摘要
脑是自然界中最复杂的器官之一，是人体的中央控制器，控制和调节着人的情感、认知、感觉、行为等活动。了解和认识脑的结构、功能和活动表达可以揭开人类脑的秘密，为脑疾病的治疗提供坚实的基础。因此，该课题的研究具有重要的理论意义和应用价值。本论文将以脑电（Electroencephalogram，EEG）信号作为研究对象，探索在不同的脑思维状态下脑电的表达方式，揭示脑工作的机理。然后，根据这些脑工作的机理来发展识别算法，从而解码在不同的脑思维状态下的脑电表征。解码脑思维状态的一个重要应用是构建脑机接口系统。脑机接口系统可以使得人们直接通过自己的脑与外界世界进行沟通交流，而不需要通过传统的神经和肌肉输出通路。本论文从揭示脑在不同思维下的脑工作机理，到发展机器学习算法来识别分类脑思维状态，到构建脑机接口系统应用原型，对整个过程进行了系统化地研究。
本文的主要贡献和创新点主要体现在以下方面：
1.脑机接口（Brain Computer Interface，BCI）研究领域大部分论文集中于算法性能提高方面的研究，而相对忽视了受试者产生易分脑电模式的训练范式的研究。我们结合算法模型和受试者训练两方面，提出了双向自适应训练框架。在双向自适应训练框架中，在训练时受试者的自我调节因素被充分考虑进去，受试者可以根据神经反馈的信息来更好地完成被要求的想象任务。此两个过程交替进行，互相学习，以使得两者之间达到动态平衡。通过受试者的调节和BCI模型的更新学习来提高系统的稳定性。
2.传统训练范式存在的问题是当受试者有时候想象的运动类型和提示给出的类型不一样时，用于标记数据的类型还是按照提示给出的类型标记。那么误标的数据会干扰模型的训练，将会降低模型的性能和有效性。为了尽量避免这个问题的出现，我们提出了一种主动训练范式。在主动训练范式中，在必要时受试者有机会去确认前面一个trial数据的标签。与传统的训练范式相比较，我们提出的范式使得受试者更加主动地参与到实验中。受试者不仅可以通过调节自己的脑活动来影响训练效果，而且也可以通过数据的标示来更主动地影响训练过程。
3.为了探讨不同尺度下运动想象引起事件相关电位的异同，我们设计了食指（小尺度）和手臂（大尺度）的运动想象对比实验，检验不同尺度下的运动想象对性能的影响，分别从分类准确率和频谱能量两方面来衡量对比不同尺度下运动想象效果的优劣。本实验的目的是研究不同尺度下不同想象运动类型的性能差异，从而可以为运动想象模式的选择提供指导，为从事运动想象范式的研究人员在想象模式选择中提供经验参考。
4.设计了一个延迟运动执行的实验范式用于研究在意图运动方向准备过程中脑活动的机理和动态变化过程。相对于意图运动方向来说，ERP（Event Related Potential）的幅值在PPC（Posterior ParietalCortex）区域呈现同侧正，对侧负的现象。在后期，α频带的能量呈现明显的同侧能量增强，对侧能量减弱的现象。另外，在后期我们在额叶中间和顶叶中间区域观察到了与方向相关的脑活动（慢波）。ERP和频谱能量的分析结果显示在视觉运动转换和视觉空间注意这两个过程中PPC区域记录的EEG中包含了意图运动方向相关的互补的信息。因此，结合ERP特征和频谱能量特征可以提高意图运动方向的解码能力。
5.根据不同领域的应用和不同的使用目的，我们设计开发了辅助轮椅控制系统、多人赛车系统、网页浏览系统和脑电数据可视化系统。辅助轮椅控制系统旨在帮助运动功能严重受限的患者实现自主地移动，而不需要借助于看护人员的帮助。多人赛车系统的目的是提供一种基于BCI技术的游戏控制原型，可以为游戏领域提供一种新的控制输入方式。网页浏览系统是为了实现大脑直接控制浏览网上的信息，构建脑与网络之间的交流通路。为了简单直观地呈现脑电数据中的本质信息，我们开发了脑电可视化系统。
6.设计开发了运动功能康复训练平台，用于实际的康复训练当中。通过患者的想象运动来训练脑运动区域的神经元细胞和功能性电刺激设备等刺激外周神经和肌肉的感觉通路来闭环的训练运动传输通路，使得神经元细胞连接重组或代偿形成新的网络链接，恢复原来受损的运动功能，实现脑运动功能的重建与康复。
总之，本论文研究了在特定任务下脑活动的动态过程，揭示了人脑在不同思维状态下的工作机理。设计了两种训练范式，提高了训练的性能和可靠性。此外，设计和开发了几个基于BCI技术的系统原型，开发了运动功能康复训练平台，用于患者的康复训练当中。该论文的工作揭示了脑在不同思维状态下的神经机理，开发了不同功能的BCI系统，将为脑研究和未来BCI技术的现实应用提供理论基础和技术原型。
Brain is one of the most complex organs in nature. And, it is the control center ofwhole body, involved in activities such as emotion, cognition, sense, and behavior. Inves-tigation in structure, function, and activity representation of brain will reveal mysteries ofbrain, and provide a solid basis for treatment of brain diseases. Therefore, research of brainmechanisms, in this dissertation, is of important significance and practical value. In thisdissertation, we investigated the expressions of cerebral activities under circumstances ofdifferent statuses of thoughts, and revealed the mechanisms of brain activities. According tothe mechanisms, we developed recognition algorithms to classify different states of activitiesunder circumstances of different statuses of thoughts. An important application for decod-ing brain activities is to build a brain computer interface. Brain computer interface (BCI)allows people to directly communicate with external world without the traditional pathwaywhich needs helps of external nerves and muscles. We systematically studied on the wholeprocess, including revelation of cerebral activities under circumstances of different statusesof thoughts, development of recognition algorithms for classification of brain activities, andbuilding of brain computer interface systems.
