Feasibility of Using Low-Cost EEG Acquisition ...

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Feasibility of Using Low-Cost EEG Acquisition Devices for Motor Imagery BCIs Sumit Soman, Soumya Sen Gupta, P Govind Raj Center for Development of Advanced Computing, Noida, India Email: {sumitsoman, soumyasengupta, pgovindraj}@cdac.in

Abstract—An important component of Brain Computer Interface (BCI) systems is the acquisition of Electroencephalogram (EEG) signals. The ergonomics and cost of the acquisition system play an important role in determining the feasibility of the system developed. In this paper, we consider motor-imagery based BCI systems that require EEG signals from electrodes placed over the motor cortex of the scalp. We used the low-cost, wireless Emotiv EEG headset, but found that its electrode positions are not suitable for such systems. However, we find that re-positioning of the electrodes of the Emotiv system gives better accuracy on offline data, hence making it suitable for such BCI systems. We also evaluate the performance of two feature extraction methods, CSP and WOSF, and infer that the accuracy of the BCI system depends not only on the processing pipeline, but also the proficiency of the user in performing motor imagery. Index Terms—Brain Computer Interface, Low cost BCI, CSP, WOSF, Motor Imagery

I. I NTRODUCTION A BCI system allows users to control external systems by voluntary variations in brain activity, captured by EEG. There are several types of BCI systems, based on the control paradigm used, as illustrated in Figure I. For instance, motor imagery BCI systems are controlled by the user imagining motor movements (such as movement of the limbs), and these are translated to control actions such as movement of a mouse cursor on a desktop [1] or wheelchair movement. Of late, these systems have acquired importance as they can be used by people suffering from movement disorders such as locked-in syndrome, owing to their brain activity being intact.

Fig. 1.

Control Paradigms for BCI Systems

The Center for Development of Advanced Computing

(CDAC), Noida is involved in a project which aims at developing assistive technology using non-invasive BCI. The project aims at developing BCI-based applications for controlling User Desktop and wheelchair, that could be used by patients suffering from locomotive disorders. For clinical inputs, CDAC is coordinating with doctors and neurotechnologists at the Department of Neurology at Nizam’s Institute of Medical Sciences(NIMS), Hyderabad. The project envisages to use the motor-imagery paradigm to enable users to operate the system. BCI systems have three primary components, responsible for signal acquisition, processing (including feature extraction and classification) and translation into user command (the end-user application). The signal acquisition component essentially comprises of an EEG acquisition system. Typically, EEG acquisition systems have been in use in the clinical domain for monitoring brain activity in patients suffering from neurological disorders. These systems are bulky as they have a bunch of wires that connect the electrodes from the cap worn on the scalp to the EEG amplifier. In the present form, these are unsuitable to be used in BCI systems as they are not portable and convenient for long-term usage. BCI systems require EEG headsets that are portable and easy to wear. A comparison of EEG headsets used in BCI systems is shown in Table I [2]. TABLE I A C OMPARISON OF EEG HEADSETS Device MindWave

Price 99.95

Electrodes 1

Mindflex

50

1

Emotiv

299

14

Star Wars Force Trainer MindSet

45 199

1 1

Neural Impulse Actuator

99

3

MindBall Xwave Headset MyndPlay BrainBand

20,000 90 158

1 1 1

Sensors Interpret 2 mental states; eyeblinks 1 mental state 4 mental states; 13 thoughts; facial expressions; head movements 1 mental state 2 mental states; eyeblinks 2 brainwaves (Alpha, Beta); facial muscle and eye movements 1 mental state 8 EEG bands 8 EEG bands

One such low-cost EEG headset is the 14-channel Emotiv headset. It has electodes in the frontal and occipital regions of the brain. However, motor imagery can be captured by EEG signals acquired from electrodes placed in the cortical region of the brain, such as in the C3 and C4 positions, in

