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1EEG Visualization and Analysis Techniques
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3We present some information depicting the current status of EEG research with projected applications in the areas of health care. We describe a method of quick prototyping an EEG headset, in a cost-effective way and with state-of-the-art technologies. We use meditation research to reach out to the high-end applications of EEG data analysis in understanding human brain states and assisting in promoting human health care. Some devotees to the practices of transcendental meditation have shown the ability to control these brain states. We want to numerically prove or disprove this assumption; the analysis of these states could be the initial step in a process to first predict and later allow individuals to control these states. To this end, we begin to build a system for dynamic and onsite brain state analysis using EEG data. The system will allow users to transit EEG data to an online database through mobile devices, interact with the web server through web interface, and get feedback from EEG data analysis programs on real-time bases. Background Using electroencephalographic (EEG) data, cognitive psychologists can visualize and observe correlations between different active brain states. It is desirable to create an application that takes EEG data and exposes it to various analytical techniques so the resultant brain states can be studied and predicted. We present explanations of the design and implementation offered herein. The presentation will consist of an extrication of the design of an EEG headset, which can collect EEG, pulse, and temperature data, and a case study in which EEG signals demonstrate differences between different brain states. An EEG device can record the electric signals from a human scalp. EEG devices used to be only available in professional healthcare institutions for 
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5clinic use. Last decade witnessed the development of cheap EEG devices, for example, EPOC from Emotiv ( http://​www.​emotiv.​com ) and NeuroSky ( http://​www.​neurosky.​com/​ ), and increasing interest in EEG-based brain–computer interfaces (BCI). EEG signals characterize the result of the neuron activities inside a human brain. Naturally, they are used to study and understand human brain activities. In particular, EEG signals indicate that neural patterns of meanings in each brain occur in trajectories of discrete steps, while the amplitude modulation in EEG wave is the mode of expressing meanings [ 1 ]. Zhou et al. have proposed some novel features for EEG signals to be used in brain–computer interface (BCI) system to classify left- and right-hand motor imagery [ 2 ]. The experimental results have shown that based on the proposed features, the classifiers using linear discriminant analysis, support vector machines, and neural network achieve better classification performance than the BCI-competition 2003 winner on the same data set in terms of the criteria of either mutual information or misclassification rate. Dressler et al. studied the anaesthetics on the brain and the level of sedation [ 3 ]. Lin et al. studied the change of human emotion during music listening through EEG signals [ 4 ]. The vast implications of using EEG data to analyze brain states include designing brain–computer interfaces (BCI) where users can operate on a machine via brain activities, and using brain state models in healthcare-related activities. Imagine a world where mere thinking about retrieving information will give you the results you are looking for; a world that no longer requires a keyboard, mouse, or traditional hardware input devices to interface with a computer; a place where you can instantly find out your health information in real time. Imagine a device that can instantly retrieve your body and mind condition and share this information with a medical expert that can then immediately analyze the data and make an appropriate health diagnosis. Instead of reacting to a condition that may already have not be able to know exactly what you are thinking, but we can gather brainwave data and make it available for analysis. We can control computers with mere thought! The methods may still be somewhat primitive and the technology in its infancy, but nevertheless, with only a few inexpensive off the shelve parts and a little ingenuity we can create a device that is capable of sensing body conditions and even read brainwaves. As an example, we present a case study in transcendental meditation [ 5 ], a spiritual development technique, which was popularized by former Hindu ascetic Mharishi Mahesh Yogi and gained popularity in the west during the 1960s [ 6 ]. The concurrent brain states associated with transcendental meditation have been viewed as something outside of the world of physical measurement and objective evaluation by most scientific communities. Scientists now have the ability to measure and register electric potential of the human brain through the use of electroencephalographic technologies. One approach is to study finite differences within the minds of those practicing meditation, and those who do not. Such an endeavor is an avenue towards modeling a wide range of brain states [ 7 ]. The combination of electroencephalographic data with modeling methods in fields such as data mining and bioinformatics could be used to prove that subjects in a state of transcendental meditation are in a verifiable and observable state of mind that can be monitored and predicted [ 5 ]. Experiments found that cancer patients that practiced meditation experienced higher well-being levels, better cognitive function and lower levels of inflammation than a control group [ 8 ].