The main contributions of this dissertation are given as follows:
1. Existent studies in the field of BCI mainly emphasize on improving BCI system byprogress in BCI model, but ignoring human neuro-feedback adaptive adjustment whichis considered as the other aspect to improve performance of BCI. In our bilateral train-ing paradigm, we take human factors into consideration during the process of training.Subjects are requested to adjust their brain activities according to neuro-feedback re-flecting ongoing neural activities and to try their best to correctly achieve tasks ofmotor imagery. BCI model training and human training are implemented alternately.Human and BCI system are both trained in order to adapt to each other and graduallyattain a dynamic equilibrium. Hence, the reliability of system is improved due to themutual adaptation of human subject and BCI model.
2. A problem within the traditional training paradigm is that a trial is probably misla-beled if the participant don’t imagine the required type of motor imagery accordingto the given cue. Then, those mislabeled trials affect model training, which leads toa decrease of validity and performance of that model. Hence, we proposed an activetraining paradigm to solve this problem. With active training paradigm, the partic-ipants have a chance to confirm or disconfirm the label of the preceding trial whenthey feel it is necessary. Our paradigm makes participants more actively in the ex-periment than the traditional paradigm. Thus, participants are able to influence thetraining effect not only through modulation of their brain activities, but also throughactive engagement in labeling trials.
3. In order to investigate effects on event-related potential (ERP) caused by different scalemotor imageries, we designed comparative experiment between index finger motorimagery (small scale) and arm motor imagery (large scale). Performances of differentscale motor imageries are evaluated in terms of classification accuracy and spectralpower. The aim of this experiment is to explore a mode, which can induce ERP moreeasily. We expect to provide a positive experience for researchers who engage in motorimagery experiments and make them more expediently select suitable type of motorimagery.
4. A delayed movement paradigm was designed to investigate brain activities and mech-anisms in the human brain during planning of reaching movements. The ERP ampli-tude shows ipsilateral positivity and contralateral negativity in the PPC with respectto the movement direction. EEG spectra in the α band exhibit a distinct pattern ofipsilateral spectral augmentation and contralateral spectral depression with respect tothe movement direction at a later stage. During this later stage, direction-related ac-tivities (slow waves) in the medial frontal and medial parietal areas are found. Theresults of ERP and spectral power analysis showed that EEG signals generated in thePPC areas carry complementary information about intended movement direction in thebrain processes of visuomotor transmission and visuospatial attention. Hence, decod-ing accuracy could be improved by combination of ERP features and spectral powerfeatures.
5. We designed and developed assistive wheelchair system, multi-person car racing sys-tem, web browsing system and EEG visualization system according to a variety ofapplication purposes. Assistive wheelchair system is used to help people with severe motor disabilities restore movements, without assistance of caregivers. multi-personcar racing system is a prototype of BCI-based game, and could become a new inputmethod in the future game development. In addition, a web browsing system is devel-oped to directly access network information from brain. In order to display essentialinformation contained in EEG signals, we developed EEG visualization system.
6. We developed a training platform of motor function rehabilitation, which has beentested in hospital. This platform could help patients restore or rebuild the motor func-tion by motor imagery and external FES. Activation in motor cortex area through motorimagery and stimuli in peripheral nerves and muscles through FES result in restoringor renewing neuron connections, and rebuilding the pathway between brain and limb.
