Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter September 28, 2013

Brain-computer interface technologies: from signal to action

  • Alexis Ortiz-Rosario

    Alexis Ortiz-Rosario was born in San Juan, Puerto Rico. He received his B.S. in Industrial Engineering from the University of Puerto Rico (UPR) at Mayagüez, Puerto Rico, in 2011. He is currently a PhD student in Biomedical Engineering and a Graduate Research Associate at the Motor Systems and Neurophysiology Lab at The Ohio State University. His current research interests include neural networks, neural signal processing, brain-computer interfaces, motor systems, and electrophysiology.

    and Hojjat Adeli

    Hojjat Adeli is Professor of Civil, Environmental, and Geodetic Engineering, Biomedical Engineering, Biomedical Informatics, Electrical and Computer Engineering, Neurological Surgery, and Neuroscience at The Ohio State University. He has authored/co-authored 15 books. He is the author of Automated EEG-based Diagnosis of Neurological Disorders – Inventing the Future of Neurology (CRC Press, 2010). In 1998 he received The Ohio State University’s highest research honor, the Distinguished Scholar Award ‘in recognition of extraordinary accomplishment in research and scholarship’. He is a Fellow of AAAS and IEEE. He is the Editor-in-Chief of the international research journals Computer-Aided Civil and Infrastructure Engineering which he founded in 1986 and Integrated Computer-Aided Engineering which he founded in 1993. He is also the Editorin-Chief of the International Journal of Neural Systems.

    EMAIL logo

Abstract

Here, we present a state-of-the-art review of the research performed on the brain-computer interface (BCI) technologies with a focus on signal processing approaches. BCI can be divided into three main components: signal acquisition, signal processing, and effector device. The signal acquisition component is generally divided into two categories: noninvasive and invasive. For noninvasive, this review focuses on electroencephalogram. For the invasive, the review includes electrocorticography, local field potentials, multiple-unit activity, and single-unit action potentials. Signal processing techniques reviewed are divided into time-frequency methods such as Fourier transform, autoregressive models, wavelets, and Kalman filter and spatiotemporal techniques such as Laplacian filter and common spatial patterns. Additionally, various signal feature classification algorithms are discussed such as linear discriminant analysis, support vector machines, artificial neural networks, and Bayesian classifiers. The article ends with a discussion of challenges facing BCI and concluding remarks on the future of the technology.


Corresponding author: Hojjat Adeli, Departments of Biomedical Engineering, Biomedical Informatics, Civil and Environmental Engineering and Geodetic Science, Electrical and Computer Engineering, Neurological Surgery, and Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, USA, e-mail:

About the authors

Alexis Ortiz-Rosario

Alexis Ortiz-Rosario was born in San Juan, Puerto Rico. He received his B.S. in Industrial Engineering from the University of Puerto Rico (UPR) at Mayagüez, Puerto Rico, in 2011. He is currently a PhD student in Biomedical Engineering and a Graduate Research Associate at the Motor Systems and Neurophysiology Lab at The Ohio State University. His current research interests include neural networks, neural signal processing, brain-computer interfaces, motor systems, and electrophysiology.

Hojjat Adeli

Hojjat Adeli is Professor of Civil, Environmental, and Geodetic Engineering, Biomedical Engineering, Biomedical Informatics, Electrical and Computer Engineering, Neurological Surgery, and Neuroscience at The Ohio State University. He has authored/co-authored 15 books. He is the author of Automated EEG-based Diagnosis of Neurological Disorders – Inventing the Future of Neurology (CRC Press, 2010). In 1998 he received The Ohio State University’s highest research honor, the Distinguished Scholar Award ‘in recognition of extraordinary accomplishment in research and scholarship’. He is a Fellow of AAAS and IEEE. He is the Editor-in-Chief of the international research journals Computer-Aided Civil and Infrastructure Engineering which he founded in 1986 and Integrated Computer-Aided Engineering which he founded in 1993. He is also the Editorin-Chief of the International Journal of Neural Systems.

References

Acharya, U.R., Sree, S.V., and Suri, J.S. (2011). Automatic detection of epileptic EEG signals using higher order cumulant features. Int. J. Neural Syst. 5, 403–414.10.1142/S0129065711002912Search in Google Scholar

Acharya, U.R., Sree, S.V., Alvin, A.P., Yanti, R., and Suri, J.S. (2012). Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int. J. Neural Syst. 22, 1250002.10.1142/S0129065712500025Search in Google Scholar

Adeli, H. and Ghosh-Dastidar, S. (2010). Automated EEG-Based Diagnosis of Neurological Disorders – Inventing the Future of Neurology. (Boca Raton, FL: CRC Press/Taylor & Francis).10.1201/9781439815328Search in Google Scholar

Adeli, H., Zhou, Z., and Dadmehr, N. (2003). Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Methods 123, 69–87.10.1016/S0165-0270(02)00340-0Search in Google Scholar

Adeli, H., Ghosh-Dastidar, S., and Dadmehr, N. (2005). Alzheimer’s disease: models of computation and analysis of EEGs. Clin. EEG Neurosci. 36, 131–140.10.1177/155005940503600303Search in Google Scholar PubMed

Adeli, H., Ghosh-Dastidar, S., and Dadmehr, N. (2007). A wavelet-chaos methodology for analysis of EEGs and EEG sub-bands to detect seizure and epilepsy. IEEE Trans. Biomed. Eng. 54, 205–211.10.1109/TBME.2006.886855Search in Google Scholar PubMed

Adeli, H., Ghosh-Dastidar, S., and Dadmehr, N. (2008). A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer’s disease. Neurosci. Lett. 444, 190–194.10.1016/j.neulet.2008.08.008Search in Google Scholar PubMed

Ahmadlou, M. and Adeli, H. (2010a). Enhanced probabilistic neural network with local decision circles: a robust classifier. Integr. Comput. Aided Eng. 17, 197–210.10.3233/ICA-2010-0345Search in Google Scholar

Ahmadlou, M. and Adeli, H. (2010b). Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD. Clin. EEG Neurosci. 41, 1–10.10.1177/155005941004100103Search in Google Scholar PubMed

Ahmadlou, M. and Adeli, H. (2011). Fuzzy synchronization likelihood with application to attention-deficit/hyperactivity disorder. Clin. EEG Neurosci. 42, 6–13.10.1177/155005941104200105Search in Google Scholar PubMed

