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Neuroscience Letters 587 (2015) 87–92

Contents lists available at ScienceDirect

Neuroscience Letters

jo ur nal ho me page: www.elsev ier .com/ locate /neule t

esearch article

lassification of prefrontal and motor cortex signals for three-classNIRS–BCI

eum-Shik Hong a,b,∗, Noman Naseer b, Yun-Hee Kim c

School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 609-735, South KoreaDepartment of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 609-735, South KoreaDepartment of Physical and Rehabilitation Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Irwon-dong,angnam-gu, Seoul 135-710, South Korea

i g h l i g h t s

Feasibility of three-class fNIRS–BCI demonstrated.Classification of fNIRS signals corresponding to three different brain activities.Motor and prefrontal cortex activities used.Intentionally-generated cognitive tasks as inputs.

r t i c l e i n f o

rticle history:eceived 20 August 2014eceived in revised form 4 December 2014ccepted 13 December 2014vailable online 18 December 2014

a b s t r a c t

Functional near-infrared spectroscopy (fNIRS) is an optical imaging method that can be used for a brain-computer interface (BCI). In the present study, we concurrently measure and discriminate fNIRS signalsevoked by three different mental activities, that is, mental arithmetic (MA), right-hand motor imagery(RI), and left-hand motor imagery (LI). Ten healthy subjects were asked to perform the MA, RI, and LI

eywords:rain-computer interfaceunctional near-infrared spectroscopyinear discriminant analysisotor imagery

during a 10 s task period. Using a continuous-wave NIRS system, signals were acquired concurrentlyfrom the prefrontal and the primary motor cortices. Multiclass linear discriminant analysis was utilizedto classify MA vs. RI vs. LI with an average classification accuracy of 75.6% across the ten subjects, for a 2–7 stime window during the a 10 s task period. These results demonstrate the feasibility of implementing athree-class fNIRS-BCI using three different intentionally-generated cognitive tasks as inputs.

© 2014 Elsevier Ireland Ltd. All rights reserved.

. Introduction

One of the aims of a brain-computer interface (BCI) is to pro-ide a communication pathway for control of a computer (orther external devices) through the direct process of neural signals1]. Electroencephalography (EEG) and/or functional near-infraredpectroscopy (fNIRS)-based BCI has been attractive for its ability toontrol external devices through a “direct” communication with the

rain. Among the various brain-signal acquisition methods (EEG,RI, MEG, etc.), fNIRS is cost effective, portable, low noise, and most

f all a real-time imaging of inside the brain is possible. It also offers

∗ Corresponding author at: School of Mechanical Engineering, Pusan National Uni-ersity, 2 Busandaehak-ro, Geumjeong-gu, Busan 609-735, South Korea.el.: +82 515102454; fax: +82 515140685.

E-mail addresses: kshong@pusan.ac.kr (K.-S. Hong), noman@pusan.ac.kr (N.aseer).

ttp://dx.doi.org/10.1016/j.neulet.2014.12.029304-3940/© 2014 Elsevier Ireland Ltd. All rights reserved.

flexibility in use and a better spatial resolution than EEG; however,it has a poor temporal resolution as compared to EEG. The princi-ple of fNIRS, first elaborated by Jobsis [2], has been applied to brainmapping, brain-state decoding, and BCI over the last decade [2–30].

BCIs can be divided into active and reactive [31]: An active BCIgenerates its outputs in relation to brain activities that are notdependent on any external events or devices, whereas the reac-tive BCI generates its outputs upon the brain activities arising inreaction to external stimulation(s). In the active BCI, the user usu-ally controls the output by intentionally evoking different patternsof activation in a particular brain region. This is usually done byperforming different mental tasks, such as motor imagery [5,7,32],mental singing [6,9,11], mental arithmetic [9,11,16], and others [6].The BCI system then detects and interprets these patterns, and pro-

duces appropriate command signals to communicate with or tocontrol an external device in a manner intended by the user.

