spectral power estimation and found coherence to be a more robust feature extraction method, when using the BCI over a longer time interval across sessions. To validate these results we implemented an online BCI system using coherence and could show that coherence also performed more robust in an online setting than traditional methods. Keywords: Brain connectivity; Brain-Computer Interface (BCI); Coherence; Magnetoencephalogrpahy (MEG); non-stationarity.

1

Introduction

A Brain-Computer Interface (BCI) provides a user with the means to control a computer by pure brain activity [1]. Its main purpose is to restore communication in people who have lost voluntary muscle control due to neurodegenerative diseases or traumatic brain injuries. The basic principle of a BCI relies on the user being able to voluntarily alter his brain activity. These changes in the recorded brain activity can be detected and used as an input signal for a computer. There are different signal acquisition techniques that allow to measure the brain activity of a user. While Electroencephalography (EEG) is the most commonly used method for recording brain activity, we focus on Magnetoencephalography (MEG) in this paper. While MEG has been shown to work well with BCI [2], it is rarely used, which may be attributed to the lack of portability and its high costs. On the bright side, MEG offers a higher spatial resolution and more information in the higher frequency range above 40 Hz compared to EEG [3], since the magnetic field is not distorted by skull and scalp. Regardless of the underlying recording technique, the power spectrum is the feature that is most commonly used in motor imagery BCIs [4]. If a user imagines a one-sided hand movement, a power decrease in the mu rhythm (8-13 Hz) can be detected over the contralateral motor cortex. Methods for estimating brain connectivity like coherence or phase synchronisation have been shown as V.M. Mladenov and P.C. Ivanov (Eds.): NDES 2014, CCIS 438, pp. 363–370, 2014. c Springer International Publishing Switzerland 2014

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alternatives for detection of motor imagery [5]. While the power spectrum only yields information about the local brain activity, connectivity methods allow to capture the dynamics between different brain regions and thereby may give additional information that could be used to detect the user’s intention. That connectivity methods can also be used as a feature extraction method for classification in BCI has been shown in several publications with EEG [6,7,8,9], but so far only Bensch et al. [10] have investigated the use of connectivity methods for MEG-based BCIs. More importantly, none of them has addressed the robustness of those connectivity methods for feature extraction. The robustness of the features used is important because of the non-stationary nature of the recorded brain signals, which is still a critical issue in current state-of-the-art Brain-Computer Interfacing [11]. This non-stationarity especially is a problem when a classifier trained on data of a previous session is used for classification in a current session, which is often referred to as the session-transfer problem. While adaptation is one way to counter non-stationarity [12], the use of more robust features is another way to alleviate the problem of non-stationarity. In this paper we evaluate connectivity methods like coherence and Phase Locking Value and show how robust they are across sessions in an online MEG-based BCI.

2

Methods

In this chapter, we will first describe the three different methods for feature extraction that were evaluated in this paper. Afterwards we will describe the data and methods we used for the offline analysis of MEG data and the online experiment. 2.1

Feature Extraction Methods

To better classify the brain signals, feature extraction methods can be used to extract specific features of the signals, which allow for a better classification. In the following, we introduce three different feature extraction methods, that were used and evaluated in this paper. In the brackets, the corresponding abbreviation is given, which is used later in this paper to identify the different methods. Power Spectral Density Estimated by Autoregressive Model (AR). Estimating the power spectrum by means of an autoregressive model is a commonly used method for feature extraction in EEG- and MEG-based BCIs [2]. In this paper we used the Burg algorithm [13] for estimating the coefficients for the autoregressive model with a model order of 16. Coherence (COH). Given two signals sx (t) and sy (t), the coherence [7] can be derived from the cross-spectrum Sxy (f ) of the two time-series: Sxy (f ) = Xx (f )Xy∗ (f )

(1)