Ahmadlou, M., Adeli, H., and Adeli, A. (2010a). New diagnostic EEG markers of the Alzheimer’s disease using visibility graph. J. Neural Transm. 117, 1099–1109.10.1007/s00702-010-0450-3Search in Google Scholar PubMed

Ahmadlou, M., Adeli, H., and Adeli, A. (2010b). Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder. J. Clin. Neurophysiol. 27, 328–333.10.1097/WNP.0b013e3181f40dc8Search in Google Scholar PubMed

Ahmadlou, A., Adeli, H., and Adeli, A. (2011). Fractality and a wavelet-chaos methodology for EEG-based diagnosis of Alzheimer’s disease. Alzheimer’s Dis. Assoc. Dis. 25, 85–92.10.1097/WAD.0b013e3181ed1160Search in Google Scholar PubMed

Ahmadlou, M., Adeli, H., and Adeli, A. (2012a). Fractality analysis of frontal brain in major depressive disorder. Int. J. Psychol. 85, 206–211.10.1016/j.ijpsycho.2012.05.001Search in Google Scholar PubMed

Ahmadlou, M., Adeli, H., and Adeli, A. (2012b). Improved visibility graph fractality with application for diagnosis of autism spectrum disorder. Physica A 391, 4720–4726.10.1016/j.physa.2012.04.025Search in Google Scholar

Ahmadlou, M., Adeli, H., and Adeli, A. (2012c). Graph theoretical analysis of organization of functional brain networks in ADHD. Clin. EEG Neurosci. 43, 5–13.10.1177/1550059411428555Search in Google Scholar PubMed

Ahmadlou, M., Adeli, A., Bajo, R., and Adeli, H. (2013). Complexity of functional connectivity networks in mild cognitive impairment patients during a working memory task. Clin. Neurophysiol. In press.Search in Google Scholar

Ahmed, S., Shahjahan, M., and Murase, K. (2011). A Lempel Ziv complexity-based neural network pruning algorithm. Int. J. Neural Syst. 21, 427–441.10.1142/S0129065711002936Search in Google Scholar PubMed

Al-Naser, M. and Soderstrom, U. (2012). Reconstruction of occluded facial images using asymmetrical principal component analysis. Integr. Comput. Aided Eng. 19, 273–283.10.3233/ICA-2012-0406Search in Google Scholar

Andersen, R.A., Musallam, S., Burdick, J., and Cham, J.G. (2005). Cognitive based neural prosthetics. Proc. IEEE 2, 1908–1913.10.1109/ROBOT.2005.1570392Search in Google Scholar

Andersen, R.A., Eun, J.H., and Mulliken, G.H. (2010). Cognitive neural prosthetics. Annu. Rev. Psychol. 61, 169–190.10.1146/annurev.psych.093008.100503Search in Google Scholar PubMed PubMed Central

Andres, D.S., Cerquetti, D., and Merello, M. (2011). Finite dimensional structure of the GPI discharge in patients Parkinson’s disease. Int. J. Neural Syst. 21, 175–186.10.1142/S0129065711002778Search in Google Scholar PubMed

Bansal, A.K., Truccolo, W., Vargas-Irwin, C.E., and Donoghue, J.P. (2012). Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials. J. Neurophysiol. 107, 1337–1355.10.1152/jn.00781.2011Search in Google Scholar PubMed PubMed Central

Besio, W.G., Liu, X., Wang, L., Medvedev, A.V., and Koka, K. (2011). Transcutaneous focal electrical stimulation via concentric ring electrodes reduces synchrony induced by pentylenetetrazole in beta and gamma bands in rats. Int. J. Neural Syst. 21, 139–149.10.1142/S0129065711002729Search in Google Scholar PubMed

Birbaumer, N. (2006a). Brain-computer-interface research: coming of age. Clin. Neurophysiol. 117, 479–482.10.1016/j.clinph.2005.11.002Search in Google Scholar PubMed

Birbaumer, N. (2006b). Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. Psychophysiology 43, 517–532.10.1111/j.1469-8986.2006.00456.xSearch in Google Scholar PubMed

Birbaumer, N. and Cohen, L.G. (2007). Brain-computer interfaces: communication and restoration of movement in paralysis. J. Physiol. 579, 621–636.10.1113/jphysiol.2006.125633Search in Google Scholar PubMed PubMed Central

Blankertz, B., Lemm, S., Treder, M., Haufe, S., and Müller, K.-R. (2011). Single-trial analysis and classification of ERP components – a tutorial. NeuroImage 56, 814–825.10.1016/j.neuroimage.2010.06.048Search in Google Scholar PubMed

Boulay, C.B., Sarnacki, W.A., Wolpaw, J.R., and McFarland, D.J. (2011). Trained modulation of sensorimotor rhythms can affect reaction time. Clin. Neurophysiol. 122, 1820–1826.10.1016/j.clinph.2011.02.016Search in Google Scholar PubMed PubMed Central

Cabrerizo, M., Ayala, M., Goryawala, M., Jayakar, P., and Adjouadi, M. (2012). A new parametric feature descriptor for the classification of epileptic and control EEG records in pediatric population. Int. J. Neural Syst. 22, 1250001.10.1142/S0129065712500013Search in Google Scholar PubMed

Carmena, J.M., Lebedev, M.A., Crist, R.E., O’Doherty, J.E., Santucci, D.M., Dimitrov, D.F., Patil, P.G., and Nicolelis, M.A.L. (2003). Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 1, 193–208.10.1371/journal.pbio.0000042Search in Google Scholar PubMed PubMed Central

Carmena, J.M., Lebedev, M.A., Henriquez, C.S., and Nicolelis, M.A. (2005). Stable ensemble performance with single-neuron variability during reaching movements in primates. J. Neurosci. 25, 10712–10716.10.1523/JNEUROSCI.2772-05.2005Search in Google Scholar

Chao, Z.C., Nagasaka, Y., and Fujii, N. (2010). Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys. Front. Neuroeng. 3, 3.10.3389/fneng.2010.00003Search in Google Scholar

Chapin, J.K., Moxon, K.A., Markowitz, R.S., and Nicolelis, M.A. (1999). Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat. Neurosci. 2, 664–670.10.1038/10223Search in Google Scholar

Chase, S.M., Kass, R.E., and Schwartz, A.B. (2012). Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex. J. Neurophysiol. 108, 624–644.10.1152/jn.00371.2011Search in Google Scholar

