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Brain–Computer Interface Technology and Development By Narisa N.Y. Chu The emergence of imprecise brainwave headsets in the commercial world. Digital Object Identifier 10.1109/MCE.2015.2421551 Date of publication: 15 July 2015 july 2015 ^ IEEE Consumer Electronics Magazine 35 Image licensed by INGram publishing to nine sensors have entered the market, with some examples demonstrated in Figures 1 and 2, where sensor placements have also been indicated for various settings based on standard P300 EEG detection locations on the scalp. A road map that highlights the BCI technology development is shown in Figure 3, contrasting with the original research focus on brain signal processing algorithms with the advent of many products’ introduction of headsets. Due to the small signal induced by neurons, event-revoked potentials (ERPs) of the brain activities, measured from the outside of the scalp via sensors, require intricate interpretation. More than 50 digital processing algorithms have been developed since 1981 to dwarf the noise, overcome attenuation, and discriminate from physiological interferences due to tissue thickness and wetness; chemical change; and stimulus of visual, audio, and muscle movements from eye to toe. The major schools in developing these algorithms are represented by the five blue arrows in Figure 3. The reconstructed brain features reflect many assumptions, sometimes taking into consideration a priori knowledge, however, leaving plenty of room to guess about the real intention of the brain. These socalled “inverse problem solutions,” similar to “reverse engineering” effort, demonstrated accuracy between 60% to 90% in various controlled environments. The performance so far 36 IEEE Consumer Electronics Magazine ^ july 2015 has significantly limited BCI usage in real life. Regardless of the range of uncertainty, innovative applications have been trialed recently, and BCI headsets (a sample product is shown at the bottom center in Figure 3, with no commercial endorsement) designed for mass consumption have made inroads for rehabilitation and learning fun. This accelerated product introduction has been quite strategic in building around an imprecise BCI measurement while creating special effects for entertainment such as in games and videos. However, before more headsets and applications become popular, it is helpful to establish a standardized brainwave databank for identifying and sharing brain signals from many denominations with the aim to facilitate an intelligent search down the road. This standardization effort would represent a broader collaboration between software and neuroscience leading to treatments for brain-related sickness, rehabilitation, and wellness, and probably furthering brain-triggered marketing and privacy protection. The new approach to standardization is illustrated in the gold stream in Figure 3. The conventional digital signal processing algorithms based on statistics, complex mathematical transformation, and estimation tend to be agnostic, reaching a performance limit. Entrepreneurs have not been shy with this technology Nz Fp1 Fpz Fp2 AF7 F9 FT9 A1 TP9 P9 TP7 P7 PO7 PO3 PO4 PO8 O2 OZ l Z O1 POZ CP5 P5 P3 P1 P2 P4 P6 P8 P10 PZ CP3 CP1 CPZ CP2 CP4 CP6 TP8 TP10 T9 T7 C5 C1 C2 C6 FT7 FC5 FC3 FC1 FCZ FC2 FC4 FC6 FC8 FT10 T8 T10 A2 F7 F5 F3 F1 FZ F2 F4 F6 F8 F10 AF3 AFZ AF4 AF8 C3 CZ C4 MUSE (Four Sensors) Staalhemel (Eight Sensors) • Relaxation • Focus Motor Imagery (MI): C3–Right-Hand MI CZ–Foot MI C4–Left-Hand MI Examples: EPOC (4 + 9 = 14 Sensors): AF3, AF4, F3, F4, FC5, FC6, F7, F8 T7, T8, P7, P8, O1, O2 – • Instantaneous Excitement • Long-Term Excitement • Frustration • Engagement INSIGHT (Five Sensors): AF3, AF4, T7, T8, Pz – • Instantaneous Excitement • Long-Term Excitement • Stress • Meditation • Engagement • Relaxation • Interest • Focus TP9, TP10, Fp1, Fp2 Fp1, Fp2, F7, F8, C3, C4, O1, O2 eMotiv EPOC (2014) INSIGHT (2015) Interaxon NeuroSky Muse (2014) MindWave Mobile (2012) FIGURE 1. BCI sensor placement to detect EEG based on the international P300 standard. FIGURE 2. BCI products entering the