Robust Online Brain-Computer Interface (BCI) Control

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with Xx (f ) being the Fourier transform of sx (t) and Xx∗ (f ) being the complex conjugate of the Fourier transform sx (t). The expectation operator is denoted by . The complex coherence can be obtained by normalizing the cross-spectrum with the two spectra of the corresponding signals: Sxy (f ) Cxy (f ) = Sxx (f )Syy (f )

(2)

By taking the absolute value of the complex coherence, the coherence can be calculated: Cohxy (f ) = |Cxy (f )| (3) To obtain a scalar value, the average coherence in a specified frequency band or the whole frequency range can be calculated. The result is a value between 0 and 1 with 0 meaning that the frequency components of both signals are not correlated. Phase Locking Value (PLV). Given two signals sx (t), sy (t) and their corresponding phases ϕx (t), ϕy (t) the Phase Locking Value (PLV) [7] describes the stability between ϕx (t) and ϕy (t). It can be computed across subsequent time samples with: (4) P LV = |ej(ϕx (t)−ϕy (t)) | where denotes the expectation operator. Therewith the PLV equals the absolute mean over all ej(ϕx (t)−ϕy (t)) in one window. For a randomly distributed phase difference (between [0, 2π]), the PLV will be 0, while it will be 1 for a constant phase difference (phase synchronisation). The Hilbert transform can be employed to compute the instantaneous phase ϕ(t). For computation of the PLV we used the improved algorithm published in [6]. 2.2

Offline Analysis of MEG Data

Data and Tasks. To evaluate the use of the different methods, we performed an offline analysis on data recorded for a previous study [14]. In this study 10 subjects performed different mental tasks in two sessions. In each session 51 trials were recorded per task. Recording was done with a 275-channel wholehead MEG-system (VSM MedTech Ltd.) at a sampling rate of 586 Hz. Since it was shown in [14] that right hand and subtraction were the two mental tasks that could be classified with the highest accuracy we concentrated on data from these two tasks. In the task right hand the subject had to imagine a right hand movement and in the subtraction task the subject had to do subtractions by choosing a random number (around 100) and subtract 7 repeatedly until the end of the trial. Each trial lasted 4.05 seconds with about 6 seconds of break between the trials. Instructions were given on a screen and a fixation cross was displayed during trials to minimize eye movement.

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Signal Processing and Classification. Before classification, the signals were filtered and resampled to 200 Hz. For spatial filtering a small Laplacian derivation was applied. To reduce the number of channels we only used the 185 inner channels, since the outer channels are supposed to hold little task-related information and are more influenced by possible artifacts. After the preprocessing, we used the three different feature extraction methods AR, COH and PLV as previously described. To reduce the computational load in favor of later online applications of the BCI, we wanted to use the same default parameters for each session and each subject. To estimate the best default parameters for each feature extraction method we tested different frequency ranges by cross-validation and selected the frequency range that gave the best accuracy for each method. For the AR method we found the range of 1 to 40 Hz in 2 Hz bins to yield the highest accuracies, which is why AR was used with this frequency range. For coherence the frequency range of 2.5 Hz to 15 Hz was chosen, while the frequency range was not limited for PLV. Before classification we did a feature selection based on r2 -values [15] and used the 1000 features with the highest r2 -values for classification. For classification we chose a Support Vector Machine (SVM) [16], using the LibSVM [17] implementation with standard parameters (RBF-Kernel, C = 1, γ = 0.001). SVM was used for classification, since it was shown to be superior to other classification methods on BCI data [18]. To estimate classification accuracies, we used two different approaches. The first one was a a 100x2-fold cross-validation where the data from session 1 and session 2 are mixed together. The data is then randomly permuted and partitioned in 2 blocks with equal size. Each block is used for training the classifier once and tested on the other block. This procedure is repeated 100 times with different permutations and the total accuracies are averaged. For the second approach we used the first session for training the classifier and the second session to test the classifier and evaluate its accuracy (referred to as S1S2-validation later). Since the data is randomly mixed for the cross-validation, the influence of non-stationarity on the classification accuracy is very small. The contrary is the case for the S1S2-validation. Thereby the comparison of both validation-techniques gives us a method to assess the robustness of the tested feature extraction methods. The number of 2 blocks for the cross-validation was chosen to have exactly the same amount of data in the training and testset as we have in the S1S2-validation. 2.3

