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An improved frequency domain feature classification BCI system based on SSVEP

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  • Save American Journal of Biomedical En gineer in g 2013, 3(1): 1-8 DOI: 10.5923/j.ajbe.20130301.01 An Improved SSVEP Based BCI System Using Frequency Domain Feature Classification Se ye d Navid Resalat, Se ye d Kamale din Setare hdan* Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran Abstract Brain Co mputer Interfacing systems provide a new commun ication channel for disabled people. Among the many d ifferent types of the BCI systems, the Steady State Visual Evoked Potential (SSVEP) based ones has attracted more attention due to its ease of use and signal processing. SSVEPs are usually recorded fro m the occipital lobe of the brain when the subject is looking at a twinkling light source. Following our previous report[10], a novel set of features along with a new high-speed classifier are introduced and used in this work. These used for SSVEP classification elicited by LED light sources separated by D = 4, 14, 24, 44 and 64 cm fro m each other while the LEDs’ plane was located 60 cm away fro m the subject's eyes. Using various SSVEP sweep lengths, the results show that the LDA and SVM classifiers outperform the other method used when applied to 0.5 and 1-second sweep lengths and to 2 and 3-second sweep lengths respectively. The Max classifier needs, however, longer sweep lengths but with a comparable Info rmation Transfer Rate (ITR). In addition, for D=44 cm and D=64 cm the algorith m could produce the highest accuracy rate of 90% and 92% respectively compared to the other distances. Also, the performance of the proposed algorithm for D=4 cm is not acceptable (p-value<0.001). Finally, it was showed that the sweep length of 0.5 second could provide a more practica l online ITR. Keywords Brain Co mputer interface (BCI), Steady State Visual Evoked Potential (SSVEP), Inter-source distance, Power Spectral Density (PSD), Student’s t-distribution, Informat ion Transfer Rate (ITR) 1. Introduction A Brain Co mputer Interfacing (BCI) system is a communicat ion channel that could assist and increase the performance of both disabled and normal people. In a BCI system, the brain activ ities are recorded fro m the scalp and coded to appropriate external control co mmands[1]. So me modalities used for brain act ivity recording in BCI applications are Electroencephalography (EEG), magneto encephalography, functional magnetic resonance imaging and near infrared spectroscopy[2]. However, due to its ease of utility and better temporal resolution the EEG is mostly used in BCI systems. The EEG-based BCI systems can be divided into two main categories according to the type of the EEG signals used. These are spontaneous signals and evoked potentials which can be recorded fro m different areas of the scalp[3]. The spontaneous EEG signals are generated in the brain when the subject is intentionally performing a mental task such as adding/subtracting numbers, imaginary rotating a cube, imagination of limb ’s movement, etc. Hence, Motor Imagery (M I), Ment al Tasks (MT) and Slo w co rt ical * Corresponding author: (Seyed Kamaledin Setarehdan) Published online at Copyright © 2013 Scientific & Academic Publishing. All Rights Reserved Potential (SCT) based methods are included in this c lass[4]. In comparison, evoked potentials are produced when an external stimu lus changes the current state of the brain and therefore the property of the corresponding recorded EEGs. P300 and Visual Evoked Potentials (VEPs) are classified in this category[5]. Among the different types of VEP-based BCI systems, the Steady State Visual Evoked Potentials (SSVEP) are mostly used in recent years as it needs simpler processing of the corresponding EEG[6-8]. SSVEP is generated in the EEG when an external light source with a constant twinkling frequency provokes the visual field. Then the effect would be appeared in the vision region of the brain which is in the occipital lobe. Record ing the EEG fro m that region, one could ext ract the SSVEP with the same frequency of the external