Chin-Teng, L., Li-Wei, K., Jin-Chern, C., Jeng-Ren, D., Ruey-Song, H., Sheng-Fu, L., and Tzai-Wen, C. (2008). Noninvasive neural prostheses using mobile and wireless EEG. Proc. IEEE 96, 1167–1183.10.1109/JPROC.2008.922561Search in Google Scholar

Cincotti, F., Mattia, D., Aloise, F., Bufalari, S., Marciani, M.G., Babiloni, F., Schalk, G., and Cherubini, A. (2008). Non-invasive brain-computer interface system: towards its application as assistive technology. Brain Res. Bull. 75, 796–803.10.1016/j.brainresbull.2008.01.007Search in Google Scholar

Colici, S., Zalay, O.C., and Bardakjian, B.L. (2011). Response neuromodulators based on artificial neural networks used to control seizure-like events in a computational model of epilepsy. Int. J. Neural Syst. 21, 367–383.10.1142/S0129065711002894Search in Google Scholar

Collinger, J.L., Wodlinger, B., Downey, J.E., Wang, W., Tyler-Kabara, E.C., Weber, D.J., McMorland, A.J., Velliste, M., Boninger, M.L., and Schwartz, A.B. (2013). High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557–564.10.1016/S0140-6736(12)61816-9Search in Google Scholar

Cong, F., Phan, A.H., Zhao, Q., Huttunen-Scott, T., Kaartinen, J., Ristaniemi, T., Lyytinen, H., and Cichocki, A. (2012). Benefits of multi-domain feature of mismatch negativity extracted by nonnegative tensor factorization from EEG collected by low density array. Int. J. Neural Syst. 22, 1250025.10.1142/S0129065712500256Search in Google Scholar PubMed

Cong, F., Phan, A.H., Astikainen, P., Zhao, Q., Wu, Q., Hietanen, J.K., Ristaniemi, T., and Cichocki, A. (2013). Multi-domain feature extraction for event-related potential through nonnegative multi-way array decomposition from low dense array EEG. Int. J. Neural Syst. 23, 1350006.10.1142/S0129065713500068Search in Google Scholar PubMed

Dai, H., Zhang, H., and Wang, W. (2012). Structural reliability assessment by local approximation of limit state functions using adaptive Markov chain simulation and support vector regression. Comput. Aided Civ. Inf. Eng. 27, 676–686.10.1111/j.1467-8667.2012.00767.xSearch in Google Scholar

Davis, K.A., Sturges, B.K., Vite, C.H., Ruedebusch, V., Worrell, G., Gardner, A.B., and Leyde, K. (2011). A novel implanted device to wirelessly record and analyze continuous intracranial canine EEG. Epilepsy Res. 96, 116–122.10.1016/j.eplepsyres.2011.05.011Search in Google Scholar PubMed PubMed Central

Debener, S., Minow, F., Emkes, R., Gandras, K., and de Vos, M. (2012). How about taking a low-cost, small, and wireless EEG for a walk. Psychophysiology 49, 1617–1621.10.1111/j.1469-8986.2012.01471.xSearch in Google Scholar PubMed

del Riego, R., Otero, J., and Ranilla, J. (2011). A low cost 3D human interface device using GPU-based optical flow algorithms. Integr. Comput. Aided Eng. 18, 391–400.10.3233/ICA-2011-0384Search in Google Scholar

Diez, P.F., Mut, V.A., Avila, P.E.M., and Laciar, L.E. (2011). Asynchronous BCI control using high-frequency SSVEP. J. Neuroeng. Rehabil. 8, 39.10.1186/1743-0003-8-39Search in Google Scholar PubMed PubMed Central

Donchin, E., Spencer, K.M., and Wijesinghe, R. (2000). The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans. Rehabil. Eng. 8, 174–179.10.1109/86.847808Search in Google Scholar PubMed

Donoghue, J.P. (2008). Bridging the brain to the world: a perspective on neural interface systems. Neuron 60, 511–521.10.1016/j.neuron.2008.10.037Search in Google Scholar PubMed

Donoghue, J.P., Nurmikko, A., Black, M., and Hochberg, L.R. (2007). Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia. J. Physiol. 579, 603–611.10.1113/jphysiol.2006.127209Search in Google Scholar PubMed PubMed Central

Esquenazi, A. (2004). Amputation rehabilitation and prosthetic restoration. From surgery to community reintegration. Disabil. Rehabil. 26, 831–836.10.1080/09638280410001708850Search in Google Scholar PubMed

Faller, J., Solis-Escalante, T., Scherer, R., Neuper, C., and Vidaurre, C. (2012). Autocalibration and recurrent adaptation: towards a plug and play online ERD-BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 313–319.10.1109/TNSRE.2012.2189584Search in Google Scholar PubMed

Farwell, L.A. and Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurol. 70, 510–523.10.1016/0013-4694(88)90149-6Search in Google Scholar

Fetz, E.E. (2007). Volitional control of neural activity: implications for brain-computer interfaces. J. Physiol. 579, 571–579.10.1113/jphysiol.2006.127142Search in Google Scholar PubMed PubMed Central

Ficke, R.C. (1992). Digest of Data on Persons with Disabilities. (Washington, DC: National Institute on Disability and Rehabilitation Research).Search in Google Scholar

Finke, A., Lenhardt, A., and Ritter, H. (2009). The MindGame: a P300-based brain-computer interface game. Neural Netw. 22, 1329–1333.10.1016/j.neunet.2009.07.003Search in Google Scholar PubMed

Freitag, S., Graf, W., and Kaliske, M.I. (2011). Recurrent neural networks for fuzzy data. Integr. Comput. Aided Eng. 18, 265–280.10.3233/ICA-2011-0373Search in Google Scholar

Furdea, A., Ruf, C.A., Halder, S., De, M.D., Bogdan, M., Rosenstiel, W., and Matuz, T. (2012). A new (semantic) reflexive brain-computer interface: in search for a suitable classifier. J. Neurosci. Methods 203, 233–240.10.1016/j.jneumeth.2011.09.013Search in Google Scholar PubMed

Gage, G.J., Ludwig, K.A., Otto, K.J., Ionides, E.L., and Kipke, D.R. (2005). Naïve coadaptive cortical control. J. Neural Eng. 2, 52–63.10.1088/1741-2560/2/2/006Search in Google Scholar PubMed