Motor imagery has been shown to work well in the previousfNIRS studies [3–5,7,20] and has been successfully employed in the

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EG-based BCI studies [33,34]. Since brain activation during motormagery is similar to that during overt body movement [32], it isonsidered to be a natural way in making BCI outputs to the externalevices. The feasibility of motor-imagery-based fNIRS–BCI has beenhown by over 80% average classification accuracy in Coyle et al. [5].

The activities from the prefrontal cortex are also a good choiceor fNIRS–BCI as, unlike the motor cortex, the prefrontal cortexs less likely to be implicated in motor disabilities. The use ofrefrontal cortex activities for BCI purposes is also advantageousecause the signal attenuation due to hair artifacts is less severe.ome fNIRS–BCI studies have shown promising results in using pre-rontal activities such as music imagery and mental arithmetic [6,9].

disadvantage, however, is that sweat and muscle twitches canffect the fNIRS signals from the prefrontal area.

Although the feasibility of a two-class fNIRS–BCI by using motormageries alone or prefrontal activities alone has been shown, thusar no work on the development of a three-class fNIRS–BCI has beenone. Increasing the number of classes increases the number ofessages that the user wants to convey. A three-class fNIRS–BCI is

dvantageous because the increased number of classes is one of theactors to increase the information transfer rate of the system (thether two are the task duration and the true positive rate) [17]. Ahree-state fNIRS–BCI can differentiate three different brain activi-

ies and, therefore, result in generation of three control commands.t can also be used as a three-choice system in the multiple choiceuestions paradigm.

ig. 1. Optodes placement and channel configuration (a gray-filled circle represents anortex, six emitters and six detectors in each hemisphere are used to acquire the fNIRSortex, three emitters and five detectors are used to access the mental arithmetic tasks. F

Letters 587 (2015) 87–92

In the present work, we investigate the feasibility of a three-state fNIRS–BCI by classifying signals corresponding to mentalarithmetic (MA), right-hand motor imagery (RI), and left-handmotor imagery (LI). To the best of our knowledge, this is the firstinvestigation that discriminates the three different intentionally-generated cognitive tasks acquired simultaneously from theprefrontal and primary motor cortices, which can be translatedinto three different control commands suitable for a three-classfNIRS–BCI.

2. Materials and methods

2.1. Signal acquisition

A multichannel continuous-wave imaging system (DYNOT:DYnamic Near-infrared Optical Tomography) from NIRx MedicalTechnologies, NY, was used to acquire hemodynamic responses at asampling rate of 1.81 Hz. The system uses near-infrared light of twowavelengths (760 and 830 nm) to detect concentration changesof oxygenated hemoglobin (HbO) and deoxygenated hemoglobin(HbR) molecules in the micro-vessels in the cortex. The light is emit-ted at the skin of the head through the skull into the brain, which

As the photons pass through the cortical area, the chromophores(HbO and HbR) absorb some photons with different absorptioncoefficients. The remaining photons continue to travel and are

emitter and an unfilled circle represents a detector): (a) For the (primary) motor signals originated by right- and left-hand motor imageries. (b) For the prefrontalp1 and Fp2 are the reference points in the International 10–20 system.

ience Letters 587 (2015) 87–92 89

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etected by properly placed detectors. The intensity of the detectedight is then used to calculate the concentration changes of HbOnd HbR (i.e., �cHbO(t) and �cHbR(t)) according to the modifiedeer–Lamberts law.

�cHbO(t)

�cHbR(t)] = [

˛HbO(�1) ˛HbR(�1)

˛HbO(�2) ˛HbR(�2)]

−1

[�A(t, �1)

�A(t, �2)]

1l × d

, (1)

here �A(t; �j) (j = 1,2) is the unit-less absorbance (optical density)ariation of wavelength �j , ˛HbX(�j)is the extinction coefficient ofbX in �M−1 mm−1 (note that HbX ∈ {HbO, HbR}), d is the unit-

ess differential pathlength factor (DPF), and l is the distance (inillimeters) between an emitter and a detector.

