Advertisement

Journal of Zhejiang University SCIENCE B

, Volume 11, Issue 4, pp 298–306 | Cite as

Neural decoding based on probabilistic neural network

  • Yi Yu
  • Shao-min Zhang
  • Huai-jian Zhang
  • Xiao-chun Liu
  • Qiao-sheng Zhang
  • Xiao-xiang Zheng
  • Jian-hua Dai
Article

Abstract

Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices, such as robot arms, computer cursors, and paralyzed muscles. A variety of neural decoding algorithms have been designed to explore relationships between neural activities and movements of the limbs. In this paper, two novel neural decoding methods based on probabilistic neural network (PNN) in rats were introduced, the PNN decoder and the modified PNN (MPNN) decoder. In the experiment, rats were trained to obtain water by pressing a lever over a pressure threshold. Microelectrode array was implanted in the motor cortex to record neural activity, and pressure was recorded by a pressure sensor synchronously. After training, the pressure values were estimated from the neural signals by PNN and MPNN decoders. Their performances were evaluated by a correlation coefficient (CC) and a mean square error (MSE). The results show that the MPNN decoder, with a CC of 0.8657 and an MSE of 0.2563, outperformed the traditionally-used Wiener filter (WF) and Kalman filter (KF) decoders. It was also observed that the discretization level did not affect the MPNN performance, indicating that the MPNN decoder can handle different tasks in BMI system, including the detection of movement states and estimation of continuous kinematic parameters.

Key words

Brain-machine interfaces (BMI) Neural decoding Probabilistic neural network (PNN) Microelectrode array 