market. july 2015 ^ IEEE Consumer Electronics Magazine 37 imprecision and have developed products with promising benefits to eHealth, testing out the brain signals of the young and the sick. Although growth has been proliferating where selective parameters were chosen to represent brainwaves within private groups, it is extremely difficult for reliable pattern matching and self-learning algorithms to be incorporated universally. With standardization, brainwave data from all sources and reactions can be intelligently accumulated and used. This new approach is facilitated by the understanding of the threshold (TH) between the brain’s actual and interpreted meaning, as denoted in Figure 1. Applications can be triggered by this threshold and go on to another proven activity such as games. The databank should include not only the brainwave diagram but the processing and search algorithms associated with the brainwave, plus all prior knowledge. This databank will be well positioned to blend fuzzy logic inference and pattern recognition related to big data evolution. The sections that follow will illustrate the paths taken in achieving the level of interpretability of brain signals to date. The emergence of imprecise brainwave headsets in the commercial world is illustrated. The current tools for research and future development are discussed, with a recommendation to standardize the brain-signal databank, anticipating its reach to big data and, perhaps, cloud computing. BRAIN SIGNAL FREQUENCY BANDS Brain waves can be represented by six typical bands based on the frequency range between 1 and 100 Hz, designated as the a b, ,T, , c n, and i bands. These band frequency ranges are shown with a collective interpretation as follows [3]–[5]. ▼ Frequency 1–4 Hz: T band, symbolizing high emotional conditions or in a sleep stage. ▼ Frequency 4–7 Hz: i band, similar to the T band, also symbolizing a calm and relaxed mood. ▼ Frequency 8–12 Hz: a band, symbolizing smooth patterns: awaken, calm, and eyes closed in a relaxed mood. ▼ Frequency 8–13 Hz: n band, desires from the sensorimotor cortex. ▼ Frequency 12–30 Hz: b band, for desynchronized—normal awaken, open eyes, busy, churning, and concentrating. ▼ Frequency 25–100 Hz: c band, desires from somatosensory cortex for touch—busy, churning, and concentrating. At these bands, typically a very low signal is collected by the noninvasive sensor (in the range of 5–10 nV), while interfering noise of 10–20 times stronger than the brain signal is measured on the scalp. The correlation of these band signals with respect Common Spatial Patterns Blind Source Separation Band Power Decomposition Assumptions and Prior Knowledge Stimulants: Eye, Audio, Motion, Etc. Brain-Wave Databank Architecture and Intelligent Search Conventional Approach A New Approach Threshhold Standardized User Brain Data Input and Retrieval Sensor Interpreted Headset ≠ FIGURE 3. A BCI technology road map—two approaches. BCI headsets, injecting new break points in games and entertainment, deliver desirable special effects that can blend in our pursuit of wellness and rehabilitation. 38 IEEE Consumer Electronics Magazine ^ july 2015 to a person’s brain condition varies from group to group. In general, the interpretation states whether a person is in attention or mediation. Exactly what attention and mediation means is left for one to judge. Various digital processing algorithms were attempted for the purpose of making the brain signal interpretable via an increase of the signal-to-noise ratio, manipulation of sensor spatial and time domain parameters (TDPs), and fitting of a priori knowledge and various assumptions to yield some performance improvement. These digital processing algorithms are profiled as follows. DIGITAL BRAIN SIGNAL PROCESSING ALGORITHMS Major research efforts have been spent developing digital processing algorithms to identify brain signals at various frequency bands. A plethora of inverse problem so