The configuration/placement of optodes plays an important rolen fNIRS measurement. The emitter-detector distance is deter-

ined based on the depth of the region of interest: An increasen emitter–detector distance, for example, leads to an increase inmaging depth [35–38]. To measure the hemodynamic responseignals from the motor cortex and the prefrontal cortex, an emitter-etector separation of around 3 cm has been applied [37]. Aeparation of more than 5 cm might result in weak and unusableignals [36].

To measure the hemodynamic signals in relation to right- andeft-hand motor imageries, 6 near-infrared light emitters and 6etectors have been placed over the primary motor cortex in eachemisphere, which is known to be the area activated by motor

magery [32,39]. Additionally, 3 emitters and 5 detectors are posi-ioned over the prefrontal cortex to measure the signals caused by

ental arithmetic tasks. Fig. 1 shows the emitter-detector arrange-ents in the primary motor and prefrontal cortices.

.2. Subjects

A total of ten subjects (all right-handed, male, mean age0.2 ± 4.0) participated in the experiment. To eliminate anyariations in the hemodynamic response due to hemispheric-ominance differences, only right-handed individuals wereecruited. None of them had a history of any psychiatric or neu-ological disorder. All of them had normal or corrected-to-normalision. The experimental procedure was explained to all of themn detail before they provided their verbal informed consent. Thexperiment was conducted in accordance with the latest Declara-ion of Helsinki.

.3. Experimental procedure

The subjects were asked to seat in a comfortable chair facing a5.6 inch monitor located at approximately 70 cm apart from theirasion in a dimly lighted room. They were asked to relax for 5 minrior to the experiment in order to remove any existing hemody-amic response due to their previous activities. The subjects werelso advised to remain relaxed during the experiment, so as tovoid any unnecessary movement or thinking. In each trial, therst 20 s was a rest period to set up a baseline value, followed by

10 s task period, which was followed by another 20 s rest periodhat is preparatory to the next task (see Fig. 2). This pattern wasepeated 30 times, making the total time of one experiment to be00 s per subject. Since three mental tasks (MA, RI, and LI) were

nvolved, each mental task had appeared 10 times randomly in onexperiment.

For the MA task, the subjects were asked to perform a seriesf mental calculations of the arithmetic problems appearing on

he screen. The problem was subtraction of a two-digit numberbetween 10 and 20) from a three-digit number, which appearedandomly. For example, for an initial 3-digit number 730, subse-uent subtractions of 730 – 17, 713 − 12, 701 − 15, etc. should be

Fig. 2. Schematic of the experimental paradigm: The green blocks represent the 20 srest periods at the beginning and at the end; the red block represents the 10 s taskperiod.

calculated throughout the 10 s task period [9,26]. For the motorimagery task, the subjects were asked to kinesthetically image thesqueezing of a rubber ball while avoiding muscular tension, as inCoyle et al. [5]. An image of right-hand was shown on the screen toindicate the RI task period and an image of left-hand was shown toinform the LI task period. The screen was left blank during the restperiods. For all three cases, the subjects were asked to start (andcontinue) the tasks for the 10 s period and finish it as soon as it wasover.

2.4. Signal processing

The raw light-intensity signals were first normalized by divid-ing themselves by the mean value during the baseline period (i.e.,the initial 20 s rest period). Then, to remove the high frequencyphysiological noises due to heartbeat and respiration, the signalswere low-pass filtered using a 4th-order Butterworth filter of cut-off frequency 0.3 Hz. Then, to minimize the effect of low frequencyoscillations, such as Mayer waves, a high-pass filter with cut-offfrequency of 0.1 Hz was used. Finally, �cHbX(t) was then calculatedusing Eq. (1).

