Using Linear Discriminant Function to Detect Eyes Closing Activities through Alpha Wave Denis Delisle-Rodriguez1,2, Javier F. Castillo-Garcia1, Teodiano Bastos-Filho1, Anselmo Frizera-Neto1, Alberto Lopez-Delis2 1
Post-Graduate Program of Electrical Engineering, Federal University of Espirito Santo, Vitoria, Brazil 2 Center of Medical Biophysic, University of Oriente, Santiago de Cuba, Cuba
[email protected],
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Abstract—This work presents an alternative method to detect events correlated to eyes opening and closing, based on electroencephalography (EEG) measured from the occipital lobe. The goal is to propose a method based on linear discriminant function to classify segments of EEG signals that contain activities originated by eyes closing. A linear discriminant function presented by Fisher is employed to detect these activities on segments of 2s. This method showed a good values of sensitivity (SE ≥ 85 %) and specificity (SP ≥ 60 %). This approach can be used to control the switching of a brain computer interface (BCI). Keywords—alpha rythm; BCI; EEG; linear discriminant analysis
I.
INTRODUCTION
Stroke and several neurological diseases have a high incidence in the world. Particularly, stroke has a morbidity of 20% respect to subjects that remain with severe movement deficits. Several technologies have been developed to assist and improve the communication of people with paralyze and severe motor disability. BCI is a communication system that does not depend on the brain's normal output pathways of peripheral nerves and muscles [1]. This way, electroencephalography signals (EEG) have been employed, which contain information that allows the eyes closure detection. Opening and closing eyes events can be detected on the occipital lobe by alpha wave in a frequency range of 8 Hz to 13 Hz. A high energy of the alpha wave corresponds to closed eyes on wake subjects (in 90% of healthy and people with disabilities) [2]. Alpha waves have been applied to operate electrical device [3], however, the automatic recognition associated to eyes opened (EO) and closed (EC) is not a trivial task, because the bandwidth of alpha wave is affected by natural variation and electrical noise, and muscle artefacts. Several methods have been developed to automatic detection of alpha wave, such as: analogue filtering and smoothing (AFS), peak detection and counting (PDC), power spectrum analysis (PSA), fractal dimension (FD), KM2O-Langevin and approximated entropy (ApEn) [4]-[7]. PSA shows a higher performance than AFS and PDC methods [4], but PSA method not always distinguish the difference of the alpha wave that corresponds to EO and EC events, because the threshold value can have been determinated at a higher level from the variance in the magnitude during EC [5]. Fractal
dimension improves the effectiveness threshold respect to PSA, because fractal value during EC is lower than during EO. Also, the KM2O-Langevin method has a higher performance than PSA, because it uses stationary condition into segments of the alpha wave during EO in wake subjects. Alternatively, ApEn method has a better performance than PSA during the discrimination between wake and drowsy subjects [7]. However, the compute of ApEn needs a priori determination of three user specified parameters [8], which are critical for computation. All aforementioned methods use a threshold value as a reference that depend on each subjects and experiment conditions. The aim of this work is to propose an automatic method based on linear discriminant analysis for recognition of eyes closing events based on the alpha waves of EEG in awake subjects, in order to activate of a BCI. This approach is compared to fractal dimension that presents a good performance respect to spectral methods, using the sensitivity and specificity indices. II.
MATERIALS AND METODHOS
A. Protocol The experimental protocol allows the acquisition of EEG signals in two events: opened eyes and closed eyes. Nine healthy subjects participated in the experiment and provided written informed consent. Each subject is sit near to visual (virtual animated eye) and auditory (tone) synchronized stimuli that guides the oscillation period necessary to open (remaining 3 seconds) and close (remaining 2 seconds) the eyes during 48 seconds. Five records of EEG signals were acquired to each subject. EEG signals were acquired from Emotiv EPOC neuroheadset using fourteen electrodes on the scalp [9], placed according to the international 10/20 system [10]: AF3, AF4, F3, F4, F7, F8, FC5, FC6, P7, P8, T7, T8, O1, O2. However, only the data from occipital alpha waves (8-13Hz) on the O1 and O2 sites were used. The occipital locations O1 and O2 were chosen for two reasons: (1) alpha activity is larger in the occipital region as it is directly linked to the visual perception; and (2) there is less artefact in this region, such as ocular muscle activity, compared to the frontal scalp regions. EEG channels were sampled with 128 Hz at 1.95µV least significant bit voltage resolution.
B. Proposed method The proposed method was designed for online processing, then segments of 2s on the desired EEG channel (in this case O1), with overlapping of 1s were used. Figure 1 shows the five structure of the segments formed during online processing. OE
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Fig. 1. Structure of segments taken during 2s of periods with overlapping of 1s. OE: opened eyes; CE: closed eyes.