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accordance with the 10-20 System for electrode placement[3]. This suggested that repositioning the electrodes of the Emotiv system could affect the classification accuracy of the BCI system. Our work aims at conducting experiments to validate this claim. The implications of the results would enable this wireless, low-cost device to be used in BCI systems, as well as develop more reliable BCI systems. The next stage of the BCI pipeline is that of signal processing, which includes feature extraction and classification. The objective of a feature extraction technique is to convert the high-dimensional EEG data into a lower-dimensional feature space, wherein only the essential components of the EEG signals are retained. This is done in order to build an efficient classifier in the feature space, which would otherwise have been difficult in the original high-dimensional space. A feature extraction algorithm is chosen keeping in mind the computational complexity in computing the features, and the classification accuracy obtained. Therefore, the feature extraction technique is critical as it would affect the performance and accuracy of the BCI system. In this regard, we use the feature extraction technique of Common Spatial Patterns (CSP) [4] along with a more robust technique called Wavelength Optimal Spatial Filter (WOSF), that has been recently proposed by Lotte et al. [5] We demonstrate that a combination of these feature extraction techniques provides better classification accuracy for datasets of BCI Competitions. The paper is organized in the following sections. Section II discusses the signal acquisition component, particularly the re-positioning of Emotiv electrodes. Section III details the feature extraction techniques used in the BCI pipeline. Section IV enumerates the results obtained. Section V provides the conclusions drawn and future work. II. S IGNAL ACQUISITION : R E - CONFIGURING THE E MOTIV H EADSET ELECTRODE POSITIONS This section provides details regarding the signal acquisition component of the BCI system. We use the 14-channel Emotiv headset, that has electrodes at the following positions: AF3, AF4, F3, F4, F7, F8, FC5, FC6, P3, P4, P7, P8, T7, T8, O1 and O2. These are suitable for capturing eye-blinks, lateral eye movements and alpha rhythms efficiently. The key advantages of using the Emotiv headset are its low-cost, portability and convenience. However, literature suggests that the phenomenon of motor imagery is observed due to Event-Related Desynchronization (ERD) in the cortical regions of the brain, the seminal work being [6]. With our experiments too, we infer that the Emotiv system is sub-optimal in capturing motor imagery EEG signals due to absence of electrodes in the cortical region. Therefore, we detach two electrodes from the Emotiv headset to make them flexible, from the frontal region, and then re-position them over the cortical region over electrode positions C3 and C4. A comparison of the original Emotiv configuration and the re-positioned configuration is shown in Figure 2. From our experiments, we observe that the modified configuration gives better results than the original electrode positioning of the Emotiv headset, when using the feature extraction

(a) Original

(b) Re-positioned

Fig. 2. Comparison of the original and re-positioned electrodes of the Emotiv Headset

techniques of CSP and WOSF. The Emotiv headset performs very basic signal processing that includes low pass filtering of the EEG data with a cut-off frequency at 85 Hz, followed by a high pass filter with a cut-off at 0.16 Hz. Finally, a notch filter is applied at 50-60 Hz. Our signal acquisition paradigm consists of motor-imagery experiments. In such an experiment a visual stimulus in the form of an arrow pointing either towards left or towards right was presented to the user. The user was asked to imagine a movement of the right or the left hand corresponding to the direction of the arrow. The EEG signals of the users during the trials were recorded. These experiments consisted of multiple trials wherein in each trial a stimulus in a particular direction was shown. The users were made to sit and look at a computer monitor placed at eye level. Each user wore an Emotiv headset. Each trial started with the presentation of a fixation cross at the center of the computer screen. After 1 second the fixation cross was overlaid with an arrow at the center of the monitor for 1.25 seconds, pointing either to the right or to the left (”cue”). Depending on the direction of the arrow, the subject was instructed to imagine a movement of the right or the left hand. The sequence of right and left trials, as well as the duration of the breaks between consecutive trials (ranging between 1.5 and 3.5 sec.), was randomized. The experiment on a subject comprised of 40 trials each (20 left and 20 right). We discuss the feature extraction techniques in the following section. III. F EATURE E XTRACTION IN BCI S YSTEMS This section details the various feature extraction techniques used. As stated earlier, feature extraction aims at representing the EEG signal in a lower dimensional feature space, such that only the essential components are retained. The input to a feature extraction algorithm is the band-filtered EEG signal corresponding to trials for various classes. A class refers to the various states that need to be identified by the BCI system, for instance left or right hand movement. A trial corresponds to the EEG data acquired during training, when the subject imagines the movement of the left/right hand based on the stimulus provided. The output of the feature extraction algorithm is the