twinkling light source and its harmonics[9]. Fo r example, if the twinkling frequency is set to 10 Hz, then the corresponding EEG contains main components of 10 Hz and its harmonics of 20 Hz, 30 Hz, and so on. However, the amplitudes of the SSVEPs in higher than the second harmonic are so low that are difficu lt to detect[10]. There are many studies on the SSVEP-based BCI systems in the literature. Bin et al. in 2009 designed an online n ine-channel BCI system with 6 targets using Liquid Crystal Display (LCD) in window length of 2 seconds with a canonical correlation analysis method for frequency 2 Seyed Navid Resalat et al.: An Improved SSVEP Based BCI System Using Frequency Domain Feature Classification detection[11]. Lee et al. in 2011 presented a study of the effects of the different duty cycles of the external light source using a LCD in a given frequency of 13.16 Hz on the performance of a SSVEP-based BCI system[12]. Muller et al. in 2011 designed a BCI system with the external twinkling frequency above 30 Hz using the LCD with the decision tree classification which is updated in every second[13]. Wu et al. in 2008 investigated the different types of the external light source generators and revealed that the amplitudes of the SSVEPs generated by Light Emitting Diodes (LEDs) are higher than those generated by LCD or Cathode Ray Tube (CRT)[14]. In our recently published article in 2012, we investigated the effect of the different inter-source distances of the external LED light sources on the performance of the SSVEP-BCI systems using autoregressive coefficients[10]. The results showed that the performance of the algorith m can be imp roved using more sophisticated feature extraction method. In order to do so, and also to increase the data transfer rate the Power Spectral Density (PSD) is considered for feature extraction along with the four different classifiers of Linear Discriminant Analysis (LDA), Support Vector Machine (SVM ), Naïve-Bayes and the high-speed Max classifier in this paper. The latter will be shown to be the best choice in real t ime applications. Moreover, different sweep lengths and Informat ion Transfer Rate (ITR) is investigated to determine the optimu m window length of the EEG data within an appropriate accuracy rate. In continue, in the materials and methods section, the PSD calcu lation of the SSVEP signals together with the above mentioned four classifiers are presented. This section covers also the definition of the ITR and its applications in BCI systems. Next, in the results section, the accuracy and ITRs of each classifier for different inter-LEDs distances are presented and statistically compared. There is a brief discussion over the results in the following section. Finally, conclusion section summarizes and concludes the paper. 2. Materials and Methods 2.1. Experi mental Setup The experimental setup used in this study is shown in Figure 1. Two wh ite LEDs with constant twinkling frequencies of 15 Hz and 20 Hz are considered in this work. These frequencies are suitable for the SSVEP-based BCI applications as the amplitude of the SSVEP oscillations for higher frequencies are smaller wh ile for lo wer frequencies the background EEG which contains the α-band oscillations can interfere with the SSVEP signals[9]. In addition according to[15] choosing a duty cycle of 50% for the twinkling LEDs more stronger oscillat ions can be obtained. Moreover, the modulation depth of the experiment must be higher than 75% to ensure the elicitation of the SSVEPs [16]. Figure 1. Electrode positioning and the twinkling light sources used in this study For assessment purposes a lu minance meter was located in place of the subject's eye. The background luminance of the experiment was found to be around 40 to 140 cd/ m2 for different EEG record ing sessions while the source lu minance was around 11000 to 13000 cd/ m2. Therefore, the modulation depth varied between 97.5% and 99.2%, which can be assumed almost constant for all cases. According to the international 10-20 system for EEG recording, the positive, reference and ground electrodes are placed over the Oz, the Fz and the right ear lobe, respectively (Figure 