Galán, F., Nuttin, M., Lew, E., Ferrez, P.W., Vanacker, G., Philips, J., and Millán, J.R. (2008). A brain-actuated wheelchair: asynchronous and non-invasive brain-computer interface for continuous control of robots. Clin. Neurophysiol. 119, 2159–2169.10.1016/j.clinph.2008.06.001Search in Google Scholar PubMed

Gancet, J. (2012). MINDWALKER: going one step further with assistive lower limbs exoskeleton for SCI condition subjects. J. Proc. IEEE RAS EMBS Int. Conf. Biomed. Robot. Biomechatronics 1794–1800.10.1109/BioRob.2012.6290688Search in Google Scholar

Garcia-Cuesta, E., Galvan, I.M., and de Castro, A.J. (2011). Recursive discriminant regression analysis to find homogeneous structures. Int. J. Neural Syst. 21, 95–101.10.1142/S0129065711002663Search in Google Scholar PubMed

Georgopoulos, A.P., Schwartz, A.B., and Kettner, R.E. (1986). Neuronal population coding of movement direction. Science 233, 1416–1419.10.1126/science.3749885Search in Google Scholar PubMed

Ghodrati Amiri, G., Abdolahi Rad, A., and Khorasani, M. (2012). Generation of near-field artificial ground motions compatible with median predicted spectra using PSO-based neural network and wavelet analysis. Comput. Aided Civ. Inf. Eng. 27, 711–730.10.1111/j.1467-8667.2012.00783.xSearch in Google Scholar

Ghosh-Dastidar, S. and Adeli, H. (2007). Improved spiking neural networks for EEG classification and epilepsy and seizure detection. Integr. Comput. Aided Eng. 14, 187–212.10.3233/ICA-2007-14301Search in Google Scholar

Ghosh-Dastidar, S. and Adeli, H. (2009). A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection. Neural Netw. 22, 1419–1431.10.1016/j.neunet.2009.04.003Search in Google Scholar PubMed

Ghosh-Dastidar, S., Adeli, H., and Dadmehr, N. (2008). Principal component analysis – enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Trans. Biomed. Eng. 55, 512–518.10.1109/TBME.2007.905490Search in Google Scholar PubMed

Graf, W., Freitag, S., Sickert, J.U., and Kaliske, M. (2012). Structural analysis with fuzzy data and neural network-based material description. Comput. Aided Civ. Inf. Eng. 27, 640–654.10.1111/j.1467-8667.2012.00779.xSearch in Google Scholar

Guger, C., Harkam, W., Hertnaes, C., and Pfurtscheller, G. (1999). Prosthetic control by an EEG-based brain-computer Interface (BCI). Proc. AAATE 5th Eur. Conf. Adv. Assist. Technol. 590–595.Search in Google Scholar

Guger, C., Schlögl, A., Neuper, C., Walterspacher, D., Strein, T., and Pfurtscheller, G. (2001). Rapid prototyping of an EEG-based brain-computer interface (BCI). IEEE Trans. Neural Syst. Rehabil. Eng. 9, 49–58.10.1109/7333.918276Search in Google Scholar PubMed

Gupta, R. and Ashe, J. (2009). Offline decoding of end-point forces using neural ensembles: application to a brain-machine interface. IEEE Trans. Neural Syst. Rehabil. Eng. 17, 254–262.10.1109/TNSRE.2009.2023290Search in Google Scholar PubMed

Gustavsson, A., Svensson, M., Jacobi, F., Allgulander, C., Alonso, J., Beghi, E., and Dodel, R. (2011). Cost of disorders of the brain in Europe 2010. J. Eur. Coll. Neuropsychopharmacol. 21, 718–779.10.1016/j.euroneuro.2011.08.008Search in Google Scholar PubMed

Hammer, E.M., Halder, S., Blankertz, B., Sannelli, C., Dickhaus, T., Kleih, S., Müller, K.-R., and Kubler, A. (2012). Psychological predictors of SMR-BCI performance. Biol. Psychol. 89, 80–86.10.1016/j.biopsycho.2011.09.006Search in Google Scholar PubMed

Han, F., Wiercigroch, M., Fang, J.A., and Wang, Z. (2011). Excitement and synchronization of small-world neuronal networks with short-term synaptic plasticity. Int. J. Neural Syst. 21, 415–425.10.1142/S0129065711002924Search in Google Scholar PubMed

Hatsopoulos, N.G. and Donoghue, J.P. (2009). The science of neural interface systems. Annu. Rev. Neurosci. 32, 249–266.10.1146/annurev.neuro.051508.135241Search in Google Scholar PubMed PubMed Central

Hinterberger, T., Weiskopf, N., Veit, R., Wilhelm, B., Betta, E., and Birbaumer, N. (2004). An EEG-driven brain-computer interface combined with functional magnetic resonance imaging (fMRI). IEEE Trans. Biomed. Eng. 51, 971–974.10.1109/TBME.2004.827069Search in Google Scholar PubMed

Hinterberger, T., Veit, R., Wilhelm, B., Weiskopf, N., Vatine, J.J., and Birbaumer, N. (2005). Neuronal mechanisms underlying control of a brain-computer interface. Eur. J. Neurosci. 21, 3169–3181.10.1111/j.1460-9568.2005.04092.xSearch in Google Scholar PubMed

Hochberg, L.R., Serruya, M.D., Friehs, G.M., Mukand, J.A., Saleh, M., Caplan, A.H., Branner, A., Chen, D., Penn, R.D., and Donoghue, J.P. (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171.10.1038/nature04970Search in Google Scholar PubMed

Hochberg, L.R., Bacher, D., Jarosiewicz, B., Masse, N.Y., Simeral, J.D., Vogel, J., Haddadin, S., Liu, J., Cash, S.S., and Van der, P. (2012). Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372–375.10.1038/nature11076Search in Google Scholar PubMed PubMed Central

Hou, J., Chen, Z., Qin, X., and Zhang, D. (2011). Automatic image search based on improved feature descriptors and decision tree. Integr. Comput. Aided Eng. 18, 167–180.10.3233/ICA-2011-0364Search in Google Scholar

Hsiao, F.Y., Wang, S.S., Wang, W.C., Wen, C.P., and Yu, W.D. (2012). Neuro-fuzzy cost estimation model enhanced by fast messy genetic algorithms for semiconductor hookup construction. Comput. Aided Civ. Inf. Eng. 27, 764–781.10.1111/j.1467-8667.2012.00786.xSearch in Google Scholar

