SSVEP-based BCI classification using power

threshold. Thus, it would greatly reduce the accuracy of the systemsin real-timTo ef

should bcharactercrete cosEEG datof signalcient derin speechtral enveformationwould bthe log-mIn this Letter, a SSVEP-based BCI using a power cepstrum is pro-

Thus, it could preserve the accuracy of BCIs and be applied to real-time

Nposed to precisely represent the characteristics of frequency responsesin low-dimensional space. To precisely represent the characteristics ofthe frequency responses, the power cepstrum analysis integrating FFTand DCT is proposed to derive coefcients in low-dimensional space.

ELECTRONICS LETTERS 8th May 2014 Vol. 50e applications.ciently reduce computational time, the parameter dimensionse reduced without losing the precision of representing theistics for frequency responses. For data compression, the dis-ine transform (DCT) had been successfully used to compressa [8]. DCT then could efciently represent the characteristicss in low-dimensional space. In addition, the cepstrum coef-ived from the log-magnitude spectrum has been widely usedtechnologies [9]. It can preserve most information in its spec-lope. As the log-magnitude spectrum contains the major in-of frequency responses, the integrating DCT and cepstrum

e able to effectively represent the signicant information ofagnitude spectrum in low-dimensional space.cepstrum analysis

Yeou-Jiunn Chen, Aaron Raymond Ang See andShih-Chung Chen

The power cepstrum-based parameters for steady-state visually evokedpotential (SSVEP) is proposed. To precisely represent the character-istics of frequency responses of a visually stimulated electroencephalo-graphy (EEG) signal, power cepstrum analysis is adopted to estimatethe parameters in low-dimensional space. To represent the frequencyresponses of SSVEP, the log-magnitude spectrum of an EEG signalis estimated by fast Fourier transform. Subsequently, the discretecosine transform is applied to linearly transform the log-magnitudespectrum into the cepstrum domain, and then generate a set of coef-cients. Finally, a Bayesian decision model with a Gaussian mixturemodel is adopted to classify the responses of SSVEP. The experimentalresults demonstrated that the proposed approach was able to improveperformance compared with previous approaches and was suitablefor use in brain computer interface applications.

Introduction: Motor neuron disease weakens the muscles that consum-mates into loss of voice and voluntary controls of the patients variouslimbs, hence detaching them from the outside world [1]. To addressthe situation, the augmentative and alternative communication (AAC)system could actually help patients with neuromuscular impairmentsto communicate with other people or devices by sending messages orcommands. Thus, various types of interfaces based on voices or gestureshave been widely used in AAC systems [2, 3]. However, these interfaceswere not suitable for neuromuscular impairments. Recently, the braincomputer interfaces (BCIs) are used to design communication systemsby using electroencephalography (EEG) analysis [47]. It sends mess-ages or commands to the external world through direct measurementof brain activity, thus it is a suitable interface for neuromuscular impair-ments. Therefore, to develop a suitable BCI is benecial for patientswith neuromuscular impairments so as to communicate with otherpeople or devices.For BCI approaches, to determine the users response, the steady-state

visually evoked potential (SSVEP)-based BCI applies visual stimulus atspecic frequencies and then the electrical activity of the brain ismeasured [4]. SSVEPs are advantageous in BCIs because of their excel-lent signal-to-noise ratio and relative immunity to artefacts. Therefore, toachieve the best performance of BCIs, many researchers have focusedon selecting stimulator devices and stimuli properties [5]. However, inpractice, the frequency response of a user not only contains manynoises but also a reduction in the level of measured electrical activity.The accuracy would be greatly reduced, thus to develop a sensitiveparameter would improve the performance of the SSVEP-based BCI.To actually classify the frequency responses, the coherence and non-

stationary signal analysis methods have been used in BCIs [6]. Theseapproaches are computationally complex and cannot be applied to real-time systems. Another approach, the thresholding method, uses the spec-tral magnitude estimated by fast Fourier transform (FFT) to classify thefrequency responses [7]. As the threshold is dependent on specic users,it is difcult to apply to other users. Furthermore, the frequencyresponses of a user are usually not only noisy but also lower than thesystems. The Bayesian decision model with the Gaussian mixture model(GMM) is adopted to classify frequency responses by using cepstrumcoefcients.

Power cepstrum analysis: To precisely represent the characteristics offrequency responses in low-dimensional space, the power cepstrumanalysis is proposed in this Letter. For an input frequency responsewith N samples, x(n), the FFT is adopted to estimate the log magnitudeof spectrum X(k) and is dened as

X (k) = logN1n=0

x(n)ei2pk(n/N )( )

(1)

According to the frequencies of visual stimulations, a frequencyband limited log magnitude of the spectrum is extracted from X(k)and denoted as X (m). When the responses are accurately stimulated,a peak envelop should be produced at various frequencies.Subsequently, DCT is applied to linearly transform X (m) into the cep-strum domain and form the cepstrum coefcients C, which can bederived as

C(k) =M1m=1

X (m) cosp

Mm+ 1

2

( )k

[ ](2)

where M is the number of frequency bins in X (m).