2.5. Feature selection and classification

In this study, only the averaged �cHbO(t) signal over all the chan-nels (i.e., 17 channels for the motor cortex and 9 channels for theprefrontal cortex) was considered for classification. To improve theclassification accuracy, the scheme of using a 5 s window (insteadof using the entire 10 s task period) was adopted [20]. Henceforth,for each task, six 5 s segmented data of the averaged signal (i.e.,0–5, 1–6, 2–7, 3–8, 4–9, and 5–10 s) were re-generated. Then, thesignal slope (SS) and the signal mean (SM) for each 5 s segmentwere computed: The SS value was computed by using the polyfitfunction in Matlab, which fits a regression line to the given dataset, whereas the SM value was obtained by averaging all the fivedata points. Therefore, for each time window, there were 30 two-dimensional (i.e., SS and SM) data points corresponding to 30 trials(i.e., 3 tasks × 10 repetitions). Fig. 3 shows six 2-D feature sets forthe considered six windows and the averaged �cHbO(t) signals ofSubject 1. Note that the two feature values in Fig. 3(a) were scaledbetween 0 and 1 using the equation below.

z′ = z − min z

max z − min z(2)

where z ∈ Rn (in this paper, n = 30) denote the original data of SMand SS, z′ are the re-scaled values between 0 and 1, max z is thelargest value, and min z is the smallest value.

The multiclass linear discriminant analysis (LDA) classifier was

trained on these scaled features. Because of the simplicity of LDA,it is widely employed for BCI data classification: It shows a goodcompromise between computational cost and classification per-formance [40]. Let xk ∈ R2 (k = 1, 2,. . . 30) denote the samples, �i

90 K.-S. Hong et al. / Neuroscience Letters 587 (2015) 87–92

Fig.3. (a) 2-D feature spaces of six time windows. (b) The averaged signals over the entire channels and trials of Subject 1.

K.-S. Hong et al. / Neuroscience

Table 1Classification accuracies of various time windows [%].

Subject no. 0–5 s 1–6 s 2–7 s 3–8 s 4–9 s 5–10 s

1 63.3 60.0 73.3 73.3 70.0 63.32 53.3 63.3 70.0 70.0 76.6 66.63 53.3 63.3 76.6 56.6 53.3 60.04 56.6 56.6 80.0 66.6 53.3 73.35 53.3 73.3 70.0 80.0 63.3 53.36 63.3 60.0 83.3 73.3 56.6 63.37 53.3 66.6 73.3 76.6 60.0 50.08 50.0 63.3 76.6 70.0 63.3 53.39 60.0 73.3 80.0 66.6 66.6 56.6

10 56.6 66.6 73.3 73.3 63.3 56.6

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ote: Window 2–7 s shows the best performance.

e the sample mean of class i, and � be the total mean of all theamples. That is,

i =1ni

∑

x∈class i

x, � = 1n

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xi

xi, (3)

here ni is the number of samples of class i, and n is the total num-er of samples. Then, the multiclass LDA problem is to find theptimal projection matrix V that maximizes the following Fisher’sriterion, see [40].

(V) = det(VTSBV)det(VTSWV)

(4)

here SB and SW are the between-class scatter matrix and theithin-class scatter matrix, respectively, defined by

W =m∑

i=1

�xk∈class i

(xk − �i)(xk − �i)T (5)

W =m∑

i=1

ni(�i − �)(�i − �)T (6)

here m is the number of classes (m = 3 in our work). It is notedhat a matrix V satisfying (4) can be reformulated as a generalizedigenvalue problem as follows.

−1W SBV = �V. (7)

he optimal V is then the matrix corresponding to the largest twoigenvalues of SW−1SB.

Finally, we used 10-fold cross-validation method by mixing thentire data randomly into 10 groups, of which 9 were used for train-ng and the tenth for testing. This process continued until everyroup had been used for a testing sample.