In summary, this dissertation investigated the mechanisms in human brain under cir-cumstances of specified tasks, such as delayed reaching movements, motor imagery. Twotraining paradigms are proposed to improve the performance of BCI training. In addition, afew BCI application prototypes have been developed for a variety of application purposes.A rehabilitation platform is built for the use of motor function rehabilitation training. Thework in this dissertation provides brain mechanisms related to specified tasks and applica-tion prototypes, which would be positive in brain research and development of practical BCIsystems.
引文
[1] T.W. Berger, J.K. Chapin, G.A. Gerhardt, D.J. McFarland, J.C. Principe, W.V. Soussou, D.M.Taylor, and P.A. Tresco. Brain-Computer Interfaces: An international assessment of research anddevelopment trends. Springer,2008.
[2] S.J.M. Smith. Eeg in the diagnosis, classification, and management of patients with epilepsy.Journal of Neurology, Neurosurgery&Psychiatry,76(suppl2):ii2–ii7,2005.
[3] W. Penfield and H. Jasper. Epilepsy and the functional anatomy of the human brain. Little, Brownand Company,1954.
[4] J. Claassen, L.J. Hirsch, K.T. Kreiter, E.Y. Du, E. Sander Connolly, R.G. Emerson, and S.A. Mayer.Quantitative continuous eeg for detecting delayed cerebral ischemia in patients with poor-gradesubarachnoid hemorrhage. Clinical neurophysiology,115(12):2699–2710,2004.
[5] D.R. Labar, B.J. Fisch, T.A. Pedley, M.E. Fink, and R.A. Solomon. Quantitative eeg monitoringfor patients with subarachnoid hemorrhage. Electroencephalography and clinical neurophysiology,78(5):325–332,1991.
[6] M. Penttila¨, J.V. Partanen, H. Soininen, and PJ Riekkinen. Quantitative analysis of occipital eegin different stages of alzheimer’s disease. Electroencephalography and clinical neurophysiology,60(1):1–6,1985.
[7] E. Niedermeyer and F.L. da Silva. Electroencephalography: basic principles, clinical applications,and related fields. Lippincott Williams&Wilkins,2004.
[8] P. Ciuciu, P. Abry, C. Rabrait, and H. Wendt. Log wavelet leaders cumulant based multifractalanalysis of evi fmri time series: evidence of scaling in ongoing and evoked brain activity. IEEEJournal of Selected Topics in Signal Processing,2(6):929–943,2008.
[9] M. Xu, H. Takata, S. Ge, T. Hayami, T. Yamasaki, S. Tobimatsu, and K. Iramina. Nirs measurementof hemodynamic evoked responses in the primary somatosensory cortex by finger stimulation. InComplex Medical Engineering,2007. CME2007. IEEE/ICME International Conference on, pages1425–1429. IEEE,2007.
[10] M. Corbetta, F.M. Miezin, G.L. Shulman, and S.E. Petersen. A pet study of visuospatial attention.The Journal of Neuroscience,13(3):1202–1226,1993.
[11] M. Ha¨ma¨la¨inen, R. Hari, R.J. Ilmoniemi, J. Knuutila, and O.V. Lounasmaa. Magnetoencephalogra-phy―theory, instrumentation, and applications to noninvasive studies of the working human brain.Reviews of modern Physics,65(2):413,1993.
[12] K.R. Mu¨ller and B. Blankertz. Toward non-invasive brain-computer interfaces. IEEE Signal Proc.Magazine,23(5):125–128,2006.
[13] J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, T.M. Vaughan, et al. Brain-computerinterfaces for communication and control. Clinical neurophysiology,113(6):767–791,2002.
[14] N. Xu, X. Gao, B. Hong, X. Miao, S. Gao, and F. Yang. BCI Competition2003―Data set IIb: en-hancing P300wave detection using ICA-based subspace projections for BCI applications. Biomed-ical Engineering, IEEE Transactions on,51(6):1067,2004.
[15] B. Blankertz, K.R. Mu¨ller, D.J. Krusienski, G. Schalk, J.R. Wolpaw, A. Schlo¨gl, G. Pfurtscheller,J.R. Milla′n, M. Schro¨der, and N. Birbaumer. The BCI competition III: Validating alternative ap-proaches to actual BCI problems. Neural Systems and Rehabilitation Engineering, IEEE Transac-tions on,14(2):153,2006.
[16] J.R. Wolpaw, DJ McFarland, and TM Vaughan. Brain-computer interface research at theWadsworth Center. Rehabilitation Engineering, IEEE Transactions on,8(2):222–226,2000.
