Fractal dimension based neurofeedback in serious games

Transcription

1 Vis Comput DOI /s ORIGINAL ARTICLE Fractal dimension based neurofeedback in serious games Qiang Wang Olga Sourina Minh Khoa Nguyen Springer-Verlag 2011 Abstract EEG-based technology is widely used in serious game design since more wireless headsets that meet consumer criteria for wearability, price, portability, and ease-ofuse are coming to the market. Originally, such technologies were mostly used in different medical applications, Brain Computer Interfaces (BCI) and neurofeedback games. The algorithms adopted in such applications are mainly based on power spectrum analysis, which may not be fully revealing the nonlinear complexity of the brain activities. In this paper, we first review neurofeedback games, EEG processing methods, and algorithms, and then propose a new nonlinear fractal dimension based approach to neurofeedback implementation targeting EEG-based serious games design. Only one channel is used in the proposed concentration quantification algorithm. The developed method was compared with other methods used for the concentration level recognition in neurofeedback games. The result analysis demonstrated an efficiency of the proposed approach. We designed and implemented new EEG-based 2D and 3D neurofeedback games that make the process of brain training more enjoyable. Keywords EEG HCI BCI Neurofeedback Fractal dimension Game design Medical application 1 Introduction Electroencephalogram (EEG) is a noninvasive technique that allows recording the electrical potentials over the scalp Q. Wang ( ) O. Sourina M.K. Nguyen Institute for Media Innovation and School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore , Singapore wang0586@ntu.edu.sg which are produced by activities of brain cortex and reflect the state of the brain [31]. Nowadays, EEG-based techniques have been widely used in BCI applications that help disabled people to communicate with machines [33, 34], in video games as game controllers [25], and in neurofeedback games [42]. With the availability of portable wireless EEG devices, EEG-based applications are no longer confined to the lab environment. Neurofeedback is a technique that presents the real-time feedback to the user in the form of video display and/or sound based on the processing results of EEG signals taken from the scalp of the user [16]. Many researches reveal that EEG and Event Related Potential (ERP) distortions always reflect psychological disorders such as Attention Deficit Hyperactivity Disorder (ADHD) [13, 28], Autistic Spectrum Disorders (ASD) [9, 10, 22, 39], Substance Use Disorders (SUD) including alcohol and drug abuse [35, 36], etc. Similar to other parts of the body, brain functions can be trained as well. Neurofeedback (NF) is a technique for training brain functions and it is an alternative choice for the disorders treatment besides traditional medical treatments. Currently, EEG signal processing algorithms embedded in the neurofeedback games extract a power spectrum or an amplitude feature of the user s EEG signals. These features may not fully reveal the whole complexity of the brain process. More advanced models are needed to analyze nonlinear properties of EEG signals. In this paper, we proposed a new nonlinear fractal dimension approach to neurofeedback implementation. We use fractal dimension algorithms to analyze the complexity of EEG signals. Fractal dimension values are calculated and used to quantify the level of the user concentration. Our hypothesis is that changes in the subject s concentration level could be noticed as changes in fractal dimension values of the EEG signal. The experiment results show that the fractal dimension feature can repre-

2 Q. Wang et al. sent the brain states better than the power spectrum density. 2D and 3D neurofeedback games are implemented based on the fractal dimension model to help the user to improve the player concentration ability. In Sect. 2, neurofeedback games, EEG processing methods and algorithms, and game engines are reviewed. In Sect. 3, a new fractal dimension based method for neurofeedback implementation is introduced. The experiment on concentration level detection and comparison of the proposed algorithm with other methods are described in Sect. 4. The implementation of the proposed fractal dimension based neurofeedback games is introduced in Sect. 5. Finally, conclusion and discussion of future work are given in Sect Related work 2.1 Neurofeedback game applications Many neurofeedback games were assessed by the healing effect of the ADHD, one of the most known psychological disorders with significant EEG distortion. The patients with ADHD have problems to concentrate. The abnormal behavior in θ/β ratio was reported in [8]. Besides the ratio, the distortion in Slow Cortical Potential (SCP) was also notified by [14]. Both frequency neurofeedback training and SCP neurofeedback training can achieve good healing effects for ADHD patients [14, 15]. ASD is another psychological