lving and pattern-matching analyses have been applied to signals measured from many sensors placed on a cap noninvasively covering a person’s head. Stable reconstructions of brain-signal features could be achieved through the use of many techniques. Since the measurements and modeling techniques both contain noise and assumptions, the true solution could hardly be totally derived from the algorithms or totally determined from the measurements. A comprehensive strategy for dealing with noisy data could also include data filtering and an optimal selection of geometric parameters, such as sensor positioning. One particularly notable technique suggested was regularization [13], which attempted to achieve a compromise between a close fit to the data and stability of the algorithmic solution. By removing the high-frequency component from a derived solution, it believed that it effectively filtered a portion of the noise [13]. Various algorithms have demonstrated performances up to 90% accuracy in a controlled environment. They could be highly computationally intensive. They were primarily carried out offline in batch processing, with unproven real-time applications. They also required stringent calibration and testing to facilitate performance dedicated to an individual. To appreciate the level of effort spent in the development of these brain signal processing algorithms, three major categories are elaborated: 1) band-power feature extraction, 2) common spatial patterns (CSP) analysis, and 3) statistical source separation [6]. The year cited, spanning from 1981 to 2014, refers to approximately the first introduction of the algorithm. Band Power Feature Extraction 1) band-pass filtering and power estimation taking temporal average 2) periodogram (Fourier decomposition) 3) power spectral density from autoregressive coefficients 4) wavelet scalogram (time-scale representation) 5) spectrogram (time-frequency decomposition with averaged spectrums over time) [2008]. CSP Analysis 1) spatial filtering (SF) i) bipolar ii) Laplacian 2) physical forward modeling: inverse solution methods i) Minimum current estimate [2008, 2009] – Focal underdetermined system solver [1995] ii) Weighted minimum norm estimate – LORETA, eLORETA, and sLORETA—all assuming smoothness [1987, 1994] iii) Mixed norm estimate, combining sparsity and smoothness [2008] – S-FLEX Champagne—simple spatial structure [2011] iv) minimum entropy v) source localization paradigms – dipole modeling [1992] – multipole modeling – scanning a) subspace methods (MUSIC/RAP-MUSIC) [1986, 1999] b) beamformers [1997] i) LCMV beamformer ii) nulling beamformer vi) depth compensation modeling [1987] c) CSP [1990] i) supervised regulated spatial filters based on EEG and CSP ii) filter bank CSP iii) discriminatory filter (DFCSP) iv) sparse CSP v) source power correlation analysis (SPoC) [2014] vi) canonical spatial power comodulation [2014] vii) steady-state auditory evoked potentials [1981] viii) steady-state visual evoked potentials ix) spatiospectral decomposition. Statistical Backward Modeling: Blind Source Separation 1) linear classifiers—linear discriminant analysis (LDA), support vector machine (SVM), and infinite impulse response [2010] 2) linear regression—OLS, ridge regression, LASSO [2005] 3) principal component analysis (PCA) [2005] A comprehensive strategy for dealing with noisy data could also include data filtering and an optimal selection of geometric parameters, such as sensor positioning. july 2015 ^ IEEE Consumer Electronics Magazine 39 4) canonical correlation analysis (CCA)—hyperscanning ERP studies [2011] 5) independent component analysis (ICA) 6) Granger-causally interacting—SCSA, MVARICA— brain connectivity studies [2008] 7) dimensionality reduction—stationary subspace analysis (SSA) [2009]. Performance data ranging from 60.7%—with rudimentary band-pass filtering—to 92.8%, with DFCSP, have been demonstrated in carefully calibrated and tested setup. It should be noted that every method performs well if its specific assumptions are met. Unfortunately, no method can perform well in all real cases. It is anticipated that multiple methods may be combined to lead to better solutions. It is also possible that multiple methods exist for the same solution. With a common goal to characterize brain activity of interest, there is still no assurance