[17] J.D. Bayliss. A flexible brain-computer interface. PhD thesis, University of Rochester,2001.
[18] D.J. McFarland, C.W. Anderson, K.R. Muller, A. Schlogl, and D.J. Krusienski. Bci meeting2005-workshop on bci signal processing: feature extraction and translation. Neural Systems and Reha-bilitation Engineering, IEEE Transactions on,14(2):135–138,2006.
[19] T.M. Vaughan, W.J. Heetderks, L.J. Trejo, W.Z. Rymer, M. Weinrich, M.M. Moore, A. Ku¨bler, B.H.Dobkin, N. Birbaumer, E. Donchin, et al. Brain-computer interface technology: a review of thesecond international meeting. Neural Systems and Rehabilitation Engineering, IEEE Transactionson,11(2):94,2003.
[20] B. Blankertz, K.R. Muller, G. Curio, T.M. Vaughan, G. Schalk, J.R. Wolpaw, A. Schlogl, C. Neuper,G. Pfurtscheller, T. Hinterberger, et al. The bci competition2003: progress and perspectives indetection and discrimination of eeg single trials. Biomedical Engineering, IEEE Transactions on,51(6):1044–1051,2004.
[21] M. Tangermann, K.R. Mu¨ller, A. Aertsen, N. Birbaumer, C. Braun, C. Brunner, R. Leeb, C.Mehring, K.J. Miller, G.R. Mu¨ller-Putz, et al. Review of the bci competition iv. Frontiers inNeuroscience,6,2012.
[22] Y. Wang, Z. Zhang, Y. Li, X. Gao, S. Gao, and F. Yang. BCI competition2003―data set IV:an algorithm based on CSSD and FDA for classifying single-trial EEG. Biomedical Engineering,IEEE Transactions on,51(6):1081,2004.
[23] J.R. Wolpaw, D.J. McFarland, T.M. Vaughan, and G. Schalk. The wadsworth center brain-computerinterface (bci) research and development program. Neural Systems and Rehabilitation Engineering,IEEE Transactions on,11(2):1–4,2003.
[24] J.R. Wolpaw, D.J. McFarland, G.W. Neat, and C.A. Forneris. An eeg-based brain-computer in-terface for cursor control. Electroencephalography and clinical neurophysiology,78(3):252–259,1991.
[25] D.J. McFarland, G.W. Neat, R.F. Read, and J.R. Wolpaw. An eeg-based method for graded cursorcontrol. Psychobiology; Psychobiology,1993.
[26] D.J. McFarland, W.A. Sarnacki, and J.R. Wolpaw. Electroencephalographic (eeg) control of three-dimensional movement. Journal of neural engineering,7(3):036007,2010.
[27] G. Schalk, D.J. McFarland, T. Hinterberger, N. Birbaumer, and J.R. Wolpaw. Bci2000: a general-purpose brain-computer interface (bci) system. Biomedical Engineering, IEEE Transactions on,51(6):1034–1043,2004.
[28] 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. Neural Systemsand Rehabilitation Engineering, IEEE Transactions on,14(2):229–233,2006.
[29] A. Ku¨bler, F. Nijboer, J. Mellinger, T.M. Vaughan, H. Pawelzik, G. Schalk, DJ McFarland, N.Birbaumer, and J.R. Wolpaw. Patients with als can use sensorimotor rhythms to operate a brain-computer interface. Neurology,64(10):1775–1777,2005.
[30] S.G. Mason and G.E. Birch. A general framework for brain-computer interface design. NeuralSystems and Rehabilitation Engineering, IEEE Transactions on,11(1):70–85,2003.
[31] H. Ramoser, J. Muller-Gerking, and G. Pfurtscheller. Optimal spatial filtering of single trial eegduring imagined hand movement. Rehabilitation Engineering, IEEE Transactions on,8(4):441–446,2000.
[32] G. Pfurtscheller and A. Aranibar. Evaluation of event-related desynchronization (erd) precedingand following voluntary self-paced movement. Electroencephalography and clinical neurophysi-ology,46(2):138–146,1979.
[33] G. Blanchard and B. Blankertz. Bci competition2003-data set iia: spatial patterns of self-controlledbrain rhythm modulations. Biomedical Engineering, IEEE Transactions on,51(6):1062–1066,2004.
[34] G.R. Mu¨ller-Putz, R. Scherer, G. Pfurtscheller, and R. Rupp. Eeg-based neuroprosthesis control: astep towards clinical practice. Neuroscience letters,382(1):169–174,2005.