disorder associated with abnormalities of social interactions and communications as well as seriously restricted interests and highly repeated actions [9]. In work [10], EEG analysis during resting condition with open eyes was done for an eight-year-old girl with ASD patterns. The α band and θ band of EEG signal acted abnormally, and the corresponding neurofeedback scheme was designed to rectify the abnormalities. After 21 sessions of the treatment, the sustained attention was enhanced and the ASD symptoms were decreased. Another research group also achieved good results in neurofeedback treatment with standard Quantitative EEG (QEEG) protocol where the aim is to decrease theta band power at the central and frontal brain areas [22]. Similar to ASD, General Anxiety Disorder (GAD) could cause unacceptable social behaviors as well. GAD can also be treated with EEG α band suppression and symmetry training [20]. SUD including drug or alcohol abuse always leads to changes in social behaviors. Neurofeedback was proved to be an affordable alternative treatment for SUD. Chronic alcoholics show significant diminution in α band of EEG signals. The corresponding neurofeedback treatment could decrease the brain waves in this band that was effective in alcoholic patients treatment [35]. For drug abuse, a decreased α band power and an excess of fast beta band activities were detected. In addition, a subject with drug addiction had lower amplitude in P300 ERP component compared to the controlled subject. The addiction was proved to be relieved with the long term neurofeedback treatment [36]. Besides medical applications, neurofeedback could also help a healthy person to enhance his/her brain functions. Researchers indicated that cognitive performances, e.g. cued recall performance, can be enhanced if a healthy person learns how to increase special components of EEG signals with neurofeedback [17, 40]. 2.2 EEG signal processing methods The brain state recognition algorithms embedded in BCI systems especially neurofeedback systems are mainly based on the power spectrum analysis. EEG signal can be divided into several different frequency bands, i.e., δ band (<4 Hz), θ band (4 8 Hz), α band (8 12 Hz), β band (12 30 Hz), and γ band (>30 Hz). The Sensorimotor rhythm activity (12 15 Hz) is also used in several neurofeedback systems. Each frequency band is related to the specific brain functions. Generally, δ band is prevalent in infant s EEG or EEG when the subject is sleeping; θ band is prevalent in EEG when the subject feels drowsiness; α band is significant when the subject is relaxed; β band is associated with fast activities and γ band is related to problem solving and memory work [11]. In the feature extraction step, the power over different bands are assessed and extracted from the user s EEG signals and then rectified with corresponding therapy, e.g., θ/β ratio therapy, or compared with a standard QEEG database (QEEG protocol) to generate the adaptive recovery therapy. In work [29], the θ/β ratio in C4 position according to International System [19] was successfully used in neurofeedback to improve attention. The frequency training method is the most prevalent method used in the neurofeedback training systems and other EEG-based games because the frequency band power is easy to be obtained and analyzed with the existing signal processing tools. Besides, in work [32], the EEG spectrum weighted frequency also showed the ability in attention level recognition. ERP analysis is the process to analyze the EEG signal synchronized with an event. Slow Cortical Potential (SCP) and P300 are important ERP approaches applied in neurofeedback treatments. SCP reflects the changes in cortical polarization, i.e. negative and positive trends, of EEG signals which last from 300 ms to several seconds after an event stimulus [6]. Abnormalities in SCP of ADHD patients were studied in [14], and the corresponding neurofeedback protocol could enhance the continuous performance. The P300 component of ERP occurs during ms after an event stimulus which is obtained by oddball paradigm in which low probability target items are intermixed with high probability nontarget items. Researches indicated that the ampli-

3 Fractal dimension based neurofeedback in serious games tude of P300 component is related to the process of allocation of attention resources and its latency reflects the stimulus evaluation and classification time. The pathology of P300 component in drug abuse patients was reported in [36], and neurofeedback based on P300 component training was proposed. Although the signal processing algorithms embedded in neurofeedback games are well applied in clinical treatments, the linear features, e.g., power spectral density and amplitude, extracted from EEG cannot represent the brain activities perfectly due to the nonlinearity of EEG signal. Nonlinear methods, e.g., entropy analysis and fractal dimension analysis, have become popular in EEG processing for medical applications [23, 24, 37, 38] and been applied to neurofeedback systems [41] to model brain activities and EEG based emotion recognition system [27]. With effective nonlinear EEG features, the accuracy of brain state recognition could be improved, thus the treatment performance of the neurofeedback games would be enhanced. 