Online Experiment with MEG

To validate the results from the offline analysis of the MEG data (will be shown in the results section), we performed an online experiment, where we recruited 10 subjects (mean age 28.2 ±2.6, 5 female, 5 male) with only 2 of them having previous experience with a motor imagery BCI. The study was approved by the local ethics committee of the Medical Faculty at the University of T¨ ubingen and written consent was obtained from all subjects. Each subject participated in two sessions. The first session was used to record training data without feedback. The second session was used for testing and feedback was given to the subject.


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Recording was done with a 275-channel whole-head MEG-system (VSM MedTech Ltd.) at a sampling rate of 586 Hz. During measurement, the head position relative to the MEG sensors was continuously monitored and saved along with the MEG data. Due to a slightly different technical setup we had to use a different block size than during the recording of the data used for the offline analysis. This resulted in slightly different time intervals for trials and breaks. First a cue was given, what task should be performed next. Then a symbol was displayed to indicate that the trial starts in less than a second. During the trial, the subject should perform the mental task and a fixation cross was displayed to the subject to minimize eye movements. After the trial a 3 second break followed. If feedback was given online, this was done after the trial in the first 2 seconds of the relax period. The classification for the online feedback was done using coherence as feature with the same preprocessing and classification methods as described previously. Similar to the offline analysis, only the 185 inner channels were used in the online experiment. The calculation of the different feature exctraction methods for 1000 features and 185 channels took on average 103 ms for AR, 123 ms for COH and 394 ms for PLV. To compare the online results using COH, we also did a simulated online experiment with AR and PLV as feature. To appropriately simulate the online case, all parameters and data were exactly the same as in the online experiment, except the different feature extraction methods that were tested. For comparability, we used the same default parameters as for the previous offline analysis.

3 3.1

Results Offline Analysis of MEG Data

The results for the 100x2 cross-validation are shown in table 1. When using AR as feature, an average accuracy of 87.3 % (±7.6 %) was achieved. When using COH and PLV, an average accuracy of 87.0 % (±9.7 %) and 87.4 % (±8.3 %), respectively, was achieved. In contrast to the cross-validation, COH and PLV perform better than AR in the S1S2-validation, in which the classifier was trained with data from session 1 and tested with data from session 2 (see table 1). While an average accuracy of 79.2 % (±9.9 %) was reached when using AR, using COH and PLV for classification resulted in an average accuracy of 84.8 % (±9.5 %) and 84.2 % (±7.1 %), respectively. 3.2

Online Experiment with MEG

The results from the online experiment as well as the simulations using AR are shown in table 2. At first it is notable, that subjects BI and BJ were not able to control the BCI and achieved an accuracy around or below chance level (50 %). During the online experiment, in which COH was used for feature extraction, an average accuracy of 73.0 % (±16.4 %) was achieved. When subjects BI and BJ

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Table 1. Classification accuracies with different feature extraction methods obtained by a 100x2-fold cross-validation (on the left) and when using session 1 for training and session 2 for testing (S1S2-validation; on the right) 100x2-fold cross-validation subject AR COH PLV AA 87.2 % 75.9 % 75.4 % AB 83.6 % 90.5 % 90.9 % AC 87.4 % 88.2 % 86.8 % AD 86.1 % 71.0 % 79.1 % AE 89.7 % 93.2 % 92.7 % AF 84.0 % 91.8 % 90.3 % AG 93.0 % 93.0 % 92.3 % AH 96.2 % 97.4 % 97.2 % AI 69.6 % 73.7 % 74.2 % AJ 95.8 % 95.1 % 94.9 % mean 87.3 % 87.0 % 87.4 %