[35] G. Pfurtscheller, G.R. Mu¨ller, J. Pfurtscheller, H.J. Gerner, and R. Rupp.‘thought’–control offunctional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neuroscienceletters,351(1):33–36,2003.
[36] R. Leeb, V. Settgast, D. Fellner, and G. Pfurtscheller. Self-paced exploration of the austrian nationallibrary through thought. International Journal of Bioelectromagnetism,9(4):237–244,2007.
[37] R. Scherer, A. Schloegl, F. Lee, H. Bischof, J. Jansˇa, and G. Pfurtscheller. The self-paced grazbrain-computer interface: methods and applications. Computational intelligence and neuroscience,2007,2007.
[38] R. Scherer, F. Lee, A. Schlogl, R. Leeb, H. Bischof, and G. Pfurtscheller. Toward self-paced brain–computer communication: navigation through virtual worlds. Biomedical Engineering, IEEETransactions on,55(2):675–682,2008.
[39] V.O. Alan and R.W. Schafer. Discrete-time signal processing. New Jersey, Printice Hall Inc,1989.
[40] S. Abdullah, CKE Nidzwan, and MZ Nuawi. A study of fatigue data editing using the short-timefourier transform (stft). American Journal of Applied Sciences,6(4):565–575,2009.
[41] J. Lafferty, A. McCallum, and F.C.N. Pereira. Conditional random fields: Probabilistic models forsegmenting and labeling sequence data.2001.
[42] H.M. Wallach. Conditional random fields: An introduction. Technical Reports (CIS), page22,2004.
[43] H. Wallach. Efficient training of conditional random fields. PhD thesis, Citeseer,2002.
[44] P. Clifford. Markov random fields in statistics. Disorder in physical systems, pages19–32,1990.
[45] D.P. Bertsekas. Nonlinear programming. Athena Scientific,1999.
[46] F. Sha and F. Pereira. Shallow parsing with conditional random fields. In Proceedings of the2003Conference of the North American Chapter of the Association for Computational Linguistics onHuman Language Technology-Volume1, pages134–141. Association for Computational Linguis-tics,2003.
[47] L. Getoor and B. Taskar. Introduction to statistical relational learning. MIT press,2007.
[48] N. Cristianini and J. Shawe-Taylor. An introduction to support vector machines and other kernel-based learning methods. Cambridge Univ Pr,2000.
[49] P.N. Tan, M. Steinbach, and V. Kumar. Introduction to data mining. Pearson Addison WesleyBoston,2006.
[50] A.C.K. Soong and Z.J. Koles. Principal-component localization of the sources of the backgroundeeg. Biomedical Engineering, IEEE Transactions on,42(1):59–67,1995.
[51] V. Vapnik. The nature of statistical learning theory. Springer-Verlag, New York,1996.
[52] M.A. Hearst, ST Dumais, E. Osman, J. Platt, and B. Scholkopf. Support vector machines. Intelli-gent Systems and their Applications, IEEE,13(4):18–28,1998.
[53] Keinosuke Fukunaga. Introduction to statistical pattern recognition. Academic Pr,1990.
[54] C. Neuper and G. Pfurtscheller. Neurofeedback training for BCI control. Springer,2010.
[55] G. Pfurtscheller, D. Flotzinger, and J. Kalcher. Brain-computer interface―a new communicationdevice for handicapped persons. Journal of Microcomputer Applications,16(3):293–299,1993.
[56] D.J. McFarland, D.J. Krusienski, W.A. Sarnacki, and J.R. Wolpaw. Emulation of computer mousecontrol with a noninvasive brain–computer interface. Journal of neural engineering,5(2):101,2008.
[57] Q.B. Zhao, L.Q. Zhang, and A. Cichocki. EEG-based asynchronous BCI control of a car in3Dvirtual reality environments. Chinese Science Bulletin,54(1):78–87,2009.
[58] M.A. Lebedev, M.A.L. Nicolelis, et al. Brain machine interfaces: past, present and future. TRENDSin Neurosciences,29(9):536–546,2006.
[59] N.G. Hatsopoulos and J.P. Donoghue. The science of neural interface systems. Annual review ofneuroscience,32:249,2009.
[60] M.A.L. Nicolelis et al. Actions from thoughts. Nature,409(6818):403–408,2001.
[61] D.M. Taylor, S.I.H. Tillery, and A.B. Schwartz. Direct cortical control of3d neuroprosthetic de-vices. Science,296(5574):1829–1832,2002.
[62] G. Santhanam, S.I. Ryu, M.Y. Byron, A. Afshar, and K.V. Shenoy. A high-performance brain–computer interface. Nature,442(7099):195–198,2006.