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7Challenges The challenges in EEG-related studies include the design of the measuring tools and the methodologies in analyzing EEG data. Here we extricate a method to build an inexpensive headset to measure brainwaves. An EEG is a tool used to capture brainwave activity while it is performing a cognitive task. This allows the detection of the location and magnitude of brain activity involved in the various types of cognitive functions. EEGs allow the viewing and recording of the changes in brain activity during the time a task is performed. EEGs for this purpose have been around for many years, albeit only in medical research facilities and typically being very expensive. The intrigue is being able to inexpensively build an EGG with off-the-shelf parts and be able to perform the same type of brainwave research at home as sophisticated medical research facilities. In addition, a platform for comprehensive EEG data storage and processing is desirable to promoting applications of using EEG tools in both physiological (e.g., clinical uses, sleep evaluation, fatigue detection, etc.) and psychological (cognitive sciences, BCI, etc.) scopes. Such a platform consists of EEG data collection devices (viz., EEG headset), communication channels (e.g., smart phones), a web server that provides a web interface for users to access stored EEG data and activate data analysis algorithms, and an online database for EEG data storage and processing. The primary motivation behind this is to know what signals the brain produces does under certain situations and to know how these signals are consciously manipulated via controlled thoughts. Additionally it is desirable to know if there is a way to enhance studying and learning abilities and being able to retain more information. As an example, attempts have been made to is having one’s every whim under observation. Through this study, it is anticipated to start a campaign to establish “measurable” meditation methods, applying scientific methodology to religions, and eventually making religions “tangible”. Current Techniques A cursory look into the topic revealed a wealth of information, much theoretical and limited to large government organizations and research facilities with huge budgets. For instance, the government has a program called the “Brain Research through Advancing Innovative Neurotechnologies™ (BRAIN)”. The web site states the following: “The Brain Research through Advancing Innovative Neurotechnologies™ (BRAIN) Initiative is part of a new Presidential focus aimed at revolutionizing our understanding of the human brain. By accelerating the development and application of innovative technologies, researchers will be able to produce a revolutionary new dynamic picture of the brain that, for the first time, shows how individual cells and complex neural circuits interact in both time and space. Long desired by researchers seeking new ways to treat, cure, and even prevent brain disorders, this picture will fill major gaps in our current knowledge and provide unprecedented opportunities for exploring exactly how the brain enables the human body to record, process, utilize, store, and retrieve vast quantities of information, all at the speed of thought”. The site even contains funding opportunities for companies and research facilities to participate and contribute to the program. Examples such as this can be
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9found in abundance and what quickly becomes apparent is that there is a thirst for more knowledge about the human brain and how it works. Very little information exists in the hobby and home space for EEG devices. Organizations such as OpenEEG and OpenBCI are available and facilitate the information sharing among hobbyists and attempt to inform the general public about the subject of gathering brainwave data. Companies like NeuroSky and Emotive sell headset EEG devices and provide software development kits (SDK) that include the tools necessary to gather brainwave data, but are limited to only reading brainwaves. In research perspectives, there is still space to gather more information, to have an enhanced data model, and see additional dependencies while the brain performs or reacts to specific tasks. On the brain state modeling side, two types of research models have been used: statistical models and micro-models. Statistics models are built by applying statistical analysis to collected data from meditation practitioners, while micro-models try to catch physiological features of the brain state under examination. Current literatures show that both methods are used in the study of complementary and alternative medicine, which includes meditation as one of the methods. Loizzo et al., performed a 20-week contemplative self-healing program study, which showed that a contemplative self-healing program can be effective in significantly reducing distress and disability among the testers [ 9 ]. Habermann et al., on the other hand, performed a long-term (5–20 years) project to investigate the use of complementary and alternative medicine and its effects on the testers’ health [ 10 ]. Comparisons across different groups of people are also found. For example, in a 6-week mindfulness-based stress-reduction program, subjects assigned to the program demonstrated significant improvements in psychological status and quality of life compared with usual care [ 11 ]. Another comparison is found where a group of Qigong practitioners were study [ 12 ]. A survey of the literature on cognitive impairment and cancer presented in [ 13 ] suggests that meditation may help improve cancer-related cognitive dysfunction and alleviate other cancer-related sequelae. It is well understood that although statistical studies can provide evidence for the effectiveness of meditation, it fails to provide a systematic view of human’s epistemology and psychology. This addresses the needs for micro-models that depict the inter-relationship between human’s mind and physical body. To accurately and objectively record moods when one is practicing meditation, we seek a solution which could objectively measure the effectiveness of meditation in real time. We start with a project that aims to create an application that takes EEG data and exposes it to various analytical techniques so the resultant brain states can be studied and predicted. We anticipate that, upon completion, this software can be used to produce important and dependable conclusions about a given subject’s brain states and correlate that to an identified physical or psychological activity, and ultimately, we will be able to build a brain state model for meditation. The concurrent brain states associated with transcendental meditation were viewed as something outside of the world of physical measurement and objective evaluation by most scientific communities. Due to the easily obtainable EEG headsets, recording EEG signals can be performed in a large scale. It is therefore possible to build a model for meditation brain state [ 14 ]. By applying data mining algorithms that quantify psychological states, we expect to analyze brain state associated with meditation to build models for meditation brain states [ 5 ]. Comparisons of the results obtained from different methods can be performed to fuse different models in order to have a deeper understanding of the central and peripheral nervous systems’ role
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11in attaining different levels of mediation. The practical significance of finding a meditation model includes establishment of the guidance for effective meditation exercises and a methodology for verifying the effectiveness of meditation methods. A scientific meditation model will advance studies in natural computer interface, identifying depression and mental illness, detect fatigue and boredom, and comprehending human emotion, etc. Any advances in these areas will have great social, economic, and technical significance. We have conducted a trial collection of EEG data on a patient who performed MQ and a few other subjects performing tasks with different levels of brain activities, including the attempts of resting the brain. The Emotiv EPOC headsets we used can collect 14 channels of EEG signals. The locations of the contact points on the scalp, called nodes, are demonstrated in Fig. 1 . idle activity, reading news headlines, and participating in a mathematics exam limited to basic algebra every 60 s for 20 min. This data include 123,001 samples for all 20 min. With 129 samples per packet we have approximately 953 packets per node. We can assume that this leaves approximately 47 packets per node per minute. Then we apply linear regression to the generated scatter-plot, the positive or negative slope correlates to an increase or decrease in brain activity for the entire packet of said node. In the first packet of our data set, we notice correlations between two sets of four nodes ({AF3 F3 FC5 F7} and {AF4 F8 FC6 F4} respectively). By cross examining node position from Fig. 1 with the first packet of each node in Fig. 2 , we can observe similar scatter-plots that are geometrically symmetric when referencing nodes regarding both left and right hemispheres of the frontal lobe. This tells us that {AF3 F3 FC5 F7} and {AF4 F8 FC6 F4} are sections of the brain that work together when the user is in an idle brain state. Our efforts are currently invested into recognizing repeatable patterns throughout the packets. Our developed signal processing algorithms will be used to determine repeatable characteristics in sets of packets that belong to each of the idle, news headline, and mathematics test states.
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13Fig. 2 Packet 1 of Nodes AF3, F7, F3, FC5, F4, FC6, F8, and AF4 We also have developed a first version of an iOS application for iPad to analyze and visualize collected EEG data. This application can parse and visualize EEG data. It is also possible to extend this app to collect EEG data built consists of an EEG sensor, a pulse sensor, a temperature sensor, a microprocessor, and a microprocessor blue tooth shield. (1) EEG sensor, commercial product from NeuroSky. The NeuroSky technology was chosen for its dry sensors capabilities. This means that the sensor requires no special liquid chemicals while making contact with the skin to read brainwaves. (2) Pulse Sensor, Open Source pulse sensor from pulsesensor.com. The pulse sensor is a current to voltage converter Op Amp circuit that uses a photodiode as current source. It has a low-pass Filter for output. (3) Temperature sensor, commercial integrated circuit sensor, TMP36—Analog Temperature sensor from Adafruit. The TMP36 temperature sensor is a solid state device. Meaning it does not use mercury. Instead, it uses the fact that as temperature increases, the voltage across a diode increases at a known rate. By precisely amplifying the voltage change, it is easy to generate an analog signal that is directly proportional to temperature. (4) Microprocessor: Arduino Mega 2560, Open Source. (5) Microprocessor Blue Tooth Shield: Bluetooth Low Energy (BLE) Shield from redbear.com. Added to the Arduino for low-energy bluetooth communications with the iPhone. The assembled headset is shown in Fig. 3 , where the three sensors are mounted on the tips of three legs in the forehead, the microprocessor and the