1). Th is electrode montage is common in SSVEP - BCI systems. EEG signal acquisition was performed by using an AD-instrument EEG system. The impedance of the electrodes was continuously measured to keep them less than 5 KΩ for the best possible performance of the recorded data. The sampling frequency of the EEG recording system was set to 1000 Hz. Five d ifferent horizontal d istances (D) of 4, 14, 24, 44 and 64 cm were examined between the two sources, while the plane of the sources were located 60 cm away fro m the subject as shown in figure 1. Therefore, the inter-LEDs distances of D=4, 14, 24, 44 and 64 cm are equivalent to a horizontal angle of 3.8°, 13.4°, 22.6°, 40.2° and 56° respectively. Considering a constant distance of 60 cm between the subject and the light sources’ plane during all experiments, for simp licity fro m now on the inter-LEDs distances are only represented by their distance in centimetre instead of the angles between them in degree. Wider inter-LEDs distances than 64 cm are not applicable since one of the sources falls out of the visual field when looking at the other one. These stepwise distances were selected arbitrary to cover the co mplete field o f view fro m D=4 cm to D=64 cm. Selection of other steps for inter-LED distances would provide comparable results[10]. American Journal of Biomedical Engineer ing 2013, 3(1): 1-8 3 2.2. Data Acquisition and Analysis The dataset used in this research were recorded fro m eight normal-eyesight subjects in the Bio medical Engineering Laboratory of the University of Tehran. Each subject was instructed to look d irect ly at each LED, in the presence of twin kling of the other LED in all specified inter-LED d istances (D), for a period of 60 seconds during which the SSVEP signals were recorded. Th is procedure was repeated twice for each light source producing 2 minutes of SSVEP signal for each of the light sources and 4 minutes of data for the two light sources for each subject and each inter-LED distance. This procedure was repeated for all five inter-LED distances of 4, 14, 24, 44 and 64 cm. Data analysis consists of the following steps. At first step, the recorded data is passed through a band-pass filter of 5-45 Hz to eliminate the lower and higher interfering frequencies. Then at the second step, in order to evaluate the proposed system in real-time applications, four different durations (sweeps) of 0.5, 1, 2 and 3 seconds were considered. Each 4-minute data is then divided into non-overlapping segments with a length equal to each sweep. For examp le, considering the sweep length of 0.5 second, each 240 seconds (4-minute) data has 480 observations. This would be 240, 120 and 80 observations for sweep lengths of 1, 2 and 3 seconds, respectively. Shorter sweep lengths are not practical since the signal to noise ratio of the EEGs becomes too low and the evoked potentials could be indistinguishable fro m the background EEGs [10]. At the third step, a novel PSD based feature is introduced which is computed using the amplitude of the main and harmonic frequencies of the observations as follo ws: feature= = FFT{xn} 2 f M= F + FFT{xn} 2 f HMF (1) N where, xn is the segmented data in each sweep length, FFT{•} is the Discrete Fourier Transform, |•| is the absolute operator, MF is the main t win kling frequency (f= 15 Hz or 20 Hz), HMF is the first harmonic of the main t win kling frequency (f= 30 Hz or 40 Hz) and N is the total nu mber of samples in the frequency domain. It should be noted that two features are ext racted fro m each observation; one corresponds to the main frequency of 15 Hz and its first harmonic and the other corresponds to the main frequency of 20 Hz and its first harmonic. Therefore, the total data matrix in sweep lengths of 0.5, 1, 2 and 3 seconds will be of dimension 480×2, 240×2, 120×2 and 80×2, respectively. To assess the performance of the proposed system for separating the two classes, i.e. if the first or the second light source is in the centre o f attention of the subject, the averaged accuracy of all subjects is computed using the LDA, the linear SVM, the Naïve-Bayes and the Max classifiers. The LDA and the SVM classifiers use