[63] L.R. Hochberg, M.D. Serruya, G.M. Friehs, J.A. Mukand, M. Saleh, A.H. Caplan, A. Branner, D.Chen, R.D. Penn, and J.P. Donoghue. Neuronal ensemble control of prosthetic devices by a humanwith tetraplegia. Nature,442(7099):164–171,2006.
[64] G. Schalk, J. Kubanek, KJ Miller, NR Anderson, EC Leuthardt, JG Ojemann, D. Limbrick, D.Moran, LA Gerhardt, and JR Wolpaw. Decoding two-dimensional movement trajectories usingelectrocorticographic signals in humans. Journal of neural engineering,4(3):264,2007.
[65] T. Pistohl, T. Ball, A. Schulze-Bonhage, A. Aertsen, and C. Mehring. Prediction of arm movementtrajectories from ecog-recordings in humans. Journal of neuroscience methods,167(1):105–114,2008.
[66] M. Velliste, S. Perel, M.C. Spalding, A.S. Whitford, and A.B. Schwartz. Cortical control of aprosthetic arm for self-feeding. Nature,453(7198):1098–1101,2008.
[67] R.A. Andersen, L.H. Snyder, D.C. Bradley, and J. Xing. Multimodal representation of space inthe posterior parietal cortex and its use in planning movements. Annual review of neuroscience,20(1):303–330,1997.
[68] J.C. Culham, C. Cavina-Pratesi, and A. Singhal. The role of parietal cortex in visuomotor control:What have we learned from neuroimaging? Neuropsychologia,44(13):2668–2684,2006.
[69] M. Desmurget, CM Epstein, RS Turner, C. Prablanc, GE Alexander, ST Grafton, et al. Role of theposterior parietal cortex in updating reaching movements to a visual target. Nature neuroscience,2:563–567,1999.
[70] H. Scherberger, M.R. Jarvis, and R.A. Andersen. Cortical local field potential encodes movementintentions in the posterior parietal cortex. Neuron,46(2):347–354,2005.
[71] R.Q. Quiroga, L.H. Snyder, A.P. Batista, H. Cui, and R.A. Andersen. Movement intention is betterpredicted than attention in the posterior parietal cortex. The Journal of neuroscience,26(13):3615–3620,2006.
[72] G.H. Mulliken, S. Musallam, and R.A. Andersen. Decoding trajectories from posterior parietalcortex ensembles. the Journal of Neuroscience,28(48):12913–12926,2008.
[73] R.A. Andersen and C.A. Buneo. Intentional maps in posterior parietal cortex. Annual review ofneuroscience,25(1):189–220,2002.
[74] N. Birbaumer. Breaking the silence: brain–computer interfaces (bci) for communication and motorcontrol. Psychophysiology,43(6):517–532,2006.
[75] P.S. Hammon, S. Makeig, H. Poizner, E. Todorov, and V.R. de Sa. Predicting reaching targets fromhuman eeg. Signal Processing Magazine, IEEE,25(1):69–77,2008.
[76] S. Waldert, H. Preissl, E. Demandt, C. Braun, N. Birbaumer, A. Aertsen, and C. Mehring. Handmovement direction decoded from meg and eeg. The Journal of neuroscience,28(4):1000–1008,2008.
[77] T.J. Bradberry, F. Rong, and J.L. Contreras-Vidal. Decoding center-out hand velocity from megsignals during visuomotor adaptation. NeuroImage,47(4):1691–1700,2009.
[78] T.J. Bradberry, R.J. Gentili, and J.L. Contreras-Vidal. Reconstructing three-dimensional handmovements from noninvasive electroencephalographic signals. The Journal of Neuroscience,30(9):3432–3437,2010.
[79] W. Wang, G.P. Sudre, Y. Xu, R.E. Kass, J.L. Collinger, A.D. Degenhart, A.I. Bagic, and D.J. Weber.Decoding and cortical source localization for intended movement direction with meg. Journal ofneurophysiology,104(5):2451–2461,2010.
[80] R.H.J. van der Lubbe, B. Wauschkuhn, E. Wascher, T. Niehoff, D. Ko¨mpf, and R. Verleger. Lat-eralized eeg components with direction information for the preparation of saccades versus fingermovements. Experimental Brain Research,132(2):163–178,2000.
[81] Y. Wang and S. Makeig. Predicting intended movement direction using eeg from human posteriorparietal cortex. Foundations of Augmented Cognition. Neuroergonomics and Operational Neuro-science, pages437–446,2009.
[82] J.W. Bisley and M.E. Goldberg. Attention, intention, and priority in the parietal lobe. Annualreview of neuroscience,33:1–21,2010.