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EEG data in feature space, which are then used to train the classifier. A. Common Spatial Patterns (CSP) CSP is a very common technique used for feature extraction for BCI systems. It essentially performs the joint diagonalization of the covariance matrices of the different classes of signal data and gives as output a new set of signal data whose variances contain the most discriminative features related to the different classes of EEG signals. It has been widely used as it provides better results over other available spatial filters like bipolar filter, Common Average Reference (CAR) filter, Laplacian filter etc [7]. The steps for implementing CSP are as follows: 1. Given EEG data Xa in the form [channels X samples] for trial a = 1, 2, ..., N , we compute the covariance matrix Ca as in equation 1 Ca = Xa ∗ XaT (1) where X T denotes transpose of X. 2. Compute the normalized covariance matrix Z for all N trials of a class using equation 2, where trace(X) denotes the sum of the diagonal elements of X. Za =

1 N

N X i=1

Cai trace(Cai )

(2)

3. Add the normalized covariance matrices for the two classes, say a and b, as shown in equation 3, to obtain the composite normalized transformation Z. Z = Za + Zb

(3)

4. Perform the eigenvalue decomposition of the composite normalized transformation as shown in equation 4. This is done using Schur transform to get the corresponding eigenvalues and eigenvectors. U λU T = schur(Z)

(4)

where λ is the diagonal matrix of eigenvalues and U is the matrix of eigenvectors corresponding to the eigenvalues. 5. Compute the whitening transformation matrix P using equation 5. P = λ−0.5 U T (5) 6. The covariance matrices are then transformed as in equation 6 and decomposed using Schur transform as in equation 7. Sa = P Za P T V λa V

T

= schur(Sa )

7. The eigenvectors in V corresponding to k maximum and minimum eigenvalues are found from λa , as in equation 8. The spatial filter and its transpose are then computed as shown in equations 9, 10. V → Vk SFa =

(8)

VkT P

(9) T

SPa = (SFa )

(10)

where SFa is the spatial filter for class a and SPa is the corresponding spatial pattern. 8. The features Fs for a trial of a class s are obtained from EEG Xa as shown in equation 11. Fs = log(var(SFa Xa ))

(11)

Similarly, features are obtained for all trials for the various classes and then used to train a classifier. The primary advantage of using CSP is that it addresses the problem of spatial blurring due to volume conduction [8]. However, CSP is highly sensitive to artifacts; even a single high amplitude artifact can bias the training of spatial filters. B. Wavelength Optimal Spatial Filter (WOSF) This section discusses the use of WOSF as a feature extraction technique, which aims at designing an optimal spatial filter based on Wavelength (WL) features. It provides a measure of the complexity of a signal, in terms of the cumulative length of the signal analyzed [5]. To understand the difference between the CSP and WOSF techniques, we compare the objective functions for both. Formally, CSP filter (SF ) aims to extremize the objective function in equation 12, where Xi is the EEG data for a trial of class i. ||SF T X1 ||22 (12) JCSP (SF ) = ||SF T X2 ||22 However, in case of WOSF, the objective function is redefined as in equation 13, where X i:j denotes the EEG signal matrix X with only rows i to j. JW OSF (SF ) =

||SF T X12:N − SF T X11:N −1 ||2 ||SF T X22:N − SF T X21:N −1 ||2

(13)

This feature measures the cumulative length of the EEG signal analyzed, as indicated by the term ∆X = X12:N − X11:N −1 . To compute WOSF features, we proceed as follows: 1. Compute ∆X for each trial of EEG data Xi as indicated in equation 14. ∆X = Xi2:N − Xi1:N −1

(14)

2. Compute the spatial filter and WOSF features as in CSP, using equations 1 to 11, using ∆X instead of X.

(6) (7)

where λa and V are the eigenvalue and eigenvector matrices after the eigenvalue decomposition of Sa .

3. Combine the feature sets using CSP and WOSF to train the classifier. As suggested in [5], we follow the approach of combining the CSP and WOSF features as both methods would lead to a