Hsu, W.Y. (2011). Continuous EEG signal analysis for asynchronous BCI application. Int. J. Neural Syst. 21, 335–350.10.1142/S0129065711002870Search in Google Scholar PubMed

Hsu, W.Y. (2012). Application of competitive Hopfield neural network clustering to brain-computer interface systems. Int. J. Neural Syst. 22, 51–62.10.1142/S0129065712002979Search in Google Scholar PubMed

Hsu, W.Y. (2013). Single-trial motor imagery classification using asymmetry ratio, phase relation and wavelet-based fractal features, and their selected combination. Int. J. Neural Syst. 23, 1350007.10.1142/S012906571350007XSearch in Google Scholar PubMed

Hsu, W.Y., Lin, C.H., Hsu, H.J., Chen, P.H., and Chen, I.R. (2012). Wavelet-based envelope features with automatic EOG artifact removal: application to single-trial EEG data. Expert Syst. Appl. 39, 2743–2749.10.1016/j.eswa.2011.08.132Search in Google Scholar

Huang, D., Qian, K., Fei, D.-Y., Bai, O., Jia, W., and Chen, X. (2012). Electroencephalography (EEG)-based brain-computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 379–388.10.1109/TNSRE.2012.2190299Search in Google Scholar PubMed

Iturrate, I., Antelis, J.M., Kubler, A., and Minguez, J. (2009). A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation. IEEE Trans. Robot. 25, 614–627.10.1109/TRO.2009.2020347Search in Google Scholar

Iversen, I.H., Ghanayim, N., Kubler, A., Neumann, N., Birbaumer, N., and Kaiser, J. (2008). Conditional associative learning examined in a paralyzed patient with amyotropic lateral sclerosis using brain-computer interface technology. Behav. Brain Funct. 4, 1–53.10.1186/1744-9081-4-53Search in Google Scholar PubMed PubMed Central

Jiang, X. and Adeli, H. (2004). Wavelet packet-autocorrelation function method for traffic flow pattern analysis. Comput. Aided Civ. Inf. Eng. 19, 324–337.10.1111/j.1467-8667.2004.00360.xSearch in Google Scholar

Jiang, X., Ma, Z.J., and Ren, W.X. (2012). Crack detection from the slope of the mode shape using complex continuous wavelet transform. Comput. Aided Civ. Inf. Eng. 27, 187–201.10.1111/j.1467-8667.2011.00734.xSearch in Google Scholar

Jin, J., Sellers, E.W., and Wang, X. (2012a). Targeting an efficient target-to-target interval for P300 speller brain-computer interface. Med. Biol. Eng. Comput. 50, 289–296.10.1007/s11517-012-0868-xSearch in Google Scholar PubMed PubMed Central

Jin, J., Allison, B.Z., Wang, X., and Neuper, C. (2012b). A combined brain-computer interface based on P300 potentials and motion-onset visual evoked potentials. J. Neurosci. Methods 205, 265–275.10.1016/j.jneumeth.2012.01.004Search in Google Scholar PubMed

Jumutc, V., Zayakin, P., and Borisov, A. (2011). Ranking-based kernels in applied biomedical diagnostics using support vector machine. Int. J. Neural Syst. 21, 459–473.10.1142/S0129065711002961Search in Google Scholar PubMed

Junfei, Q. and Honggui, H. (2010). A repair algorithm for radial basis function neural network with application to chemical oxygen demand modeling. Int. J. Neural Syst. 20, 63–74.10.1142/S0129065710002243Search in Google Scholar PubMed

Kamousi, B., Liu, Z., and He, B. (2005). Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis. IEEE Trans. Neural Syst. Rehabil. Eng. 13, 166–171.10.1109/TNSRE.2005.847386Search in Google Scholar PubMed

Kayagil, T.A., Bai, O., Henriquez, C.S., Lin, P., Furlani, S.J., Vorbach, S., and Hallett, M. (2009). A binary method for simple and accurate two-dimensional cursor control from EEG with minimal subject training. J. Neuroeng. Rehabil. 6, 1–14.10.1186/1743-0003-6-14Search in Google Scholar PubMed PubMed Central

Kilgard, M.P., Vazquez, J.L., Engineer, N.D., and Pandya, P.K. (2007). Experience dependent plasticity alters cortical synchronization. Hear. Res. 229, 171–179.10.1016/j.heares.2007.01.005Search in Google Scholar PubMed PubMed Central

Koyama, S., Chase, S.M., Whitford, A.S., Velliste, M., Schwartz, A.B., and Kass, R.E. (2010). Comparison of brain-computer interface decoding algorithms in open-loop and closed-loop control. J. Comput. Neurosci. 29, 73–87.10.1007/s10827-009-0196-9Search in Google Scholar PubMed

Kreilinger, A. (2012). Error potential detection during continuous movement of an artificial arm controlled by brain-computer interface. Med. Biol. Eng. Comput. 50, 223–230.10.1007/s11517-011-0858-4Search in Google Scholar PubMed

Krusienski, D. and Shih, J. (2010). Control of a visual keyboard using an electrocorticographic brain-computer interface. Neurorehabil. Neural Repair 25, 323–331.10.1177/1545968310382425Search in Google Scholar PubMed PubMed Central

Krusienski, D.J., Sellers, E.W., McFarland, D.J., Vaughan, T.M., and Wolpaw, J.R. (2008). Towards enhanced P300 speller performance. J. Neurosci. Methods 167, 15–21.10.1016/j.jneumeth.2007.07.017Search in Google Scholar PubMed PubMed Central

Krusienski, D.J., McFarland, D.J., and Wolpaw, J.R. (2012). Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface. Brain Res. Bull. 87, 130–134.10.1016/j.brainresbull.2011.09.019Search in Google Scholar PubMed PubMed Central

Kübler, A. (2005). Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 64, 1775–1777.10.1212/01.WNL.0000158616.43002.6DSearch in Google Scholar PubMed

Kübler, A. and Birbaumer, N. (2008). Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients. Clin. Neurophysiol. 119, 2658–2666.10.1016/j.clinph.2008.06.019Search in Google Scholar PubMed PubMed Central

Lance, B.J., Kerick, S.E., Ries, A.J., Oie, K.S., and McDowell, K. (2012). Brain-computer interface technologies in the coming decades. Proc. IEEE 100, 1585–1599.10.1109/JPROC.2012.2184830Search in Google Scholar