2.3 Game engines EEG-based games consist of two parts: signal processing and game implementation. Game implementation can be effectively done with the help of game engines. Game engines are tools that programmers use to design and implement games. They provide ready-made utilities or tools to develop a game. According to Ward [43], three types of game engines are frequently seen: roll-your-own game engines, mostly-ready game engines, and point-and-click engines. Roll-your-own game engines, including OpenGL and DirectX, require the game makers to be well-versed in programming, and it takes a lot of time to design a game. However, they give the game makers flexibility and more freedom in designing their own components for the game. Mostly-ready game engine is most popular in the market. Renderer, physics engine, collision detection, graphic, sound system, etc. are usually available in these game engines. OGRE, Panda3D, Unreal, etc. belong to this kind of game engine. A point-and-click engine is the highest level game engine that requires least programming knowledge. However, they are quite limited in the number of the provided readymade functions. These engines include Alice, Game Maker, etc. As EEG-based games include signal processing, a game engine should support programming language C++, Python or any other scripting environments which allow EEG recognition and interpretation. In our neurofeedback games, SDL [4] game engine and Flash CS3 were adopted for 2D games; Panda3D [2] and Unreal Engine [5] were applied in 3D games. Most of them are open source and can be easily integrated with our EEG signal processing methods. 3 Methodology 3.1 EEG-based game design In the past, traditional neurofeedback games were implemented for clinical applications with complex EEG devices which were hard to be set up. Recently, more and more portable and wireless EEG devices became available. More effective EEG processing algorithms which could be used with fewer electrodes in real-time applications are on demand. In this paper, we proposed a design of neurofeedback concentration games based on fractal dimension model with one-channel EEG signal. Different from traditional neurofeedback games, we focus on the monitoring of the brain state recognized from the EEG using fractal dimension model. The main idea of concentration games is using the neurofeedback to encourage the player to improve his/her level of concentration. The proposed EEG analysis method is based on fractal dimension model which could capture any changes in the brain state. In order to develop a real-time application, fewer channels and faster signal processing algorithms should be used. In our implementation, only one channel located in occipital lobe is selected as the occipital lobe is responsible for visual perception and visual attention [26, 30]. Entropy based fractal dimension model [21] is used to distinguish brain states such as relaxed and concentrated. 3.2 Previous approaches The main task in our research is brain state recognition, especially concentration level recognition. There are methods for attention level recognition. In work [29], θ/β ratio is used for attention level estimation. In work [32], so-called brain rate is used as a reliable attention level indicator. Both methods are based on the analysis of power spectrum density of EEG signals. In the θ/β ratio method, a power spectrum density of EEG signals could be estimated with periodogram method. Discrete Time Fourier Transform (DTFT) with fixed number of samples (N) is applied to estimate the power density spectrum (PSD) as shown in (1) and (2): DTFT { x(n) } = X ( e jω) = n= x(n)e jωn, (1) PSD { x(n) } = S(ω) = 1 X ( e jω) 2, (2) N where x(n) is the input EEG signal, X(e jω ) is the DTFT of EEG signal, and S(ω) is the power spectrum density of EEG signal. The θ and β band power could be estimated by integrating the power spectrum within the θ band (4 8 Hz) and β band (12 30 Hz) respectively. Then the ratio of the