that one method is better than others in all circumstances. Assumptions play a major role in the derivative and convergence of these methods: 1) Assumptions often made with inverse analysis and blind source separation: Brain activity is assumed to be: – correlated with behavior or stimulus variables [OLS, ridge regression, LASSO] – reflected in the strongest components of EEG [PCA] – correlated across subjects/stimulus repetitions [CCA] – stationary, major signaling stays local, unaffected by neurons away from the measuring point [SSA] – different from experimental conditions [LDA, SVM]. Brain components are assumed to be: – mutually independent [ICA] – Granger-causally interacting [SCSA, MVARICA]. 2) Not all EEG phenomena are phase-locked to certain events. There are rhythms depending on the mental state. Most rhythms are idle, attenuated during activation (e.g., eyes open/close, arm at rest/moves). 3) Sensor-spatial analysis assumes smoothness and sparsity where neighboring voxels (discrete volume elements) show similar activity, and only a small part of the brain is active for a single task. A limited evaluation has provided some insight as to how to improve the BCI performance. The performance of the digital brain signal algorithmic processing has reached nearly 93% under a controlled environment. For example, the basic band-power approach demonstrated 60.7% average accuracy in a BCI competition [6]. Applying the Laplacian SF technique, the average accuracy reached 68%. Using the bipolar SF technique, the average accuracy was improved to 70.5%, slightly better than Laplacian. Combining supervised regulated spatial filters on EEG with CSP and TDP, the average accuracy was demonstrated between 78.7 and 88.9%, a range too large to lend enough confidence. Another combination of TDP, SF, and CSP provided 80.1% accuracy. Using filter bank CSP, the average accuracy achieved was between 81.1 and 90.9%. The best accuracy of 92.8% was accomplished by adopting DFCSP [6]. Thus, the performance has not demonstrated enough robustness for all known algorithms, to say the least. One reaches a diminishing return if one continues searching for better algorithmic processing of brain activities. Training and testing input have been employed to augment these algorithms. Recently, another notable approach by means of virtual reality and gaming has opened up new frontiers for BCI, in lieu of the imprecise algorithms. BCI TOOLS FOR DEVELOPMENT ACCELERATION Two major research tools have been developed extensively: BCI2000 [15] from the Wadsworth Center in New York and BCILAB [8] from the University of California, San Diego, the Swartz Center of Computational Neuroscience (SCCN). These pioneering platforms of the BCI were initiated for the disabled to operate wheelchairs/computers. Later, they were extended to support more efforts for rehabilitation, education, and entertainment purposes. Recently, commercial tools offered by Neurosky [5], Interaxon [4], and eMotiv [3], bundled with products, have also been made available with various degrees of open utility and readiness. Most of the commercial software development kits (SDKs) present various degrees of stability and maturity. There is enough momentum from the development community to trial and inject further “kickstarter” initiatives. Benefiting from more than two decades of neuroscience research and development, these tools have become widely available within the last several years. Table 1 summarizes the status of these tools. It is clear that both a vigorous research tool and a product realization kit can be chosen for development. It should also be noted that the number of sensors gathering brain signals has purposefully decreased significantly in commercial produ