Larkindale, J., Yang, W., Hogan, P.F., Simon, C.J., Zhang, Y., Jain, A., Habeeb-Louks, E.M., and Cwik, V.A. (2013). Cost of illness for neuromuscular diseases in the U.S. Muscle Nerve DOI: 10.1002/mus.23942.10.1002/mus.23942Search in Google Scholar PubMed

Laubach, M., Wessberg, J., and Nicolelis, M.A. (2000). Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task. Nature 405, 567–571.10.1038/35014604Search in Google Scholar PubMed

Lauer, R.T., Peckham, P.H., and Kilgore, K.L. (1999). EEG-based control of a hand grasp neuroprosthesis. NeuroReport 10, 1767–1771.10.1097/00001756-199906030-00026Search in Google Scholar PubMed

Lemm, S., Blankertz, B., Curio, G., and Müller, K.R. (2005). Spatio-spectral filters for improving the classification of single trial EEG. IEEE Trans. Biomed. Eng. 52, 1541–1548.10.1109/TBME.2005.851521Search in Google Scholar PubMed

Leuthardt, E.C., Schalk, G., Wolpaw, J.R., Ojemann, J.G., and Moran, D.W. (2004). A brain-computer interface using electrocorticographic signals in humans. J. Neural Eng. 1, 63–71.10.1088/1741-2560/1/2/001Search in Google Scholar PubMed

Li, J., Liang, J., Zhao, Q., Hong, K., and Zhang, L. (2013). Design of wheelchair assistive system directly steered by human thoughts. Int. J. Neural Syst. 23, 1350013.10.1142/S0129065713500135Search in Google Scholar PubMed

Liao, L.-D., Chang, J.-Y., Lin, C.-T., Ko, L.-W., McDowell, K., Wickenden, A.E., and Gramann, K. (2012). Biosensor technologies for augmented brain-computer interfaces in the next decades. Proc. IEEE 100, 1553–1566.10.1109/JPROC.2012.2184829Search in Google Scholar

Lin, C.M., Ting, A.B., Hsu, C.F., and Chung, C.M. (2012). Adaptive control for MIMO uncertain nonlinear systems using recurrent wavelet neural network. Int. J. Neural Syst. 22, 37–50.10.1142/S0129065712002992Search in Google Scholar PubMed

Linderman, M.D., Santhanam, G., Gilja, V., O’Driscoll, S., Yu, B.M., Afshar, A., Kemere, C.T., and Meng, T.H. (2008). Signal processing challenges for neural prostheses. IEEE Signal Proc. Mag. 25, 18–28.10.1109/MSP.2008.4408439Search in Google Scholar

Liu, C., Wang, J., Chen, Y.Y., Deng, B., Wei, X.L., and Li, H.Y. (2013). Closed-loop control of the thalamocortical relay neuron’s parkinsonian state based on slow variable. Int. J. Neural Syst. 23, 1350017.10.1142/S0129065713500172Search in Google Scholar

Lopez-Gordo, M.A., Pelayo, F., Prieto, A., and Fernandez, E. (2012). An auditory brain-computer interface with accuracy prediction. Int. J. Neural Syst. 22, 1250009.10.1142/S0129065712500098Search in Google Scholar

Lotte, F., Congeado, M., Lecuyer, A., Lamarche, F., and Arnaldi, B. (2007). A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4, R1–R13.10.1088/1741-2560/4/2/R01Search in Google Scholar

Luis de Mello, F., Strauss, E., and Fernandes de Oliveira, A.A. (2011). Computer theory and digital image processing applied to emotional brain activation recognition. Integr. Comput. Aided Eng. 18, 157–166.10.3233/ICA-2011-0368Search in Google Scholar

Malik, W.Q., Brown, E.N., Hochberg, L.R., and Truccolo, W. (2011). Efficient decoding with steady-state Kalman filter in neural interface systems. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 25–34.10.1109/TNSRE.2010.2092443Search in Google Scholar

Manyakov, N.V., Chumerin, N., and Van Hulle, M.M. (2012). Multichannel decoding for phase-coded SSVEP brain-computer interface. Int. J. Neural Syst. 22, 1250022.10.1142/S0129065712500220Search in Google Scholar

Martins, A.L.D., Mascarenhas, N.D.A., and Suazo, C.A.T. (2011). Spatio-temporal resolution enhancement of vocal tract MRI sequences based on image registration. Integr. Comput. Aided Eng. 18, 143–156.10.3233/ICA-2011-0367Search in Google Scholar

Martis, R.J., Acharya, U.R., Tan, J.H., Petznick, A., Yanti, R., Chua, C.K., Ng, E.Y., and Tong, L. (2012). Application of empirical mode decomposition (EMD) for automated detection of epilepsy using EEG signals. Int. J. Neural Syst. 22, 1250027.10.1142/S012906571250027XSearch in Google Scholar

Maynard, E.M., Nordhausen, C.T., and Normann, R.A. (1997). The Utah intracortical electrode array: a recording structure for potential brain-computer interfaces. Electroencephalogr. Clin. Neurophysiol. 102, 228–239.10.1016/S0013-4694(96)95176-0Search in Google Scholar

McFarland, D.J., Anderson, C.W., Müller, K.R., Schlögl, A., and Krusienski, D.J. (2006). BCI Meeting 2005 workshop on BCI signal processing: feature extraction and translation. IEEE Trans. Neural Syst. Rehabil. Eng. 14, 135–139.10.1109/TNSRE.2006.875637Search in Google Scholar PubMed

McFarland, D.J., Sarnacki, W.A., and Wolpaw, J.R. (2010). Electroencephalographic (EEG) control of three-dimensional movement. J. Neural Eng. 7, 036007.10.1088/1741-2560/7/3/036007Search in Google Scholar PubMed PubMed Central

Mehta, N.A., Hameed, S.H.S., and Jackson, M.M. (2011). Optimal control strategies for an SSVEP-based brain-computer interface. Int. J. Hum. Comput. Int. 27, 85–101.10.1080/10447318.2011.535755Search in Google Scholar

Mellinger, J., Schalk, G., Braun, C., Preissl, H., Rosenstiel, W., Birbaumer, N., and Kubler, A. (2007). An MEG-based brain-computer interface (BCI). Neuroimage 36, 581–593.10.1016/j.neuroimage.2007.03.019Search in Google Scholar PubMed PubMed Central