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15microprocessor bluetooth shield are mounted on the back, and the ear lobe is used as the base of the EEG sensor. Fig. 3 Prototype multi-functional headset In order to test and validate that the headset is working properly and that all the sensors are functioning, a test environment had to be constructed. To simulate a real-world environment, a mobile Smart Phone application was developed on the Apple iPhone platform. This platform was chosen for ease of access to development tools and availability of software development kits (SDK) from all the hardware and chipset vendors. Both NeuroSky and Red Bear Labs included sample applications that were then easily transferred to a custom application using a simple view to display all the sensor values. To show that the headset sensors are working a custom mobile application was developed to view the results. Sample screenshots of the application with the actual results are displayed in Fig. 4 . The left snapshot shows the signal strengths of the EEG sensor (the lower number) and the Arduino sensors (the upper number). Readings of the three sensors are the EEG signals are filtered into signals of different frequency intervals. Fig. 4 iOS sensor headset application Example Two: Analysis of Meditation State The brain emits electrical signals that are caused by neurons firing in the brain. The patterns and frequencies of these electrical signals can be measured by placing a sensor on the scalp. For example, the EEG sensor by NeuroSky is able to measure the analog electrical signals commonly referred to as brainwaves and process them into digital signals to make the measurements available for further analysis. Table 1 lists the most commonly
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17recognized frequencies that are generated by different types of brain activity. Table 1 Brainwave frequencies Brainwave type Frequency range (Hz) Mental states and conditions Delta 0.1–3 Deep, dreamless sleep, non-REM sleep, unconscious Theta 4–7 Intuitive, creative, recall, fantasy, imaginary, dream Alpha 8–12 Relaxed, but not drowsy, tranquil, conscious Low Beta 12–15 Formerly SMR, relaxed yet focused, integrated Midrange Beta 16–20 Thinking, aware of self and surroundings High Beta 21–30 Alertness, agitation Emotions play an essential role in many aspects of our daily lives, including decision-making, perception, learning, rational thinking and actions. To detect the emotion of a person, the first approach is based on text, speech, facial expression, and gesture. This approach, needless to say, is not reliable to detect emotion, especially when people want to conceal their feelings. Some emotions can occur without corresponding facial emotional expressions, emotional voice changes, or body movements. On the contrary, such displays could be faked easily. Using multi-modality approach can overcome this shortcoming to limited extent. The new approach is through affective computing, which employs EEG signals recorded when users perform some brain activities and apply analytical algorithms to EEG data to detect the emotion. This approach is based on the fact that brain activities have direct information about emotion and EEG signals can be measured at any moment and are not dependent on expression. Different recognition techniques can be used in different situations to maximize recognition rates. An Empirical Study We measured an experienced meditator’s brainwaves while meditating and compared them to several other states including idle and talking. We found prominent differences between the experienced meditator’s brainwaves and those of other states. The experienced meditator’s brainwaves clearly displayed a stable state most of the time, as shown in Fig. 5 a. However, during certain times after the initial meditation stage, extraordinary high waves were observed, as shown in Fig. 5 b. Fig. 5 An experienced meditator’s brain waves
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19Figure 6 shows the brainwaves of idle, talking, and meditating from an inexperienced meditator. We can clearly see that the irregularities of these states are higher than the experienced meditator’s state, especially the idle and the talking states. The inexperienced meditator showed some similarity to the state shown in Fig. 5 a but it did not show the features in Fig. 5 b. This initial study indicates that trained meditators can demonstrate regularity during meditation practice. Fig. 6 Brain waves of other states A Platform for Cumulative Brain State Modeling We are investigating an automatic EEG-based emotion recognition system that can record the EEG signals from users and measure their emotions. The EEG data are filtered to get separate frequency bands to train emotion classifiers with the four well-known classification techniques that are SVMs, Naïve Bayes, k NN and AdaBoost.M1. Figure 7 shows the typical flowchart of and Table 3 shows the band-wise recognition rate of the AdaBoost.M1 algorithm. Table 2 Brain state recognition rates SVM (%) k NN (%) Naïve Bayes (%) AdaBoost.M1 (%) Emotion recognition rate 89.25 83.35 66 92.8 Table 3 Recognition rates of AdaBoost.M1 Delta (%) Theta (%) Alpha (%) Beta (%) All (%) Emotion recognition rate 69.95 68.4 75.5 89.7 92.8