4 Q. Wang et al. Fig. 1 Mono-fractal Weierstrass signal (a) fractal dimension value is 1.1 (b) fractal dimension value is 1.7 θ band power to β band power that indicates the attention level can be obtained as follows: θ/β ratio = θ band power β band power = 2π 8/F s 2π 4/F s S(ω)dω 2π 30/F s (3) 2π 12/F s S(ω)dω, where Fs is the sampling frequency of the EEG device. In the brain rate method, the PSD is also estimated first with periodogram method as illustrated before. The brain rate can be calculated as spectrum weighted frequency as follows: f b (brain rate) = 2π ωs(ω)dω Fs, (4) S(ω)dω where Fs is the sampling frequency of the EEG device. 3.3 Fractal dimension model We proposed to use fractal dimension as a feature instead of the power of EEG signals in the implementation of neurofeedback in serious games. Fractal dimension could be used as a measurement of complexity and irregularity of a signal. In signal processing, Higher fractal dimension value means that the signal is more complex, while lower fractal dimension (FD) value means that the signal is more regular. In Fig. 1a and Fig. 1b, examples of monofractal Weierstrass signals with low FD value 1.1 and high FD value 1.7 are shown. In our real-time implementation, Higuchi [18] and box-counting [7] algorithms were chosen for FD calculation. Let us briefly describe the algorithms. In the Higuchi method, the samples are first clustered into several subsequences according to the poly-phase structure as follows: X m k : x(m),x(m + k),x(m + 2k),..., ( ( ) ) N m x m + k. (5) k The length of the sequences L(k) is calculated according to (6) and (7): [( ( N m k ) L k (m) = 1 x(m + ik) k i=1 ) ] x ( m + (i 1)k ) N 1 ( N m k ) k, (6) L(k) = 1 k k L k (m), (7) m=1 where k denotes the number of the subsequences and L k (m) denotes the length of the mth subsequence. The total length L(k) is proportional to k FD : L(k) k FD, (8) Algorithm 1 Higuchi method kmax 2 log 2(length(x)) 4 xlist emptylist() {list for storing data in x direction for calculating the slope} ylist emptylist() {list for storing data in y direction for calculating the slope} for k = 1tokMax do sumlk 0 for m = 1tok do Xk m extractsubsequence(x) lmk SumSubSequence(Xk m) sumlk sumlk + lmk m m + 1 end for lk sumlk/k xlistaddelement(log(1/k)) ylistaddelement(log(lk)) k k + 1 end for fd getslope(xlist, ylist)

5 Fractal dimension based neurofeedback in serious games Fig. 2 Boxes construction in box-counting method, the dark boxes are counted where FD is the fractal dimension value and k is the timedelay information. The fractal dimension value could be calculated as follows: log L(k) FD = log k. (9) The algorithm is shown in Algorithm 1. Box-counting method can calculate the fractal dimension values of the signal in time domain without any subsequence extraction steps. The main step of box-counting method is box construction. Unified and normalized boxes are constructed in 2D space (time-amplitude) which can cover one segment of the signal. Finally, the number of occupied boxes is counted. The boxes constructing and counting processes are shown in Fig. 2. The number of counted boxes N(d) is proportional to d FD : N(d) d FD, (10) where d denotes the length of the side of the boxes. The fractal dimension value could be calculated as follows: log N(d) FD = log d. (11) The algorithm is shown in Algorithm 2. Higuchi and box-counting methods were compared in both computation complexity and accuracy. Brownian motion and Weierstrass monofractal signals whose theoretical fractal dimension values are known were used for the Algorithm 2 Box-counting method kmax log 2 (length(x)) 1 xlist emptylist() {list for storing data in x direction for calculating the slope} ylist emptylist() {list for storing data in y direction for calculating the slope} for k = 1tokMax do d 2 k ConstructNormalizedBoxes {X Y directions have the same number of sides} Nd = countbox() {Count the no. of boxes occupied by the EEG signal} xlistaddelement(log(1/d)) ylistaddelement(log(nd)) k k + 1 end for fd getslope(xlist, ylist) comparison. The results are shown in Fig. 3. Although the Higuchi method is slower than box-counting method, the accuracy of the Higuchi method is better than the box-counting method in FD value evaluation for both Brownian motion and Weierstrass signals. In our work, both algorithms were used for neurofeedback implementation. 3.4 Classifier comparison and threshold evaluation Receiver Operating Characteristics (ROC) graph is a technique for comparing the performance of classifiers or method of threshold selection, which is especially useful in two classes case [12]. In a two classes training situation, a score is calculated for all samples with labels. A threshold is needed to classify all these labeled samples into two classes according to the score calculated in the feature extraction process. For each specific threshold, a confusion matrix can be constructed (shown in Fig. 4a) and the true positive (tp) rate and false positive (fp) rate can be evaluated as follows: tp = positives correctly classified, (12) total positives Fig. 3 The comparison of Higuchi and box-counting algorithms for FD evaluation over (a) fractional Brownian Motion signals and (b) Weierstrass signals