linear hyper-planes to classify the whole dataset while the Naïve- Bayes uses the distribution of the data in the space to compute the likelihood and the prior p robability for each clas s . The accuracy of the classifiers is then computed as fo llo ws : Accuracy = TP + TN TP + TN + FP + FN (2) where True-Positive (TP) and False-Negative (FN) are the numbers of classified and misclassified observations with positive labels respectively while True-Negative (TN) and False-Positive (FP) are the same as TP and FN with negative labels. To train all classifiers (except the Max operator wh ich does not need any training), the data set is shuffled and divided into the training and test sets of 80% and 20%, respectively. The process is repeated 50 times and the resulting accuracies were averaged for each subject separately over the test sets. x 10-12Data extracted from Power Spectral Density 5 4.5 look ing at 15 Hz light source look ing at 20 Hz light source 4 MAX classifier decision line x 10-12 5 4 Output of Max Classifier classifier predicts 15 Hz light source classifier predicts 20 Hz light source MAX classifier decision line Power Spectral Density of 20 Hz Power Spectral Density of 20 Hz 3.5 3 3 2.5 2 2 1.5 1 1 0.5 0 0 1 2 3 4 5 0 0 1 2 3 4 5 Power Spectral Density of 15 Hz x 10-12 Power Spectral Density of 15 Hz x 10-12 Figure 2. Performance of the Max classifier over the data. The left subfigure illustrate the distribution of the data using the extracted features. The right one applies the Max classifer over the same dat a 4 Seyed Navid Resalat et al.: An Improved SSVEP Based BCI System Using Frequency Domain Feature Classification With the definit ion of the Max classifier, the processing time of the proposed system decreases significantly. This is simp ly done by selecting the largest feature value in the feature vector. That is, for example, if the subject is looking at 15 Hz twinkling light source in the presence of the 20 Hz twinkling source, then the feature value extracted in 15 Hz frequency band would show larger value than that of 20 Hz frequency band (see equation 1). Figure 2 illustrates the performance of the Max classifier mo re objectively. The left subfigure shows the distribution of the feature vectors. The blue and the red dots indicate the PSD ext racted fro m the corresponding EEGs when the subject is looking at 15 Hz and 20 Hz t win kling light sources, respectively. The right subfigure shows the same data classified with the Max classifier (the green line). At the fifth step, a statistical test is carried out in order to compare the output of the classifiers for different inter-LEDs distances using the Student's t-distribution[10] with significance level of 0.1% (α=0.001) and 18 degree of freedom, since the output of the LDA, the SVM and the Naïve-Bayes classifiers is a stochastic variable. The degree of freedom of 18 is calcu lated considering the number o f 10 runs for each inter-LEDs distance (18=10+10-2). At the final step, the Information Transfer Rate (ITR), which considers both the speed and the accuracy of the system fro m the practical BCI point of view, is co mputed to choose the best sweep length together with the best classifier as fo llo ws: = B log2 N + p log2 p + (1 − p ) log2 ( 1− p ) N −1 (3) where N is the number of output classes of the system, p is the accuracy of the classifier and B represents available informat ion in each trial as measured by bits/trial. By inserting the time length (sweep length) of the trial, the ITR can be measured in b its/min[17]. It should be noted that in this research N is 2 that is due to existence of two external light sources. constant parameters of its decision line (figure 2), therefo re the outputs are not stochastic and the statistical evaluations will not apply to them. Table I, II and III co mpares the outputs of each classifier statistically over the sweep lengths in two adjacent inter-LEDs distances. As it can be seen, the classification accuracies for D=4 cm are smaller than of other distances for each sweep and each classifier. In addition, the accuracies for D=44 cm and D=64 cm are almost the same while those for D=24 cm are slightly s