S1S2-validation subject AR COH PLV AA 79.4 % 74.5 % 74.5 % AB 82.3 % 87.2 % 88.2 % AC 74.5 % 91.1 % 84.3 % AD 83.3 % 69.6 % 83.3 % AE 68.6 % 88.2 % 78.4 % AF 64.7 % 81.3 % 87.2 % AG 93.1 % 93.1 % 88.2 % AH 76.4 % 97.0 % 90.2 % AI 73.5 % 73.5 % 72.5 % AJ 96.0 % 92.1 % 95.1 % mean 79.2 % 84.8 % 84.2 %

are disregarded, due to their inability to control the BCI, an average accuracy of 79.3 % (±10.8 %) was achieved for COH while AR resulted in an average accuracy of 70.8 % (±12.9 %). When pooling the results of the 10 subjects from the offline analysis with the results of the 10 subjects from the online MEG experiment, a one-sided Wilcoxon signed rank test shows that COH and PLV both perform significantly better (p < 0.05) than AR. Table 2. Online accuracies with coherence (COH) as feature, simulated online accuracies with power spectrum (AR) and PLV as feature and days between session 1 and session 2. Since subjects BI and BJ did not achieve significant BCI control, also the average accuracy without these subjects is given.

subject BA BB BC BD BE BF BG BH BI BJ mean mean (without BI, BJ)

AR 82.5 51.0 84.0 80.0 52.5 76.5 67.5 72.5 50.0 50.0 66.7 70.8

% % % % % % % % % % % %

COH 88.0 % 87.5 % 87.5 % 88.0 % 76.0 % 62.5 % 64.0 % 81.0 % 52.0 % 43.5 % 73.0 % 79.3 %

PLV 82.5 % 71.0 % 90.5 % 81.0 % 63.5 % 71.0 % 61.0 % 69.0 % 59.0 % 46.5 % 69.5 % 73.7 %

days between S1 and S2 5 2 4 6 5 6 2 1 1 0 3.2 3.9


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Discussion and Conclusion

This study aimed at evaluating the robustness of different feature extraction methods in light of the session-transfer problem. To do this, we analyzed MEG data offline using a cross-validation and a S1S2-validation. The difference between the results obtained by cross-validation and the results obtained by S1S2validation gives an estimate of the performance-drop due to the session-transfer. Because of the session-transfer, there is a covariate shift [19] of the data during the S1S2-validation, but not during the cross-validation. Therefore, one can use the difference between the cross-validation results and the results obtained by S1S2-validation as a measure for the robustness of a feature in regard of the session-transfer problem. Using this method to assess the robustness of the feature extraction methods, we could show in an offline analysis on MEG data that the connectivity measures coherence (COH) and PLV seem to be more robust features than the power spectrum (AR). To validate these results, we performed an online experiment, where COH was used as feature for online feedback. Using the data from the online experiment, we also simulated the online scenario with AR and PLV as feature extraction methods and could validate the results from the offline analysis that connectivity measures are more robust features on MEG data. For the online experiment it is also noticeable that 2 subjects did achieve online accuracies below 60 %, which is not sufficient for communication. This finding agrees with earlier results by Vidaurre et al. [20], who state that BCI control does not work for 15 % to 30 % of the subjects. As a conclusion, we have shown coherence and PLV to be more robust features in an MEG-based BCI, that help to alleviate the session-transfer problem. We have also demonstrated that coherence can be utilized for online BCI control. Further studies are needed to also evaluate the robustness of those brain connectivity methods for EEG-based BCI and to investigate the reasons for connectivity methods being affected less by non-stationarity. Acknowledgments. This study was granted by the Deutsche Forschungsgemeinschaft (DFG, Grant RO 1030/15-1, KOMEG).

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