6 Q. Wang et al. Fig. 4 Illustration of ROC (a) confusion matrix (b)roc space (c) comparison of two classifiers negatives incorrectly classified fp =. (13) total negatives The ROC space can be constructed with the fp rate as x axis and tp rate as y axis. The performance of the threshold could be drawn as a point in ROC space (shown in Fig. 4b, the threshold corresponding to point A has best performance). If the point is closer to the northwest corner in ROC space, the accuracy is higher. A ROC curve can be generalized when different thresholds are used. The best threshold is chosen according to the distance of the corresponding ROC point to the northwest corner. The performance of the classifier could be evaluated by the area of the southeast ROC plane separated by the ROC curve (shown in Fig. 4c). The bigger area means that the average performance of the classifier is better. In Fig. 4c, the classifier with ROC curve in red has better average performance even though the best accuracy is worse. 4 Experiments and results Because there is no standard database and benchmark available for the concentration (or attention) level recognition, an experiment on brain state classification was set up to distinguish relaxation and concentration states. Five subjects aged from 22 to 30 were invited to participate in the experiment. In the first session, in order to induce the relaxation state, a comfortable environment was set up to help the subject relax. In the second session, in order to induce the concentration state, the subjects were required to complete several math problems. Only one electrode was used and placed in O1 position according to the international system [19] in occipital lobe which is associated with visual perception. EEG signals were recorded by the Emotiv [35] device with sampling frequency of 128 Hz and 16-bit A/D resolution. The EEG data of two sessions were divided into thousands of pieces. Each piece of data had 1,024 samples and was labeled according to the induced brain states. Four different methods, i.e. Higuchi method, box-counting method, θ/β ratio method and brain rate, were applied for the EEG signal processing and evaluated with ROC curve. The data were processed with Matlab on an Intel Core 2 Quad CPU Q9400 (2.66 GHz) with a 3.25 GB main memory. The results of the brain state recognition experiment are shown in Fig. 5 and Table 1. Figure 5 gives an intuitive comparison of four methods we evaluated with ROC. In most cases, the curves for Higuchi and box-counting methods are closer to the northwest corner and cover more area in ROC domain. This implies that Higuchi and box-counting methods could achieve a better result than the θ/β ratio and brain rate methods. Table 1 gives the mean and variance of the accuracy and best threshold for 5 subjects in four different methods. The accuracy of Higuchi method is 88.11% with the variance of and the accuracy of box-counting method is 86.54% with variance of Both of them are better than θ/β ratio method of which the accuracy is 80.26% with variance of and brain state method of which the accuracy is 84.93% with variance of The variance in threshold also shows the boxcounting and Higuchi methods have relatively smaller difference in thresholds for different subjects. This result implies that, for different users, the changes in threshold might be insignificant when fractal dimension model is adopted in neurofeedback games. The average time for processing of 1024 samples was also given in Table 1. The Higuchi method is slower than the other methods. However, its speed is fast enough for real-time processing when the sampling frequency is 128 Hz. Both Higuchi and box-counting methods were implemented as fractal dimension calculation methods and then were applied in the neurofeedback games. The efficiency of these two methods is shown in Fig. 6 with boxplot. The states of relaxation and concentration can be separated by FD values for all five subjects. In both Higuchi and boxcounting algorithms, the experiment results show that concentration level can be distinguished for 80% of the subjects when a default threshold is set to 1.93 in Higuchi method and 1.60 in box-counting method. For 100% of the subjects, the concentration level can be recognized with

7 Fractal dimension based neurofeedback in serious games Fig. 5 ROC curve of brain state recognition methods for 5 subjects Table 1 Comparison of brain state recognition methods Higuchi Box-counting Brain rate θ/β ratio Accuracy Mean Var Best threshold Mean Var Time consuming (s) Mean a trained threshold. It is clear that fractal dimension model can be used to distinguish the relaxation and concentration states with a simple threshold even though there are overlaps which may be due to individual differences in EEG. A short training session can be applied to determine the default threshold to minimize the individual effects. 5 Neurofeedback games implementation Fig. 6 The comparison of (a) Higuchi method and (b) Box-counting method in FD evaluation of the EEG signals in different brain states for all subjects 2D neurofeedback games such as Brain Chi and Pipe, and 3D neurofeedback games such as Dancing Robot, Escape were proposed and developed for concentration level control. Brain Chi was developed with SDL game engine [4], Pipe with Flash CS3, Dancing Robot with Panda3D [2], and Escape was developed with Unreal Engine [5]. All games were scripted and compiled under Microsoft Visual C++ environment except Escape and Pipe which were scripted with Unreal Script and Action- Script, respectively.