. Results

The results of the three-class classification are shown in Table 1.he theoretical chance level in our paradigm was 33.3%. The aver-ge individual classification accuracies were well above this level,anging from 50% (for 0–5 s time window) to 83.3% (for 2–7 s timeindow). The higher classification accuracies for the 2–7 s timeindow was consistent with the literature [20]. Since the data did

ot fit any standard distribution, the statistical significance of theesult was verified using the non-parametric Mann-Whitney U test.omparing the results for the different time windows, the p-values

or the classification accuracies obtained using 2–7 s window versushose obtained using 0–5 s, 1–6 s, 3–8 s, 4–9 s and 5–10 s were

.0001, 0.0007, 0.00409, 0.0007, 0.0003, respectively. This indicateshat the classification accuracies obtained using the 2–7 s time win-ow were significantly higher than those using all the other timeindows. To confirm that the signals during the activation period

Letters 587 (2015) 87–92 91

were different from those during the rest period, classifications ofRI, LI and MA were performed against the rest period. The classifi-cation accuracies were found to be 90%, 80% and 85% for RI versusrest, MA versus rest and LI versus, respectively, which indicates thatthe signals acquired during the three activity periods were distinctfrom those acquired during the rest period.

4. Discussion

As the average accuracy, 75.6%, across ten subjects during the2–7 s window was achieved, the proposed method has successfullydemonstrated the capability of distinguishing the hemodynamicresponses corresponding to RI, MA, and LI. To use RI/LI/MA tasks togenerate more than two control commands, a concurrent measure-ment and classification is necessary. To the best of our knowledge,this is the first fNIRS–BCI study that performs a classification ofmore than two brain activities corresponding to signals acquiredconcurrently from the prefrontal and the motor cortices. In the pre-vious work of Power et al. [17], three classes of mental arithmetic,music imagery, and no control state were classified. However, theno control state corresponded to the prefrontal activity during theresting state, which is not an intentionally-evoked cognitive taskapplicable to the control of BCI output. In our case, all three cogni-tive tasks were intentionally-evoked, and, thus, can be used for thepurposes of three-class BCI-output control.

The hemodynamic response lags the neuronal activity byapproximately 2 s and takes approximately 5 s to reach its peakvalue. Therefore, naturally the 2–7 s time window is expected togive more accurate estimation of the change in hemodynamicresponse corresponding to different brain activities and, therefore,results in better classification accuracies.

Due to individual differences, the hemodynamic response and,thus, the classification accuracies varied by subjects. However,none of the subjects showed the average classification accu-racy below the chance level. Additionally, to the inter-individualvariance, there were also some intra-individual variations in clas-sification accuracy over the course of different trials. These areattributable to the trial-to-trial variability in the task-related hemo-dynamic responses, which might be caused by random backgroundactivity or as-yet-unknown sources [18].

An important factor to be noted is that all the partici-pants recruited in this research were healthy. The hemodynamicresponses may differ in the case of people with congenital orafter-injury motor disabilities. This, as reported in [6], might resultin relatively low classification accuracies. To further increase thenumber of control commands and/or to improve the classificationaccuracies for a real-time BCI application, a hybrid fNIRS–EEG sys-tems can be used [25].

5. Conclusions

The present study demonstrated the feasibility of a three-classfNIRS–BCI entailing the classification of fNIRS signals correspond-ing to three different intentionally-generated cognitive tasks. fNIRSsignals corresponding to mental arithmetic, right-hand motorimagery and left-hand motor imagery were acquired simultane-ously from the prefrontal and primary motor cortices. Using thesignal slope and signal mean calculated during the 2–7 s time win-dow, the classification accuracy of 75.6% was achieved.

Acknowledgments

This work was supported by the National Research Foun-dation of Korea under the Ministry of Science, ICT and Future

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lanning, Korea (grant no. NRF-2014R1A2A1A10049727 and NRF-014R1A2A1A01005128).

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