[83] M.S. Worden, J.J. Foxe, N. Wang, and G.V. Simpson. Anticipatory biasing of visuospatial attentionindexed by retinotopically specific-band electroencephalography increases over occipital cortex. JNeurosci,20:1–6,2000.
[84] G. Thut, A. Nietzel, S.A. Brandt, and A. Pascual-Leone. α-band electroencephalographic activityover occipital cortex indexes visuospatial attention bias and predicts visual target detection. TheJournal of neuroscience,26(37):9494–9502,2006.
[85] A. Bahramisharif, M. Van Gerven, T. Heskes, and O. Jensen. Covert attention allows for continuouscontrol of brain–computer interfaces. European Journal of Neuroscience,31(8):1501–1508,2010.
[86] M.S. Treder, A. Bahramisharif, N.M. Schmidt, M.A.J. van Gerven, and B. Blankertz. Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention.Journal of neuroengineering and rehabilitation,8(1):24,2011.
[87] S. Waldert, T. Pistohl, C. Braun, T. Ball, A. Aertsen, and C. Mehring. A review on directional infor-mation in neural signals for brain-machine interfaces. Journal of Physiology-Elsevier,103(3):244,2009.
[88] R.O. Duda, P.E. Hart, and D.G. Stork. Pattern Classification. Wiley interscience,2001.
[89] D.J. McFarland, L.M. McCane, S.V. David, and J.R. Wolpaw. Spatial filter selection for eeg-basedcommunication. Electroencephalography and clinical Neurophysiology,103(3):386–394,1997.
[90] T.P. Jung, S. Makeig, C. Humphries, T.W. Lee, M.J. Mckeown, V. Iragui, and T.J. Sejnowski.Removing electroencephalographic artifacts by blind source separation. Psychophysiology,37(2):163–178,2000.
[91] A. Delorme and S. Makeig. Eeglab: an open source toolbox for analysis of single-trial eeg dynam-ics including independent component analysis. Journal of neuroscience methods,134(1):9–21,2004.
[92] O. Friman, J. Cedefamn, P. Lundberg, M. Borga, and H. Knutsson. Detection of neural activity infunctional mri using canonical correlation analysis. Magnetic Resonance in Medicine,45(2):323–330,2001.
[93] H. Hotelling. Relations between two sets of variates. Biometrika,28(3/4):321–377,1936.
[94] T.K. Kim and R. Cipolla. Canonical correlation analysis of video volume tensors for action cat-egorization and detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on,31(8):1415–1428,2009.
[95] C.C. Chang and C.J. Lin. Libsvm: a library for support vector machines. ACM Transactions onIntelligent Systems and Technology (TIST),2(3):27,2011.
[96] K.S. Baker, T. Piriyapunyaporn, and R. Cunnington. Neural activity in readiness for incidental andexplicitly timed actions. Neuropsychologia,2012.
[97] J.J. Foxe and A.C. Snyder. The role of alpha-band brain oscillations as a sensory suppressionmechanism during selective attention. Frontiers in psychology,2,2011.
[98] M. Siegel, T.H. Donner, and A.K. Engel. Spectral fingerprints of large-scale neuronal interactions.Nature Reviews Neuroscience,2012.
[99] S.J. Luck and S.A. Hillyard. Electrophysiological correlates of feature analysis during visualsearch. Psychophysiology,31(3):291–308,1994.
[100] H.H. Kornhuber and L. Deecke. Hirnpotentiala¨nderungen bei willku¨rbewegungen und passivenbewegungen des menschen: Bereitschaftspotential und reafferente potentiale. Pflu¨gers Archiv Eu-ropean Journal of Physiology,284(1):1–17,1965.
[101] H. Shibasaki and M. Hallett. What is the bereitschaftspotential? Clinical Neurophysiology,117(11):2341–2356,2006.
[102] JR Naranjo, A. Brovelli, R. Longo, R. Budai, R. Kristeva, and PP Battaglini. Eeg dynamics ofthe frontoparietal network during reaching preparation in humans. Neuroimage,34(4):1673–1682,2007.
[103] M. Corbetta, G.L. Shulman, et al. Control of goal-directed and stimulus-driven attention in thebrain. Nature reviews neuroscience,3(3):215–229,2002.
[104] G. Dornhege, B. Blankertz, G. Curio, and K.R. Muller. Boosting bit rates in noninvasive eeg single-trial classifications by feature combination and multiclass paradigms. Biomedical Engineering,IEEE Transactions on,51(6):993–1002,2004.
[105] O. David, L.M. Harrison, K.J. Friston, et al. Modelling event-related responses in the brain. Neu-roImage,25(3):756–70,2005.