8 Q. Wang et al. Fig. 7 Concentration based neurofeedback flowchart Fig. 8 Hardware setup of the game (a) neurofeedback system hardware (b) equipped user 5.1 Neurofeedback hardware setting EEG data could be acquired by Emotiv [1] or PET 2 EEG devices. Only O1 electrode (according to the international system) in Occipital lobe is active, and the EEG signal is transmitted to computer with Bluetooth. The algorithm uses 2 42 Hz band-pass filter first, then it calculates fractal dimension values of the input EEG signals in real-time with Higuchi method or box-counting method and labels them with different brain states according to the adaptive thresholds. The default threshold used to distinguish the concentration state and relaxation state is set up to 1.93 in Higuchi based method and 1.6 in box-counting based method. The threshold is adaptive to the user s current state. The data acquisition and processing algorithm flowchart is shown in Fig. 7, and hardware setup of the neurofeedback games is showninfig.8a. The user playing neurofeedback game Dancing Robot with Emotiv device is shown in Fig. 8b. 5.2 Game strategy The proposed games have the following general game strategy. If the concentration level has been achieved by the user, points are rewarded to encourage the player. With decrease of the concentration level the player could lose the reward points. The concentration based game strategy is presented in the flowchart in Fig. 7. The game strategy could be changed to relaxation game if the player wanted to be trained how to relax. Dancing Robot is a simple 3D single-player game. In this game, the player is required to control the speed of the robot while the robot is dancing. A screenshot of the Dancing Robot game is shown in Fig. 9a. The speed of the robot dance depends on the concentration level of the player. The player concentrates to make the robot dancing faster. The robot dances sluggishly if the player is distracted. An adaptive threshold is used for training purpose.

9 Fractal dimension based neurofeedback in serious games Fig. 9 Screenshots of neurofeedback 2D and 3D games (a) Dancing Robot (b) Brain Chi (c) Pipe (d) Escape Brain Chi is a 2D single-player EEG-based game. A screenshot of Brain Chi is shown in Fig. 9b. The player controls the game simply by using his/her brain power of concentration. His/her task is to help a little boy hero to fight against evil bats using a protection ball. The size of the protection ball is controlled by the concentration level of the player. To win the game, the player needs to increase the ball by concentration to eliminate all the bats. Pipe is a classic pipe game where the player tries to arrange pieces of pipes on a board so the water can be delivered between two stations without leaking. This game is slightly different from the above two games. The recognized brain state could influence on the game s flow. For example, distraction could cause the time allocated to the player to be reduced, and hence upon recognizing this, the player is expected to put more effort in concentrating. In Fig. 9c, water level (green bar) serves as a time indicator and concentration level (blue bar) serves as a concentration level. The blue bar has effect on the rate of change of the green bar. Escape is also a 3D single-player game. However, this game has an educational purpose. The story in the game requires the player to solve the educational puzzles so he /she could get the passwords to unlock the doors. In this game, EEG could be used as an alternative way to get the passwords when the player cannot figure out how to solve those puzzles. The player has to stay concentrated for a specified time, and the password will be given to him. If the player uses brain power help, the overall game time allocated for the player to escape is reduced. The screenshot of the game isshowninfig.9d. Examples of the games are given in [3]. 6 Conclusion and future work In this paper, we reviewed EEG-based neurofeedback games and algorithms embedded in the neurofeedback games. We proposed a novel fractal dimension based method to quantify concentration level of the brain state. The proposed method was compared with other methods used for concentration level recognition in neurofeedback games. The comparison results showed that both Higuchi and box-counting algorithms could be effectively used for feature extraction in the proposed method. The brain states were recognizable with the difference in fractal dimension values. Original 2D and 3D EEG-based games such as Brain Chi, Dancing Robot, Pipe, and Escape were designed and implemented for concentration training. The fractal dimension algorithms were embedded in the neurofeedback games to enhance the efficiency of the neurofeedback. More research is planned in the future to compare the efficiency of the proposed fractal dimension approach and