ct realization. Some of the earlier algorithms of broader spatial coverage might lose their effectiveness as sensor placements are minimized for user comfort. With all due diligence exploring the intricate brain, it becomes evident that collective efforts among engineering, medical, and user disciplines have to come together for optimal use of the brain signal data. Thus, forming a standardized databank should be an obvious next step. The naming preference, “databank” instead of “data base,” is to acknowledge the amount and the dynamics of the data (big data) associated with various algorithms, training, testing, and feedback that can eventually realize brain activities. It Collective efforts among engineering, medical, and user disciplines have to come together for optimal use of the brain signal data. 40 IEEE Consumer Electronics Magazine ^ july 2015 is necessary to recognize user brain reactions eventually across the board: gender, age, environment, stimulus, intent, processing algorithm, intelligent search, and pattern association, not to omit involvement from big data, cloud computing, and sensor networking. This databank, built upon a standard brain signal profile format plus intelligent linking factors can be destined to make the retrieval and triggering function much more timely and meaningful. Once standardized, new applications and benefits can be then accelerated beyond imagination. Attributes that Rehabilitation Computer and Wheelchair Operation Attention Oblivion Defocused Neglect Random Hyped Marketing Applications Like Dislike Maybe Trial Committed Etc. Signal Characteristics + Algorithmic Processing Feature Vector Gamma 25~100 Hz Beta 12~30 Hz Alpha 8~12 Hz Mu 8~13 Hz Theta 4~7 Hz Delta 1~4 Hz Empirical Composite Learning Factor from Training and Testing Band Power Spacial Filter Temporal Average Periodogram Fourier Decomposition Power Spectral Density + AR Coefficients Wavelet Scalogram Time-Scale Representation Spectrogram TimeFrequency Decomposition Analysis Type ERP: Inverse Solution and CSP BSS, Statistic Modeling CCA ICA LDA PCA SPoC SSA Entertainment Directives Happy Sad Excited Scary Disgusted Don’t Care Etc. Database Classifier Gender Age Eye Blinking Audio Effect Facial Muscle Movement: Chewing, Clenching Teeth, Grinding Jaw, Etc. Hand/Foot Imagery Motion Chemical, Light, Color, Etc. Stimulant FIGURE 4. Attributes in a brain signal databank. Table 1. The BCI SDK/platform [3]–[5], [7], [14]–[16]. Company/ University Product/Platform Year Sensor/ Channel Tools/Platforms Apps NeuroSky MindWave Mobile 2013 1 MWM SDK Rehab for ADD, stroke; education, entertainment Interaxon MUSE 2014 7/4 Basic SDK for connection Entertainment device control eMotiv EPOC 2014 9/14 SDK Lite Entertainment neurotherapy Insight 2015 9/5 SCCN/ UCSD BCILAB/ LSL 2012 Many Open/ MATLAB Focus: Comparative evaluation of BCI methods Wadsworth Center, New York BCI 2000 2010 Many Open Rehab + general purpose Various Developers Pyff in Python 2010 – Open and free Standardization of feedback and stimulus july 2015 ^ IEEE Consumer Electronics Magazine 41 require consideration in such a standardization effort are introduced in Figure 4 as a starting point. These attributes are not meant to be exclusive. CHALLENGES IN TIME One would wonder about the time frame for the BCI to become fully developed from the first patent/prototype to mass production for general consumption. Some historical timelines of similar technology can be referenced. Considering the following track records: ▼ Wired telephone: patent established in 1876 to mass production in 1970s ▼ Mobile phone: 1946–1994 (iPhone was introduced in 2007) ▼ Brain-wired cap: from 1981 onward ▼ Brain headset: since 2012, limited products have been introduced. One can thus anticipate the acceleration point on BCI production in the next two decades or sooner. A few crucial questions are still waiting to be tackled by the research community [6], [11], [12]. ▼ What are the fundamental accuracy limits imposed by the current EEG sensors? ▼ What assumptions are widely agreeable, and what empirical data are required to improve the accuracy of the available mathematical models? ▼ How can hierarchical models be constructed to include data from multiple people, environments, and applications? ▼ As the brain-signal performance improves, how will sensor convergence be handled? ▼ How are auxiliary data included (e.g., muscle movement, eye contact, and chemical change)? ▼ How can designing methods directly target real-world applications with robustness? ▼ Will there be standardization