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different set of features; and WOSF features are spatially more focussed. These features are then sent to a Support Vector Machine classifier which translates them into class labels. These can then be used as intents to control an external system. IV. A NALYSIS AND R ESULTS ON EEG DATA This section discusses the results obtained along with an analysis of the same. We discuss the effect of using a combination of CSP and WOSF features on datasets from BCI Competitions in sub-section IV-A and the classification accuracies using the Emotiv headset’s original and modified configuration in sub-section IV-B respectively. A. Results on BCI Competition Datasets We used the datasets of BCI Competition III, dataset IIIb [9], which corresponds to cued motor imagery for 3 subjects, consisting of 270 trials per class for two subjects and 170 trials per class for the third. The EEG data has been recorded using two bi-polar channels, sampled at 125 Hz. It was filtered between 0.5 and 30Hz with notch filter on. The data was available in the gdf format [10]. We compare the classification accuracies of the feature extraction techniques in Table II. We observe that the WOSF along with CSP feature extraction techniques gives at least comparable or higher classification accuracies than those obtained by using CSP alone. This is evident from the results for subjects X11b and S4b. This can be attributed to the fact that CSP is prone to spatial blurring and is not able to capture the variances in the signal effectively. On the other hand, WOSF is a measure of the cumulative length of the signal as it is based on the difference between successive samples of the signal. Hence it captures the complexity of the EEG signal more effectively. Subject O3VR X11b S4b BCI II

CSP 85.62±8.22 67.40±4.35 55.55±7.03 79.69±9.53

CSP + WOSF 85.31±8.46 76.29±5.50 66.29±7.55 79.28±7.29

TABLE II C OMPARISON OF ACCURACIES USING CSP AND WOSF F EATURES

From this, we can infer that the feature extraction technique used in a BCI system plays a vital role in the classification accuracy obtained which affects the efficiency of the system. Also, the variation in results for multiple subjects shows that the accuracy obtained is also subject-dependent. This is in conformance to the view that there is inter-subject variability in case of motor imagery trials. In other words, the ability of the subject to generate µ rhythms responsible for ERD in motor imagery also plays a significant role in the usage of the BCI system. We now proceed to discuss the results of datasets obtained from the Emotiv headset for its original configuration as well as the re-positioned configuration with electrodes in the cortical area.

B. Accuracies Using Emotiv Headset EEG Data In this section, we present the results and analysis of the effect of changing the electrode configuration of the Emotiv headset, in the context of motor imagery experiments. We use datasets for 3 subjects, that have been acquired by using the original and modified electrode configurations of the Emotiv headset. The subjects EEG data was collected using the motor imagery paradigm discussed previously. We also compare the feature extraction techniques discussed on these datasets. The results are presented in Table III. TABLE III C LASSIFICATION ACCURACIES FOR THE ORIGINAL AND MODIFIED E MOTIV CONFIGURATION USING DIFFERENT SET OF FEATURES Subject

Subject 1 Subject 2 Subject 3

Classification Accuracy (%) Original Emotiv Config New Emotiv Config CSP CSP+WOSF CSP CSP+WOSF 62 65.5 70 74.5 68.5 67.5 77.5 82.5 60 66.5 64.5 69

From the results, we observe that there is an increase in the classification accuracy when using the Emotiv headset with electrodes re-positioned over the cortical areas. This is expected as the signals pertaining to motor imagery are obtained from the motor cortex, and hence electrodes are needed in the cortical region to capture the decrease in µ rhythms during ERD. V. C ONCLUSION AND F UTURE W ORK The paper presented the use of a low-cost EEG headset in developing motor imagery BCI systems. Though the original positioning of the electrodes was not found suitable for this paradigm, re-positioning them gave us higher classification accuracy. Also, we find that there is an improvement in classification accuracy as compared to using the CSP technique alone. This shows that the feature extraction technique chosen in the processing pipeline of a BCI system is crucial as it directly affects the classification accuracy. Both these results have contributed towards building a better and cost-effective EEG system. Future work involves development of techniques to improve the quality of EEG signals obtained from the re-positioned Emotiv headset and comparing them with the EEG signals obtained from a standard EEG amplifier. It may be noted that the effect of the electrode re-positioning on the impedance of the electrodes has not been considered in this study. Techniques for improving the same need to be further investigated. The ergonomics of the EEG headset used is also important. The repositioned Emotiv electrodes need to be securely placed over the scalp to prevent their displacement during EEG recording. For this, we used crepe bandage to tie the electrodes in place. Better design technique in this regard needs to be formulated. R EFERENCES [1] S. Sengupta, S. Soman, P.G. Raj, R. Prakash, S. Sailaja, and R. Borgohain. Detecting eye movements in eeg for controlling devices. In IEEE International Conference on Computational Intelligence and Cybernetics, CYBERNETICSCOM 2012, 2012.

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