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21As depicted above, using EEG data, cognitive psychologists can visualize and observe correlations between different active brain states. It is desirable to create an application that takes EEG data and exposes it to various analytical techniques so the resultant brain states can be studied and predicted. We present the design and implementation of a system that integrate onsite EEG data collection, analysis, web-based EEG data storage and modeling tools, and user feedback through mobile communication devices. Architecture of the system is shown in Fig. 8 . Fig. 8 EEG data analysis system architecture The web server provides a user interface that allows users to view EEG data in the database and run R program to perform data analysis. Figure 9 shows that data are rendered in wave form mode and statistical mode, respectively. Figure 10 shows that R is invoked to perform data analysis tasks in interactive mode. interface
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23Fig. 10 Running R to Analyze EEG Data iPhone users can also use the web interface to connect to the database to view data. Figure 11 shows the two functions, viz., “collect data” and “view data”, that a user can choose on the iPhone app (Fig. 11 a). The user can display data in text mode by viewing individual data frames (Fig. 11 b), or research with projected applications in the areas of health care. We hope this information provides the reader a bird’s eye view of the potentials of EEG data analysis. In addition, we described a method of quick prototyping an EEG headset, in a cost-effective way and with state-of-the-art technologies. This headset is a good starting foundation for anybody interested in researching body data via sensors. The Arduino components can be extended and exchanged to any desired configuration. It is only up to the imagination of the builder to decide what is possible and where to take the project next. We used meditation research to reach out to the high-end applications of
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25EEG data analysis in understanding human brain states and assisting in promoting human health care. Some devotees to the practices of transcendental meditation have shown the ability to control these brain states. We want to numerically prove or disprove this assumption; the analysis of these states could be the initial step in a process to first predict and later allow individuals to control these states. References 1. W.J. Freeman, A neurobiological interpretation of semiotics: meaning, representation, and information. Inf. Sci. 124 (2000), 93–102 (2000) Crossref 2. S.-M. Zhou, J.Q. Gan, F. Sepulveda, Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface. Inf. Sci. 178 (6), 1629–1640 (2008) Crossref 3. O. Dressler, G. Schneider, G. Stockmanns, E.F. Kochs, Awareness and the EEG power spectrum: analysis of frequencies. Br. J. Anaesth. 93 (6), 806–809 (2004) Crossref 4. Y.-P. Lin, C.-H. Wang, T.-P. Jung, T.-L. Wu, S.-K. Jeng, J.-R. Duann, J.-H. Chen, EEG-based emotion recognition in music listening. IEEE Trans. Biomed. Eng. 57 (7), 1798–1806 (2010) Crossref 5. R. Davidson, J. Kabat-Zinn, J. Schumacher et al., Alterations in brain and immune function Science 2.0 and Expansion of Science (S2ES 2010), the 14th World Multiconference on Systemics, Cybernetics and Informatics (WMSCI 2010) , Orlando, Florida, June 29–July 2, 2010, pp. 56–61 8. B. Oh, P. Butow, B. Mullan et al., Impact of medical Qigong on quality of life, fatigue, mood and inflammation in cancer patients: a randomized controlled trial. Ann. Oncol. 3 , 608–614 (2009) 9. J.J. Loizzo, J.C. Peterson, M.E. Charlson, E.J. Wolf, M. Altemus, W.M. Briggs, L.T. Vahdat, T.A. Caputo, The effect of a contemplative self-healing program on quality of life in women with breast and gynecologic cancers. Altern. Ther. Health Med. 16 (3), 30–37 (2010) 10. T.M. Habermann, C.A. Thompson, B.R. LaPlant, B.A. Bauer, C.A. Janney, M.M. Clark, T.A. Rummans, M.J. Maurer, J.A. Sloan, S.M. Geyer, J.R. Cerhan, Complementary and alternative medicine use among long-term lymphoma survivors: a pilot study. Am. J. Hematol. 84 (12), 795–798 (2009) Crossref 11. C.A. Lengacher, V. Johnson-Mallard, J. Post-White, M.S. Moscoso, P.B. Jacobsen, T.W. Klein, R.H. Widen, S.G. Fitzgerald, M.M. Shelton, M. Barta, M. Goodman, C.E. Cox, K.E. Kip, Randomized controlled trial of mindfulness-based stress reduction (MBSR) for survivors of breast cancer. Psychology 18 (12), 1261–1272 (2009)
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2712. B. Oh, P. Butow, B. Mullan, S. Clarke, Medical Qigong for cancer patients: pilot study of impact on quality of life, side effects of treatment and inflammation. Am. J. Chin. Med. 36 (3), 459–472 (2008) Crossref 13. K.A. Biegler, M.A. Chaoul, L. Cohen, Cancer, cognitive impairment, and meditation. Acta Oncol. 48 (1), 18–26 (2009) Crossref 14. H. Lin, Measurable Meditation, in Proceedings of the International Symposium on Science 2.0 and Expansion of Science (S2ES 2010) , Orlando, Florida, USA, pp. 56–61 (2010)