Millán, J.R. and Carmena, J. (2010). Invasive or noninvasive: understanding brain-machine interface technology. IEEE Eng. Med. Biol. Mag. 29, 16–22.10.1109/MEMB.2009.935475Search in Google Scholar PubMed

Millán, J.R., Renkens, F., Mourinũo, J., and Gerstner, W. (2004). Brain-actuated interaction. Artif. Intell. 159, 241–259.10.1016/j.artint.2004.05.008Search in Google Scholar

Moritz, C.T., Perlmutter, S.I., and Fetz, E.E. (2008). Direct control of paralysed muscles by cortical neurons. Nature 456, 639–643.10.1038/nature07418Search in Google Scholar PubMed PubMed Central

Müller-Putz, G.R., and Pfurtscheller, G. (2008). Control of an electrical prosthesis with an SSVEP-Based BCI. IEEE Trans. Biomed. Eng. 55, 361–364.10.1109/TBME.2007.897815Search in Google Scholar PubMed

Müller-Putz, G.R., Scherer, R., Pfurtscheller, G., and Rupp, R. (2005). EEG-based neuroprosthesis control: a step towards clinical practice. Neurosci. Lett. 382, 169–174.10.1016/j.neulet.2005.03.021Search in Google Scholar PubMed

Müller-Putz, G.R., Pokorny, C., Klobassa, D.S., and Horki, P. (2013). A single switch BCI based on passive and imagined movements: towards restoring communication in minimally conscious patients. Int. J. Neural Syst. 23, 1250037.10.1142/S0129065712500372Search in Google Scholar PubMed

Murray, C.J.L. and Lopez, A.D. (1996).The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries, and Risk Factors in 1990 and Projected to 2020. (Cambridge, MA, USA: Harvard School of Public Health).Search in Google Scholar

Nam, C.S., Schalk, G., and Jackson, M.M. (2011). Editorial: current trends in brain-computer interface (BCI) research and development. Int. J. Hum. Comput. Int. 27, 40912.Search in Google Scholar

Navarro, X., Krueger, T.B., Lago, N., Micera, S., Stieglitz, T., and Dario, P. (2005). A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems. J. Peripher. Nerv. Syst. 10, 229–258.10.1111/j.1085-9489.2005.10303.xSearch in Google Scholar PubMed

Nicolelis, M.A.L. and Lebedev, M.A. (2009). Principles of neural ensemble physiology underlying the operation of brain-machine interfaces. Nat. Rev. Neurosci. 10, 530–540.10.1038/nrn2653Search in Google Scholar

Osornio-Rios, R.A., Amezquita-Sanchez, J.P., Romero-Troncoso, R.J., and Garcia-Perez, A. (2012). Music-neural network analysis for locating structural damage in Truss-type structures by means of vibrations. Comput. Aided Civ. Inf. Eng. 27, 687–698.10.1111/j.1467-8667.2012.00777.xSearch in Google Scholar

Pasqualotto, E., Federici, S., and Belardinelli, M.O. (2012). Towards functioning and usable brain-computer interfaces (BCIs): a literature review. Disabil. Rehabil. Assist. Technol. 7, 89–103.10.3109/17483107.2011.589486Search in Google Scholar

Patrinos, P., Alexandridis, A., Ninos, K., and Sarimveis, H. (2010). Variable selection in nonlinear modeling based on RBF networks and evolutionary computation. Int. J. Neural Syst. 20, 365–379.10.1142/S0129065710002474Search in Google Scholar

Pfurtscheller, G., Guger, C., Müller, G., Krausz, G., and Neuper, C. (2000). Brain oscillations control hand orthosis in a tetraplegic. Neurosci. Lett. 292, 211–214.10.1016/S0304-3940(00)01471-3Search in Google Scholar

Pfurtscheller, G., Müller, G.R., Pfurtscheller, J., Gerner, H.J., and Rupp, R. (2003). ‘Thought’ – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci. Lett. 351, 33–36.10.1016/S0304-3940(03)00947-9Search in Google Scholar

Puscasu, G. and Codres, B. (2011). Nonlinear system identification and control based on modular neural networks. Int. J. Neural Syst. 21, 319–334.10.1142/S0129065711002869Search in Google Scholar PubMed

Qin, L., Ding, L., and He, B. (2005). Motor imagery classification by means of source analysis for brain computer interface application. J. Neural Eng. 1, 135–141.10.1088/1741-2560/1/3/002Search in Google Scholar PubMed

Rakotomamonjy, A. and Guigue, V. (2008). BCI competition III: dataset II-ensemble of SVMs for BCI P300 Speller. IEEE Trans. Biomed. Eng. 55, 1147–1154.10.1109/TBME.2008.915728Search in Google Scholar PubMed

Rangaprakash, D., Hu, X., and Deshpande, G. (2013). Phase synchronization in brain networks derived from correlation between probabilities of recurrences in functional MRI data. Int. J. Neural Syst. 23, 1350003.10.1142/S0129065713500032Search in Google Scholar PubMed

Rossello, J.L., Canals, V., Morro, A., and Oliver, A. (2012). Hardware implementation of stochastic spiking neural networks. Int. J. Neural Syst. 22, 1250014.10.1142/S0129065712500141Search in Google Scholar PubMed

Saliminejad, S. and Gharaibeh, N.G. (2012). A spatial-Bayesian technique for imputing pavement network repair data. Comput. Aided Civ. Inf. Eng. 27, 594–607.10.1111/j.1467-8667.2012.00762.xSearch in Google Scholar

Sankari, Z. and Adeli, H. (2011). Probabilistic neural networks for EEG-based diagnosis of Alzheimer’s disease using conventional and wavelet coherence. J. Neurosci. Methods 197, 165–170.10.1016/j.jneumeth.2011.01.027Search in Google Scholar PubMed

Sankari, Z., Adeli, H., and Adeli, A. (2012). Wavelet coherence model for diagnosis of Alzheimer’s disease. Clin. EEG Neurosci. 43, 268–278.10.1177/1550059412444970Search in Google Scholar PubMed

Scherberger, H. (2009). Neural control of motor prostheses. Curr. Opin. Neurobiol. 19, 629–633.10.1016/j.conb.2009.10.008Search in Google Scholar PubMed