maller. It worth to note that, by increasing the sweep lengths, the accuracies increase for each classifier and for each inter-LED d is tan ces . The tables also show the situation of the Nu ll Hypothesis (H) with H=1 for rejection and H=0 for failure to reject together with the corresponding p-values. x 10-6 1 0.8 X: 19.99 Y: 8.393e-007 0.6 0.4 0.2 0 5 10 15 20 25 30 35 40 x 10-7 6 5 4 3 2 3. Experimental Results As it was stated before, when the subject is looking at one of the two light sources the amplitude of the corresponding SSVEP will be higher co mpared to those elicited by the other source. This is illustrated in figure 3 where the average spectrum for all subjects in the case of D=44 cm and fo r the t ime period of 1 min is p lotted. In other words, observing the amplitude of the main frequencies and their harmonics it is possible to determine which external light source is being watched by the subject. This characteristic is to be used in BCI applications[10]. To evaluate the performance of the LDA, the SVM and the Naïve-Bayes classifiers the mean and standard deviation (STD) values for the accuracy of the data in the test sets for 5 different inter-LED distances are computed and shown in Figures 4. It also displays the accuracy of the Max classifier. As the outputs of the Max classifier do not vary due to the 1 0 5 10 15 20 25 30 35 40 x 10-6 1.2 1 X: 15.01 Y: 1.131e-006 0.8 0.6 0.4 0.2 0 5 10 15 20 25 30 35 40 Figure 3. Averaged spectrum of EEG signals when looking at no light source (down one); 15 Hz and 20 twinkling frequencies (top and middle one, respectively) American Journal of Biomedical Engineer ing 2013, 3(1): 1-8 5 The Nu ll Hypothesis is an assumption of equal performance of t wo sets of independent random samp les. According to table I, in a ll of the sweeps the performance of the LDA classifier d iffers significantly fro m D=4 cm to D=14 cm, as all the H values are 1 or their corresponding p-values are less than the significance level. The p-values also introduced to show the qualitative difference of each two adjacent inter-LED distances. As the significance level is set to 0.1%, the p-value less or greater than α=0.001 states the difference and non-difference behaviour of the proposed method over the classifier, respectively as it is also illustrated in Null Hypothesis (H) section. However, the higher the α, the stronger the corresponding conclusion would be. For examp le, in D(14-24 cm), the p-value for the sweep time of 0.5s sweep is 0.0028 while this is 3.8136×10-13 for the sweep time of 1s. Figure 4. The accuracy and STD of the LDA (a), the SVM (b), the Naïve-Bayes (c) and the Max classifier (d) Statistical test of the outputs of the LDA D (4-14 cm) D (14-24 cm) D (24-44 cm) D (44-64 cm) Table 1. Statistical evaluation of the outputs of the LDA classifier Sweep = 0.5 s Sweep = 1 s Sweep = 2 s H p-value H p-value H p-value 1 4.1086e-010 1 8.5773e-013 1 7.5720e-013 0 0.0028 1 3.8136e-013 0 0.3981 1 8.9130e-015 0 0.0014 0 0.0218 0 0.2288 0 0.4625 1 2.5459e-010 Sweep = 3 s H p-value 1 1.7472e-014 0 0.1573 0 0.3784 1 1.4386e-004 Statistical test of the output s of the SVM D (4-14 cm) D (14-24 cm) D (24-44 cm) D (44-64 cm) Table 2. Statistical evaluation of the outputs of the SVM classifier Sweep = 0.5 s H p-value 1 7.7895e-014 0 0.4180 1 5.9617e-012 0 0.1912 Sweep = 1 s H p-value 1 3.2109e-013 1 7.0367e-015 0 0.1096 0 0.3018 Sweep = 2 s H p-value 1 5.7489e-012 0 0.1332 1 3.0986e-004 1 6.3501e-007 Sweep = 3 s H p-value 1 1.3954e-011 1 4.7415e-004 1 5.2969e-004 1 4.4658e-005 Statistical test of the outputs of the NB D (4-14 cm) D (14-24 cm) D (24-44 cm) D (44-64 cm) Table 3. Statistical evaluation of the outputs of the Naïve Bayes (NB) classifier Sweep = 0.5 s H p-value 1 1.0374e-010 0 0.0041 1 1.7117e-007 0 0.0061 Sweep = 1 s H p-value 1 1.4700e-015 1 1.1717e-011 1 2.6424e-006 0 0.0034 Sweep = 2 s H p-value 1 7.8515e-016 0 0.2759 1 6.3904e-009 1 1.6797e-009 Sweep = 3 s H p-value 1 1.1604e-013 1 6.1915e-005 0 0.1315 1 2.9416e-010 H values of three classifiers D (4-14 cm) D (14-24 cm) D (24-44 cm) D (44-64 cm) Table 4. The H values of the output s of the all classifiers over the sweep lengt hs Sweep = 0.5 s LDA SVM NB 1 1 1 0 0 0 1 1 1 0 0 0 Sweep = 1 s LDA SVM NB 1 1 1 1 1 1 0 0 1 0 0 0 Sweep = 2 s LDA SVM NB 1 1 1 0 0 0 0 1 1 1 1 1 Sweep = 3 s LDA SVM NB 1 1 1 0 1 1 0 1 0 1 1 1 6 Seyed Navid Resalat et al.: An Improved SSVEP Based BCI System Using Frequency Domain Feature Classification As it can be seen in Tables II and III, the difference between the performances of the cases D=4 cm and D=14 cm is evident in both the SVM and the Naïve-Bayes classifiers. To statistically co mpare tvarious classifiers, table IV rep resents only the H values regardless of the p-values for the classifiers used. It can be seen from the results that for all classifiers and sweep lengths the performance of the p roposed system for D=4 cm is different fro m that of D=14 cm, that is the accuracies for D=4 cm are always lower than those of D=14 cm (figure 4). Therefore D=14 cm in the proposed system performs better in the LED-based BCI applicat ions. Although the performance in D=14 cm is similar to that in D=24 cm in sweep lengths of 0.5 and 2 seconds, however they are different in 1 and 3 seconds sweep. In sweep length of 2 and 3 seconds, the outputs of the classifiers differ significantly fo r D=44 cm and D=64 cm, however they are similar for 0.5 and 1-second sweeps. Figure 5, co mpares the classifiers in terms of their outputs (accuracy) in each sweep lengths. The accuracies of the LDA and the SVM are almost the same while the accuracies of the Max classifier are slightly smaller than the others. As the sweep lengths increases, the accuracies of all classifiers become closer. As stated earlier, the accuracies for D=4 cm are the lowest. These accuracies will be g reater for longer inter-LED d istances. As stated above, with an increase in the sweep lengths, the accuracy of each classifier fo r each inter-LEDs distance increases while the speed of the whole system decreases. In the final step, both the speed and the accuracy of the proposed system are co mpro mised using ITR. Shorter sweep lengths correspond to shorter response time and therefore to the higher speed of the system. Figure 6 shows the ITRs of the proposed system using the accuracies of figure 5. As the response time of the classifiers is different, this figure also considers the processing time o f each of them, separately. Figure 6. The ITR of the classifiers over the inter-LEDs distances in sweep length of 0.5s(a), 1s(b), 2s(c) and 3s(d) The processing time of the Max, the LDA, the Naive-Bayes and the SVM classifiers is nearly 0.002, 0.04, 0.04 and 0.08 seconds in a processor of 2.13 GHz, core i3 CPU and 2.67 GB RAM, respectively. In more up-to-date computers, the processing times will be smaller that could not affect the response time of the who le system. Although in the shorter sweeps the ITR values of the LDA are the highest, these become better for the SVM in the longer ones. With an increase in the sweep lengths, the ITRs of the Max classifier beco mes closer to the ITRs of the other ones; therefore it could be used in the longer sweep lengths for real-time applications as its processing time is less than that of the others. In addition, inter-LED distance of 44 cm and 64 cm have the highest ITR of 13.4 bits/min and 13.5 b its/min in sweep length of 0.5 second using the LDA classifier. 4. Discussion Fi gure 5. The accuracy of the classifiers over the int er-LEDs dist ances in sweep length of 0.5s(a), 1s(b), 2s(c) and 3s(d) In this research, the statistical evaluation of an LED-based BCI system in d ifferent inter-sources distances of D=4, 14, 24, 44 and 64 cm were investigated. To do so, a PSD method was applied to each segmented EEGs in order to ext ract the SSVEPs. Four classifiers of the LDA, the SVM, the Naïve-Bayes and the Max operator were introduced. Then, Student’s t-distribution used to compare the performance of the classifier in each two-adjacent distance and finally the ITR were presented to identify the best sweep length for real-t ime applications. Analysing the spectrum of the EEG could determine which external light source is being watched directly. In addition, longer the segmented data, more robust this identification would be. Th is is displayed