10 Q. Wang et al. traditional signal processing approaches in neurofeedback games. The possibility of applying the fractal dimension based neurofeedback in pain management would be studied as well. Acknowledgements This project is supported by grant NRF2008- IDM-IDM of National Research Fund of Singapore and by Institute for Media Innovation. References 1. Emotiv: URL 2. Panda3d: URL 3. Project personalized digital media experience : URL www3.ntu.edu.sg/home/eosourina/projects.html 4. Simple directmedia layer: URL 5. Unreal engine: URL 6. Birbaumer, N.: Slow cortical potentials: plasticity, operant control, and behavioral effects. Neuroscientist 5(2), (1999) 7. Block, A., Von Bloh, W., Schellnhuber, H.J.: Efficient boxcounting determination of generalized fractal dimensions. Phys. Rev. A 42(4), (1990) 8. Clarke, A.R., Barry, R.J., McCarthy, R., Selikowitz, M.: Electroencephalogram differences in two subtypes of attentiondeficit/hyperactivity disorder. Psychophysiology 38(2), (2001) 9. Coben, R., Linden, M., Myers, T.E.: Neurofeedback for autistic spectrum disorder: a review of the literature. Appl. Psychophysiol. Biofeedback 35(1), (2010) 10. Cowan, J.D., Markham, L.: EEG biofeedback for the attention problems of autism a case study. Biofeedback Self-Regul. 19(3), (1994) 11. Demos, J.N.: Getting Started with Neurofeedback. WW Norton & Company, New York (2005) 12. Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), (2006) 13. Fuchs, T., Birbaumer, N., Lutzenberger, W., Gruzelier, J.H., Kaiser, J.: Neurofeedback treatment for attentiondeficit/hyperactivity disorder in children: a comparison with methylphenidate. Appl. Psychophysiol. Biofeedback 28(1), 1 12 (2003) 14. Gevensleben, H., Holl, B., Albrecht, B., Schlamp, D., Kratz, O., Studer, P., Wangler, S., Rothenberger, A., Moll, G.H., Heinrich, H.: Distinct EEG effects related to neurofeedback training in children with ADHD: a randomized controlled trial. Int. J. Psychophysiol. 74(2), (2009) 15. Gevensleben, H., Holl, B., Albrecht, B., Vogel, C., Schlamp, D., Kratz, O., Studer, P., Rothenberger, A., Moll, G.H., Heinrich, H.: Is neurofeedback an efficacious treatment for ADHD? A randomised controlled clinical trial. J. Child Psychol. Psychiatry Allied Discipl. 50(7), (2009) 16. Hammond, D.C.: What is neurofeedback? J. Neurother. 10(4), (2006) 17. Hanslmayr, S., Sauseng, P., Doppelmayr, M., Schabus, M., Klimesch, W.: Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects. Appl. Psychophysiol. Biofeedback 30(1), 1 10 (2005) 18. Higuchi, T.: Approach to an irregular time series on the basis of the fractal theory. Physica D 31(2), (1988) 19. Homan, R.W., Herman, J., Purdy, P.: Cerebral location of international system electrode placement. Electroencephalogr. Clin. Neurophysiol. 66(4), (1987) 20. Kerson, C., Sherman, R.A., Kozlowski, G.P.: Alpha suppression and symmetry training for generalized anxiety symptoms. J. Neurother. 13(3), (2009) 21. Kinsner, W.: A unified approach to fractal dimensions. In: Proc. ICCI 2005: Fourth IEEE International Conference on Cognitive Informatics, pp (2005) 22. Kouijzer, M.E.J., van Schie, H.T., de Moor, J.M.H., Gerrits, B.J.L., Buitelaar, J.K.: Neurofeedback treatment in autism, preliminary findings in behavioral, cognitive, and neurophysiological functioning. Res. Autism Spectr. Disord. 4(3), (2010) 23. Kulish, V., Sourin, A., Sourina, O.: Analysis and visualization of human electroencephalograms seen as fractal time series. J. Mech. Med. Biol. 6(2), (2006) 24. Kulish, V., Sourin, A., Sourina, O.: Human electroencephalograms seen as fractal time series: mathematical analysis and visualization. Comput. Biol. Med. 36(3), (2006) 25. Lécuyer, A., Lotte, F., Reilly, R.B., Leeb, R., Hirose, M., Slater, M.: Brain-computer interfaces, virtual reality, and videogames. Computer 41(10), (2008) 26. Li, Z.H., Coles, C.D., Lynch, M.E., Ma, X.Y., Peltier, S., Hu, X.P.: Occipital-temporal reduction and sustained visual attention deficit in prenatal alcohol exposed adults. Brain Imaging Behav. 