Schwartz, A.B., Cui, X.T., Weber, D.J., and Moran, D.W. (2006). Brain-controlled interfaces: movement restoration with neural prosthetics. Neuron 52, 205–220.10.1016/j.neuron.2006.09.019Search in Google Scholar PubMed

Setiono, R., Baesens, B., and Mues, C. (2011). Rule extraction from minimal neural network for credit card screening. Int. J. Neural Syst. 21, 265–276.10.1142/S0129065711002821Search in Google Scholar PubMed

Shih, J.J., Krusienski, D.J., and Wolpaw, J.R. (2012). Brain-computer interface in medicine. Mayo Clin. Proc. 87, 268–279.10.1016/j.mayocp.2011.12.008Search in Google Scholar PubMed PubMed Central

Shin, H-C., Aggarwal, V., Acharya, S., Thakor, N.V., and Schieber, M.H. (2010). Neural decoding of finger movements using Skellam-based maximum-likelihood decoding. IEEE Trans. Biomed. Eng. 57, 754–760.10.1109/TBME.2009.2020791Search in Google Scholar PubMed

Sitaram, R., Zhang, H.H., Guan, C.T., Thulasidas, M., Hoshi, Y., Ishikawa, A., Shimizu, K., and Birbaumer, N. (2006). Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface. Neuroimage 34, 1416–1427.10.1016/j.neuroimage.2006.11.005Search in Google Scholar PubMed

Spüler, M., Bensch, M., Kleih, S., Rosenstiel, W., Bogdan, M., and Kübler, A. (2012). Online use of error-related potentials in healthy users and people with sever motor impairment increases performance of P300-BCI. Clin. Neurophysiol. 123, 1328–1337.10.1016/j.clinph.2011.11.082Search in Google Scholar

Stansbury, L.G., Branstetter, J.G., and Lalliss, S.J. (2007). Amputation in military trauma surgery. J. Trauma 63, 940–944.10.1097/TA.0b013e31814934d8Search in Google Scholar

Sykacek, P., Roberts, S.J., and Stokes, M. (2004). Adaptive BCI based on variational Bayesian Kalman filtering; an empirical evaluation. IEEE Trans. Biomed. Eng. 51, 719–727.10.1109/TBME.2004.824128Search in Google Scholar

Tao, H., Zain, J.M., Ahmed, M.M., Abdalla, A.N., and Jing, W. (2012). A wavelet-based particle swarm optimization algorithm for digital image watermarking. Integr. Comput. Aided Eng. 19, 81–91.10.3233/ICA-2012-0392Search in Google Scholar

Tomasevic, N.M., Neskovic, A.M., and Neskovic, N.J. (2012). Artificial neural network-based approach to EEG signal simulation. Int. J. Neural Syst. 22, 1250008.10.1142/S0129065712500086Search in Google Scholar

Torriente, I., Valdes-Sosa, M., Ramirez, D., and Bobes, M.A. (1999). Visual evoked potentials related to motion-onset are modulated by attention. Vision Res. 39, 4122–4139.10.1016/S0042-6989(99)00113-3Search in Google Scholar

Vidaurre, C., Schlögl, A., Cabeza, R., Scherer, R., and Pfurtscheller, G. (2006). A fully on-line adaptive BCI. IEEE Trans. Biomed. Eng. 53, 1214–1219.10.1109/TBME.2006.873542Search in Google Scholar PubMed

Waldert, S., Pistohl, T., Braun, C., Ball, T., Aertsen, A., and Mehring, C. (2009). A review on directional information in neural signals for brain-machine interfaces. J. Physiol. Paris 103, 244–254.10.1016/j.jphysparis.2009.08.007Search in Google Scholar PubMed

Wandekokem, E.D., Mendel, E., Fabris, F., Valentim, M., Batista, R.J., Varejao, F.M., and Rauber, T.W. (2011). Diagnosing multiple faults in oil rig motor pumps using support vector machine classifier ensembles. Integr. Comput. Aided Eng. 18, 61–74.10.3233/ICA-2011-0361Search in Google Scholar

Wessberg, J., Stambaugh, C.R., Kralik, J.D., Beck, P.D., Laubach, M., Chapin, J.K., and Kim, J. (2000). Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361–365.10.1038/35042582Search in Google Scholar PubMed

White, J.R., Levy, T., Bishop, W., and Beaty, J.D. (2010). Real-time decision fusion for multimodal neural prosthetic device. PLoS ONE 5, 1.10.1371/journal.pone.0009493Search in Google Scholar PubMed PubMed Central

Wolpaw, J.R., McFarland, D.J., and Bizzi, E. (2004). Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc. Natl. Acad. Sci. USA 101, 17849–17854.10.1073/pnas.0403504101Search in Google Scholar PubMed PubMed Central

Wu, W., Black, M.J., Gao, Y., Bienenstock, E., Serruya, M., Shaikhouni, A., and Donoghue, J.P. (2003). Neural decoding of cursor motion using a Kalman filter. Adv. Neural Info. Proc. Syst. 15, 130–140.Search in Google Scholar

Wu, D., Warwick, K., Ma, Z., Gasson, M.N., Burgess, J.G., Pan, S., and Aziz, T.Z. (2010). Prediction of Parkinson’s disease tremor onset using radial basis function neural network based on particle swarm optimization. Int. J. Neural Syst. 20, 109–116.10.1142/S0129065710002292Search in Google Scholar PubMed

Xiang, J. and Liang, M. (2012). Wavelet-based detection of beam cracks using modal shape and frequency measurements. Comput. Aided Civ. Inf. Eng. 27, 439–454.10.1111/j.1467-8667.2012.00760.xSearch in Google Scholar

Yamanishi, T., Liu, J.Q., and Nishimura, H. (2012). Modeling fluctuations in default-mode brain network using a spiking neural network. Int. J. Neural Syst. 22, 1250016.10.1142/S0129065712500165Search in Google Scholar PubMed

Yang, B., Yan, G., Yan, R., and Wu, T. (2007). Adaptive subject-based feature extraction in brain-computer interfaces using wavelet packet best basis decomposition. Med. Eng. Phys. 29, 48–53.10.1016/j.medengphy.2006.01.009Search in Google Scholar PubMed

Received: 2013-5-18
Accepted: 2013-8-21
Published Online: 2013-09-28
Published in Print: 2013-10-01

©2013 by Walter de Gruyter Berlin Boston

Downloaded on 29.3.2024 from https://www.degruyter.com/document/doi/10.1515/revneuro-2013-0032/html
Scroll to top button