of the brain databank? ▼ What are the privacy issues? SUMMARY Major thrusts combining neuroscience, sensor chip design, and software development have already shown remarkable advancement, regardless of the many uncertainties and challenges. Entrepreneurs have started to capture the low-hanging fruit from the BCI technology evolutionary “branches.” The BCI headsets, injecting new break points in games and entertainment, deliver desirable special effects that can blend in our pursuit of wellness and rehabilitation. To foster these promises, brainwave databank standardization can play a major role in converging the collection and utilization of users’ essential, private brain information, following the example of DNA and fingerprints. ABOUT THE AUTHOR Narisa N.Y. Chu ( is the cofounder of CWLab International and is currently focusing on BCI research. She is also a member of the IEEE Consumer Electronics Society Board of Directors. It is with the latter role that she contributed to the writing of this article. REFERENCES [1] P. Herman, G. Prasad, T. M. McGinnity, and D. Coyle, “Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification,” IEEE Trans. Neural Syst. Rehab. Eng., vol. 16, no. 4, pp. 317–326, Aug. 2008. [2] N. Brodu, F. Lotte, and A. Lécuyer, “Exploring two novel features for EEG-based brain-computer interfaces: Multifractal cumulants and predictive complexity,” Neurocomputing, vol. 79, no. 1, pp. 87–94, 2012. [3] eMotiv. (2015). EPOC & INSIGHT. [Online]. Available: http:// [4] Interaxon. (2015). MUSE. [Online]. Available: com/a/ [5] NeuroSky. NeuroSky MindWave Mobile [Online]. Available: http:// [6] S. Haufe. (2014). Berlin brain-computer interface, Winter School Lecture. [Online]. Available: bbci2014_haufe_statistical_models_01. pdf [7] C. Kothe. (2012). Lectures, SCCN/UCSD. [Online]. Available: http://sccn. [8] C. Kothe. (2013). BCILAB, SCCN/UCSD. [Online]. Available: ftp:// [9] S. Haufe, V. V. Nikulin, A. Ziehe, K.-R. Muller, and G. Nolte, “Combining sparsity and rotational invariance in EEG/MEG source reconstruction,” NeuroImage, vol. 42, no. 2, pp. 726–738, June 2008. [10] S. Haufe, V. V. Nikulin, A. Ziehe, K.-R. Muller, and G. Nolte, “Estimating vector fields using sparse basis field expansions,” in Advances in Neural Information Processing Systems, vol. 21, Cambridge, MA: MIT Press, 2008, pp. 617–624. [11] Y. Zhang and Y. Ding, “CTL model update for system modifications,” J. Artificial Intell. Res., vol. 31, pp. 113–155, 2008. [12] K.-R. Muller, S. Fazli, P. von Bunau, F. Meinecke, W. Samek, G. Curio, and B. Blankertz. (2014). BCI and nonstationarity. BBCI Winter School, Berlin. [Online]. Available: bbci2014_muller_non_stationarity_01.pdf [13] F. Lotte. (2014). Oscillatory EEG-based BCI design: Signal processing and more. InriaBordeaux Sud-Ouest, France, Potiocteam. [Online]. Available: bbci2014_lotte_signal_processomg_01.pdf [14] I. F. Gorodnitsky, J. S. George, and B. D. Rao, “Neuromagnetic source imaging with FOCUSS: A recursive weighted minimum norm algorithm,” Electroencephalogr. Clin. Neurophysiol., vol. 95, no. 4, pp. 231–251, Oct. 1995. [15] C. Brunner, G. Andreoni, L. Bianchi, B. Blankertz, C. Breitwieser, S. Kanoh,
C. A. Kothe, A. Lecuyer, S. Makeig, J. Mellinger, P. Perego, Y. Renard, G. Schalk, I. P. Susila, B. Venthur, and G. R. Muller-Putz. (2012). BCI software platforms. [Online]. Available: http://people. [16] T. M. Vaughan, D. J. McFarland, G. Schalk, W. A. Sarnacki, D. J. Krusienski, E. W. Sellers, and J. R. Wolpaw, “The Wadsworth BCI research and development program: At home with BCI,” IEEE Trans. Neural Syst. Rehab. Eng., vol. 14, no. 2, pp. 229–233, June 2006. [17] B. Venthur, S. Scholler, J. Williamson, S. Dahne, M. S. Treder, M. T. Kramarek, K. R. Muller, and B. Blankertz, “Pyff—A pythonic framework for feedback applications and stimulus presentation in neuroscience,” Front. Neurosci., vol. 4:179, Dec. 2010. [18] EEG Resources. (2014, Jan. 5). Biomed research, Middle East Medical Information Center and Directory. Epilepsy Awareness Program. [Online]. Available: researches/epilepsy/eeg_resources.html

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