2(1), (2008) 27. Liu, Y., Sourina, O., Nguyen, M.K.: Real-time EEG-based human emotion recognition and visualization. In: Proc Int. Conf. on Cyberworlds, pp (2010) 28. Lubar, J.F., Swartwood, M.O., Swartwood, J.N., O Donnell, P.H.: Evaluation of the effectiveness of EEG neurofeedback training for ADHD in a clinical setting as measured by changes in t.o.v.a. scores, behavioral ratings, and Wisc-r performance. Biofeedback Self-Regul. 20(1), (1995) 29. Lutsyuk, N.V., Éismont, E.V., Pavlenko, V.B.: Modulation of attention in healthy children using a course of EEG-feedback sessions. Neurophysiology 38(5 6), (2006) 30. Murray, S.O., Wojciulik, E.: Attention increases neural selectivity in the human lateral occipital complex. Nat. Neurosci. 7(1), (2004) 31. Nunez, P.L., Srinivasan, R.: Electric Fields of the Brain. Oxford University Press, New York (2006) 32. Pop-Jordanov, J., Pop-Jordanova, N.: Neurophysical substrates of arousal and attention. Cogn. Process. 10(1), (2009) 33. Rebsamen, B., Burdet, E., Guan, C., Zhang, H., Teo, C.L., Zeng, Q., Ang, M., Laugier, C.: A brain-controlled wheelchair based on p300 and path guidance. In: Proc. 1st IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, pp (2006) 34. Rebsamen, B., Teo, C.L., Zeng, Q., Ang, M.H. Jr, Burdet, E., Guan, C., Zhang, H., Laugier, C.: Controlling a wheelchair indoors using thought. IEEE Intell. Syst. 22(2), (2007) 35. Saxby, E., Peniston, E.G.: Alpha-theta brainwave neurofeedback training: an effective treatment for male and female alcoholics with depressive symptoms. J. Clin. Psychol. 51(5), (1995) 36. Sokhadze, T.M., Cannon, R.L., Trudeau, D.L.: EEG biofeedback as a treatment for substance use disorders: review, rating of efficacy, and recommendations for further research. Appl. Psychophysiol. Biofeedback 33(1), 1 28 (2008) 37. Sourina, O., Kulish, V., Sourin, A.: Novel tools for quantification of brain responses to music stimuli. In: Proc. 13th International Conference on Biomedical Engineering, pp (2009)

11 Fractal dimension based neurofeedback in serious games 38. Sourina, O., Sourin, A., Kulish, V., Gagalowicz, A., Philips, W.: EEG data driven animation and its application. In: Proc. Computer Vision/Computer Graphics Collaboration Techniques, pp (2009) 39. Thompson, L., Thompson, M., Reid, A.: Neurofeedback outcomes in clients with asperger s syndrome. Appl. Psychophysiol. Biofeedback 35(1), (2010) 40. Vernon, D., Egner, T., Cooper, N., Compton, T., Neilands, C., Sheri, A., Gruzelier, J.: The effect of training distinct neurofeedback protocols on aspects of cognitive performance. Int. J. Psychophysiol. 47(1), (2003) 41. Wang, Q., Sourina, O., Nguyen, M.K.: EEG-based serious games design for medical applications. In: Proc Int. Conf. on Cyberworlds, pp (2010) 42. Wang, Q., Sourina, O., Nguyen, M.K.: Fractal dimension based algorithm for neurofeedback games. In: Proc. CGI 2010, p. SP25 (2010) 43. Ward, J.: What is a game engine? URL gamecareerguide.com/features/529/what_is_a_game.php Qiang Wang is a Ph.D. candidate of the School of Electric & Electronic Engineering and Institute for Media Innovation, Nanyang Technological University, Singapore. His research interests include EEG signal processing, neurofeedback game design, and 3D virtual reality medical therapy. Olga Sourina is an Assistant Professor in Nanyang Technological University, Singapore. She earned the M.Sc. in Computer Engineering from Moscow Engineering Physics Institute in 1983, and her Ph.D. in Computer Science from NTU in She is a senior member of the IEEE Computer Society. Dr. Sourina s research interests are in data mining, bioengineering, human-computer interfaces, brain-computer interfaces, computer graphics, and virtual reality. For her scientific achievements, Dr. Sourina was awarded the honorary diploma of the Academy of Sciences of the USSR, the Silver Medal of the National Exhibition Centre of the USSR, and the Medal of the Ministry of Education of the USSR. Minh Khoa Nguyen obtained a Bachelor Degree in Electrical and Electronics Engineering (EEE) from Nanyang Technological University (NTU) in He is now working as a project officer for the School of EEE in NTU. His current research topic is the application of EEG in medical therapy and entertainment.