Previously, this approach needs a training step to estimate the linear discriminant function [11]. For this, the database is divided in two sets: training and testing. Training set is formed by a second record of each subject, while testing set is formed by four records of each subject. Testing set is employed to evaluate the performance of the linear discriminant function when classifying closed eyes events on the EEG records. Determination of Linear Discriminant Function First, the EEG channels of the training set are pre-processed by a Common Average Reference filter. Second, the EEG channel captured on the occipital lobe (O1) is filtered by a bandpass elliptic filter (order five, bandwidth of 8 Hz to 13 Hz). Third, the O1 channel is employed to obtain the linear discriminant function by the Fisher method [11]. To obtain the linear discriminant function it is necessary to determine the learning matrix. The learning matrix is formed by two class: the class one corresponds to segments that contain activity of closed eyes (segments 3 to 5), and the class two corresponds to segments that not contain activity of closed eyes (segments 1 and 2). The learning matrix has a dimension (n1+n2) × p, where p are independent variables, and n1 and n2 are objects of the class one and two, respectively. Each segment was divided in two windows of 1s, to compute each one of the following values: root mean square amplitude, variance amplitude, temporal variability of peaks (TVP), peak of power spectrum of TVP, and total power of power spectrum of TVP. These values are employed as predictor variables (p=5×2) to represent the column of the learning matrix. Figure 2 shows the peak detection of the EEG signal and the power spectrum of TVP. Power spectrum of TVP was computed by Welch periodogram, employing Blackman windows, 50% overlapped. Temporal serie of peaks was sampled to four times (512 Hz) of the sample frequency of EEG signal. Finally, all values were used for determining a linear discriminant function, which can be computed by the following equations (1-3): = −
(1)
= −
(2)
1 2
=
,
(3)
where x1 and x2 are the mean vectors of the p predictor variables of the class one and two, respectively. S-1 is the covariance matrix of the classes, and x is the object to classify. Fisher assigns an object x to class one, if y is nearest to y1 that y2. If y > (y1+y2)/2, then y is nearest to y1. C. Evaluation Indices of sensitivity (SE) and specificity (SP) were employed to evaluate the performance of the proposed method, with a testing set. These indices can be computed by equations (4) and (5), respectively. =
(4)
= ,
(5)
where TP, FP, TN, FN are true positive, false positive, true negative and false negative, respectively. The segments or objects that contain activity of closed eyes, classified correctly as class one, are considered as true positive. However, segments not classified correctly are considered as false negative. The segments that contain only activity of opened eyes, classified correctly as class two, are considered as true negative. On the other hand, segments not classified correctly are considered as false positive. III.
RESULTS AND DISCUSSION
Table 1 shows the performance intra-subject of the proposed method to classify closed eyes events from the occipital lobe through EEG signals. Intra-subject analysis only is employed to the second record of one subject to obtain the learning matrix, and the others four records are used as testing set. The sensitivity index has good values (SE > 85%) to all subjects, although the specificity index presents a low values (SP > 60%). However, subjects number 2, 6, 8 and 9 have good values of sensitivity and specificity. This suggest that the classification performance of the proposed method can be improved with a higher number of records in the training set. TABLE I.
PERFORMANCE OF CLASSIFICATION BY LINEAR DISCRIMINANT FUNCTION INTRA-SUBJECT.
Subjects 1 2 3 4 5 6 7 8 9
Sensitivity (%) 100 94.59 100 86.49 86.95 100 86.48 100 97.3
Specificity (%) 60.71 84.61 60.34 65.11 60.65 91.89 61.22 90 85.37
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Fig. 2. Representation of the peaks of the alpha wave on EEG signals (left side), as well as their power spectrum of the temporal variability (right side). Black and blue lines correspond to the first and second windows of 1s, respectively. From top to bottom, segments 1 to 5, respectively.
The performance inter-subject of the proposed method was higher, with indices of sensitivity and specificity of 98.5% and 73.4%, respectively. In this case, the training set was formed by the second record of all subjects. For the same record, the dimension fractal method has indices of sensitivity and specificity of 40.54% and 63.66%, respectively.
should be evaluated on EEG records that contain activities correlated to eyes opening during 10s of periods and 2s of periods with closed eyes. This condition could improve the estimation and performance of the linear discriminant function.
Figure 2 presents the five segments of 2s formed into period of 6s. The row three shows a considerable change in the power spectrum of temporal variability of peaks, during the transition of eyes opening to eyes closing. This change may have arisen from synchronous and coherence (in phase generative) of the electrical activity of thalamic.
Authors would like to thank to CAPES and CNPq by their support to this research.
IV.
ACKNOWLEDGMENT
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CONCLUSION
Linear discriminant function can be employed to classify events of eyes opening and eyes closing through EEG signals measured on the occipital lobe. This method not need the definition of the threshold value to decide between both classes, because Fisher method select the class nearest to mean values of classes. The performance of proposed method depends of the learning step, and the selectivity criteria is set according to the variance information. However, the most methods reported employing empirical threshold values. Additional variables as temporal variability of peaks (TVP), peak of power spectrum of TVP, and total power of power spectrum of TVP, can help to improve the estimation and performance of the linear discriminant function. This method can be used to control the switching of the BCI systems, as autonomous car [12]. To future works, the proposed method
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