Bayesian decision model: To accurately classify the frequencyresponses, the Bayesian decision model is adopted in this Letter. Fora cepstrum coefcient C(k), a class of frequency responses kwith great-est posterior probability can be found by using Bayes rule

vk = argmaxvi

P vi C|( )

= argmaxvi

P vi( )P(C)

P C vi|( )(3)

As the denominator P(C ) is a constant term for all classes and the priorprobability P(i) is treated as a uniform distribution, (3) can be rewrittenas

vk = argmaxvi

P C vi|( ) (4)

To precisely represent the distributions, the conditional probabilityP(C|i) is modelled by the GMM with J mixtures and can be derived as

P C vi|( ) =Jj=1

wjN C mj, sj( ) (5)

where wj, j and j are the mixture weight, mean and the standardvariance associated with the jth Gaussian component. wj is subject tothe following constraint: wj 0 and

Jj=1 wj = 1. These parameters

of the GMMs are estimated by the expectation maximisationalgorithm [10].

Experimental results: To evaluate the proposed approach, ve subjects(four males and one female) aged between 21 and 23 participated in thisstudy. The frequency responses were stimulated using a liquid crystaldisplay screen and the visual stimulator (as shown in Fig. 1) ickeringat ve frequencies from 6 to 10 Hz with 1 Hz increments. Then, theEEG signals were measured using electrodes placed at the Oz, A1 andA2 (ground) in accordance with the international EEG 10-20 system.The sampling rate and the frame size were set to be 1 kHz and 1 s,respectively. Subsequently, using 4096 points FFT, the log-magnitudespectrum can be estimated and band limited from 5 to 11 Hz. Thenumber of cepstrum coefcients used range from 6 to 10 coefcients.As each subject had 60 epochs for each specic frequency, thenumber of mixtures in GMM was set to be two mixtures.

o. 10 pp. 735737

go forward

turn left turn right8 Hz

7 Hz 9 Hz

ach, the log-magnitudes e i selected. Subsequently,t r efcients. The experi-m w F a sed approach exhibitedt es were not clear in ourd ve acceptable perform-

Conclusions: In this Letter, a power cepstrum analysis using DCT isproposed to improve the efciency of SSVEP-based BCI classication.DCT was successfully applied to linearly transform the log-magnitudespectrum into the cepstrum domain in low-dimensional space. Hence,the cepstrum could preserve most information of frequency responsesand then effectively reduce the computational time. According to theexperimental results, the proposed approach improved the performanceof the SSVEP-based BCI system and outperformed other approaches.Moreover, it obtained high accuracy in low-dimensional space.Therefore, the proposed approach is suitable for implementing theSSVEP-based BCI system in a real-time platform. In future, the cep-strum coefcients can be integrated to provide the rejection mechanismfor epochs without messages or commands.

ERance. Furthermore, the error reduction rates were 45.80 and 25.00%for 6 and 9 Hz, respectively. Thus, the proposed approach had the great-est improvement, when the induced responses were clear enough.Consequently, the experimental results demonstrated that the proposedapproach is useful for AAC systems.

70

75

80

85

90

95

100

acc

ura

cy, %

6 7 8 9 10frequency, Hz

proposed approachlog magnitude spectrumthresholding method

Fig. 2 Performance of different approaches

ELECTRONICS LETThe best performatabase, the pance. When the induced responsroposed approach still can preserre sho n in ig. 2 nd the propo

he proposed aental results approach used seven cepst um co

pectrum and the thr shold ng methods were

To objectively compare the proposed approAverage 90.75 92.58 92.50 92.50 92.58elevate

10 Hz 6 Hz

dive

Fig. 1 Multiple visual stimulators used to induce SSVEP response

In the study, a subject (male) was randomly selected as training dataand other subjects were treated as testing data. The detailed results withdifferent numbers of cepstrum coefcients are shown in Table 1. Theaccuracy of 8 Hz (87%) was lower than that of other frequencies.The reason is that the noise is high and the magnitude of the frequencyresponse is low in our database. However, the average accuracy was92.58% with seven cepstrum coefcients, thus the cepstrum coefcientscan efciently represent the characteristics of frequency responses inlow-dimensional space. Hence, the computational time can be efcientlyreduced. The experimental results demonstrated that the proposedapproach would be benecial for real-time-based BCI applications.

Table 1: Accuracies (%) of proposed approach

Frequency (Hz) Number of cepstrum coefcients

6 7 8 9 10

6 95.83 95.42 95.00 95.00 95.42

7 89.17 91.67 91.67 92.92 94.17

8 87.08 87.08 86.67 88.33 85.83

9 96.25 97.50 97.08 93.75 95.00

10 85.42 91.25 92.08 92.50 92.50Acknowledgment: This work was supported by the National ScienceCouncil, Taiwan, under grant numbers NSC100-2632-E-218-001-MY3 and NSC 102-2221-E-218-001.

The Institution of Engineering and Technology 201419 January 2014doi: 10.1049/el.2014.0173One or more of the Figures in this Letter are available in colour online.

Yeou-Jiunn Chen, Aaron Raymond Ang See and Shih-Chung Chen(Department of Electrical Engineering, Southern Taiwan University ofScience and Technology, No. 1, Nan-Tai Street, Yungkang District,Tainan 710, Taiwan)

E-mail: chung@mail.stust.edu.tw

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