[106] J.W. Bisley and M.E. Goldberg. Neuronal activity in the lateral intraparietal area and spatial atten-tion. Science,299(5603):81–86,2003.
[107] J.D. Connolly, R.A. Andersen, and M.A. Goodale. Fmri evidence for a’parietal reach region’in thehuman brain. Experimental Brain Research,153(2):140–145,2003.
[108] I. Levy, D. Schluppeck, D.J. Heeger, and P.W. Glimcher. Specificity of human cortical areas forreaches and saccades. The Journal of neuroscience,27(17):4687–4696,2007.
[109] A.P. Georgopoulos, J.F. Kalaska, R. Caminiti, and J.T. Massey. On the relations between thedirection of two-dimensional arm movements and cell discharge in primate motor cortex. TheJournal of Neuroscience,2(11):1527–1537,1982.
[110] D.W. Moran and A.B. Schwartz. Motor cortical activity during drawing movements: populationrepresentation during spiral tracing. Journal of neurophysiology,82(5):2693–2704,1999.
[111] J. Rickert, S.C. de Oliveira, E. Vaadia, A. Aertsen, S. Rotter, and C. Mehring. Encoding of move-ment direction in different frequency ranges of motor cortical local field potentials. The Journal ofneuroscience,25(39):8815–8824,2005.
[112] W. Truccolo, G.M. Friehs, J.P. Donoghue, and L.R. Hochberg. Primary motor cortex tuningto intended movement kinematics in humans with tetraplegia. The Journal of Neuroscience,28(5):1163–1178,2008.
[113] M. Eisenberg, L. Shmuelof, E. Vaadia, and E. Zohary. Functional organization of human motorcortex: directional selectivity for movement. The Journal of Neuroscience,30(26):8897–8905,2010.
[114] F. Lacquaniti, E. Guigon, L. Bianchi, S. Ferraina, and R. Caminiti. Representing spatial informationfor limb movement: role of area5in the monkey. Cerebral Cortex,5(5):391–409,1995.
[115] A. Fanini and J.A. Assad. Direction selectivity of neurons in the macaque lateral intraparietal area.Journal of neurophysiology,101(1):289–305,2009.
[116] S. Fabbri, A. Caramazza, and A. Lingnau. Tuning curves for movement direction in the humanvisuomotor system. The Journal of Neuroscience,30(40):13488–13498,2010.
[117] G. Pfurtscheller, C. Brunner, A. Schlo¨gl, S.F.H. Lopes, et al. Mu rhythm (de) synchronization andeeg single-trial classification of different motor imagery tasks. NeuroImage,31(1):153,2006.
[118] C. Guger, G. Edlinger, W. Harkam, I. Niedermayer, and G. Pfurtscheller. How many people areable to operate an eeg-based brain-computer interface (bci)? Neural Systems and RehabilitationEngineering, IEEE Transactions on,11(2):145–147,2003.
[119] C. Brunner, R. Scherer, B. Graimann, G. Supp, and G. Pfurtscheller. Online control of a brain-computer interface using phase synchronization. Biomedical Engineering, IEEE Transactions on,53(12):2501–2506,2006.
[120] J.M. Schoffelen and J. Gross. Source connectivity analysis with meg and eeg. Human brainmapping,30(6):1857–1865,2009.
[121] J.L. Calton, A.R. Dickinson, L.H. Snyder, et al. Non-spatial, motor-specific activation in posteriorparietal cortex. Nature neuroscience,5(6):580–588,2002.
[122] J. Van Der Werf, O. Jensen, P. Fries, and W.P. Medendorp. Neuronal synchronization in humanposterior parietal cortex during reach planning. The Journal of Neuroscience,30(4):1402–1412,2010.
[123] Y. Wang, Y.T. Wang, and T.P. Jung. Translation of eeg spatial filters from resting to motor imageryusing independent component analysis. PloS one,7(5):e37665,2012.
[124] J. Li and L. Zhang. Bilateral adaptation and neurofeedback for brain computer interface system.Journal of neuroscience methods,193(2):373–379,2010.
[125] H. Cui and R.A. Andersen. Posterior parietal cortex encodes autonomously selected motor plans.Neuron,56(3):552–559,2007.
[126] B. Hjorth. Eeg analysis based on time domain properties. Electroencephalography and clinicalneurophysiology,29(3):306–310,1970.
[127] D. Lloyd-Jones, R.J. Adams, T.M. Brown, M. Carnethon, S. Dai, G. De Simone, T.B. Ferguson, E.Ford, K. Furie, C. Gillespie, et al. Heart disease and stroke statistics―2010update. Circulation,121(7):e46–e215,2010.