in figure 3 in a window length of 1 min. Although the peak amplitudes are so clear, this period of t ime is not feasib le in real-time ap p licatio n s . American Journal of Biomedical Engineer ing 2013, 3(1): 1-8 7 According to the figures 4, in all classifiers, the accuracy increases by the increment of the sweep lengths. A reason for this could be that more sweep lengths contains more SSVEPs; hence the corresponding peak amp litudes would be higher and classifiers would be mo re accurate. Moreover, it can be concluded that by increasing the inter-LED distances from D=4 cm to D=64 cm, the accuracy of the BCI system is generally increasing. That is due to less interference of each flickering LED with the other one in longer inter-LED distances. This is evident in the tables that the performance of the proposed system in D=4 cm differs significantly fro m the one in other distances in all sweep lengths (p-value < 0.001). According to figure 5, the performance of the LDA outperforms the others in sweep length of 0.5 and 1 seconds whilst in sweep lengths of 2 and 3 seconds the performance of the SVM is better than the other classifiers. Ho wever considering the processing time of the classifiers the LDA will be more preferable. In addition, according to the ITRs, the LDA has the highest values and with the sweep increments the Max classifier could be best choice for online processing. Finally, it worth to note that the D=44 cm is the turning point among the other inter-LEDs distances as it has high ITR values and is closer to the eyes instead of D=64 cm. For practical applications, using the inter-LEDs distance of D=24 cm is more desirable due to the shorter distance despite of its a few less accuracy. In one previous paper, the performance of an SSVEP-based system with two LEDs using Auto-Regressive (AR) model for feature ext raction and three classifiers of the LDA, the SVM and the Naïve-Bayes were studied. It was showed that AR model order of 13 has the highest accuracy rate with either the LDA or the SVM of among the other orders. In addition, even in sweep length of 3 seconds the reported accuracy of 78% is not so high for the detection of the external light sources[10]. However, this study not only overcomes this problem with a PSD-based feature extraction method but also represents the high-speed Max classifier to be used in higher sweep lengths 5. Conclusions In this paper, a novel frequency domain feature is defined and used based on the PSD of the SSVEP signal. The effect of the different inter-LED d istances on the accuracy of the LED-based BCI systems was then statistically investigated. Moreover, the high-speed Max classifier was introduced to decrease the processing time of the proposed system. It was showed that in 0.5 and 1-second sweeps the LDA and in 2 and 3-second sweeps the SVM could outperform the other classifiers while the Max classifier could also be used in longer sweeps with a co mparab le ITR. In addition, an inter-LED distance of D=64 cm (equivalent to the visual angle of 56°) could produce the highest accuracy of 92% among the other distances that were studied. Finally, it was showed that the sweep length of 0.5 second could provide a more practica l online ITR. REFERENCES [1] H. Cecotti (2011) Spelling with non-invasive Brain-Computer Interfaces – Current and future trends. Journal of Physiology-Paris, Vol. 105, Issues 1–3, 106-114. [2] S. N. Resalat, et al (2012) High-speed SSVEP-based BCI: study of various frequency pairs and inter-sources distances. IEEE-EM BS Int. Conf. Biomedical and Health Informatics, 220 – 223. [3] D. J. M cFarland et al (2005) Brain-computer interface (BCI) operation: signal and noise during early training sessions. Clinical Neurophysiology, Vol. 116, No. 1, 56-62. [4] H. M . Wadsworth and R. K. Kana (2011) Brain mechanisms of perceiving tools and imagining tool use acts: A functional M RI study. Neuropsychologia, Vol. 49, No. 7, 1863-1869. [5] Yu Zhang, Jing Jin et al (2012) LASSO based stimulus frequency recognition model for SSVEP BCIs. Biomedical Signal Processing and Control, Volume 7, Issue 2, 104-111. [6] K. 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