CLC number

TP2 R741 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Brockwell, A.E., Rojas, A.L., Kass, R.E., 2004. Recursive bayesian decoding of motor cortical signals by particle filtering. J. Neurophysiol., 91(4):1899–1907. [doi:10.1152/jn.00438.2003]CrossRefPubMedGoogle Scholar
  2. Brown, E.N., Frank, L.M, Tang, D., Quirk, M.C., Wilson, M.A., 1998. A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells. J. Neurosci., 18(18):7411–7425.PubMedGoogle Scholar
  3. Carmena, J.M., Lebedev, M.A., Crist, R.E., O’Doherty, J.E., Santucci, D.M., 2003. Learning to control a brain- machine interface for reaching and grasping by primates. PLoS. Biol., 1(2):E42. [doi:10.1371/journal.pbio.0000042]CrossRefPubMedGoogle Scholar
  4. Eden, U.T., Frank, L.M., Barbieri, R., Solo, V., Brown, E.N., 2004. Dynamic analysis of neural encoding by point process adaptive filtering. Neural Comput., 16(5):971–998. [doi:10.1162/089976604773135069]CrossRefPubMedGoogle Scholar
  5. Feng, Z.Y., Chen, W.D., Ye, X.S., 2007. A remote control training system for rat navigation in complicated environment. J. Zhejiang Univ.-Sci. A, 8(2):323–330. [doi:10.1631/jzus.2007.A0323]CrossRefGoogle Scholar
  6. Gage, G.J., Ludwig, K.A., Otto, K.J., Ionides, E.L., Kipke, D.R., 2005. Naive coadaptive cortical control. J. Neural Eng., 2(2):52–63. [doi:10.1088/1741-2560/2/2/006]CrossRefPubMedGoogle Scholar
  7. Georgopoulos, A.P., Kettner, R.E., Schwartz, A.B., 1988. Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population. J. Neurosci., 8(8):2928–2937.PubMedGoogle Scholar
  8. Georgopoulos, A.P., Lurito, J.T., Petrides, M., 1989. Mental rotation of the neuronal population vector. Science, 243(4888):234–236. [doi:10.1126/science.2911737]CrossRefPubMedGoogle Scholar
  9. Hatsopoulos, N., Joshi, J., O’Leary, J.G., 2004. Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles. J. Neurophysiol., 92(2): 1165–1174. [doi:10.1152/jn.01245.2003]CrossRefPubMedGoogle Scholar
  10. Hochberg, L.R., Serruya, M.D., Friehs, G.M., Mukand, J.A., Saleh, M., 2006. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099): 164–171. [doi:10.1038/nature04970]CrossRefPubMedGoogle Scholar
  11. Lebedev, M.A., Nicolelis, M.A., 2006. Brain-machine interfaces: past, present and future. Trends Neurosci., 29(9): 536–546. [doi:10.1016/j.tins.2006.07.004]CrossRefPubMedGoogle Scholar
  12. Lebedev, M.A., Carmena, J.M., O’Doherty, J.E., 2005. CCortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. J. Neurosci., 25(19):4681–4693. [doi:10.1523/JNEUROSCI.4088-04.2005]CrossRefPubMedGoogle Scholar
  13. Ministry of Health of the People’s Republic of China, 2000. Guide for Animal Experiment Technology. Ministry of Health of the People’s Republic of China, p.9–20.Google Scholar
  14. Nicolelis, M.A., 2003. Brain-machine interfaces to restore motor function and probe neural circuits. Nature Rev. Neurosci., 4(5):417–422. [doi:10.1038/nrn1105]CrossRefGoogle Scholar
  15. Paxinos, G., Watson, C.R., Emson, P.C., 1980. AChE-stained horizontal sections of the rat brain in stereotaxic coordinates. J. Neurosci. Methods, 3(2):129–149. [doi:10.1016/0165-0270(80)90021-7]CrossRefPubMedGoogle Scholar
  16. Schmidt, E.M., McIntosh, J.S., Durelli, L., 1978. Fine control of operantly conditioned firing patterns of cortical neurons. Exp. Neurol., 61(2):349–369. [doi:10.1016/0014-4886(78)90252-2]CrossRefPubMedGoogle Scholar
  17. Serruya, M.D., Hatsopoulos, N.G., Paninski, L., Fellows, M.R., Donoghue, J.P., 2002. Instant neural control of a movement signal. Nature, 416(6877):141–142. [doi:10.1038/416141a]CrossRefPubMedGoogle Scholar
  18. Shoham, S., Paninski, L.M., Fellows, M.R., Hatsopoulos, N.G., Donoghue, J.P., 2005. Statistical encoding model for a primary motor cortical brain-machine interface. IEEE Trans. Biomed. Eng., 52(7):1312–1322. [doi:10.1109/TBME.2005.847542]CrossRefPubMedGoogle Scholar
  19. Smith, A.C., Brown, E.N., 2003. Estimating a state-space model from point process observations. Neural Comput., 15(5):965–991. [doi:10.1162/089976603765202622]CrossRefPubMedGoogle Scholar
  20. Specht, D.F., 1990. Probabilistic neural networks. Neural Netw., 3(1):109–118. [doi:10.1016/0893-6080(90)90049-Q]CrossRefGoogle Scholar
  21. Taylor, D.M., Tillery, S.I., Schwartz, A.B., 2002. Direct cortical control of 3D neuroprosthetic devices. Science, 296(5574):1829–1832. [doi:10.1126/science.1070291]CrossRefPubMedGoogle Scholar
  22. Velliste, M., Perel, S., Spalding, M.C., 2008. Cortical control of a prosthetic arm for self-feeding. Nature, 453(7198): 1098–1101. [doi:10.1038/nature06996]CrossRefPubMedGoogle Scholar
  23. Wang, Y., Paiva, A.R.C., Príncipe, J.C., Sanchez, J.C., 2009. Sequential monte carlo point-process estimation of kinematics from neural spiking activity for brain-machine interfaces. Neural Comput., 21(10):2894–2930. [doi:10.1162/neco.2009.01-08-699]CrossRefPubMedGoogle Scholar
  24. Wessberg, J., Stambaugh, C.R., Kralik, J.D., Beck, P.D., Laubach, M., Chapin, J.K., Kim, J., Biggs, S.J., Srinivasan, M.A., Nicolelis, M.A.L., 2000. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature, 408(6810):361–365. [doi:10.1038/35042582]CrossRefPubMedGoogle Scholar
  25. Wu, W., Black, M.J., Gao, Y., 2002. Inferring Hand Motion from Multi-Cell Recordings in Motor Cortex Using a Kalman Filter. Workshop on Motor Control in Humans and Robots: On the Interplay of Real Brains and Artificial Devices. Edinburgh, Scotland (UK), p.66–73.Google Scholar
  26. Wu, W., Black, M.J., Gao, Y., Bienenstock, E., Serruya, M., 2003. Neural Deocoding of Cursor Motion Using a Kalman Filter. Advances in Neural Information Processing Systems 15. Cambridge, MIT Press, MA, p.1–8.Google Scholar
  27. Wu, W., Black, M.J., Mumford, D., Gao, Y., Bienenstock, E., 2004. Modeling and decoding motor cortical activity using a switching Kalman filter. IEEE Trans. Biomed. Eng., 51(6):933–942. [doi:10.1109/TBME.2004.826666]CrossRefPubMedGoogle Scholar
  28. Wu, W., Gao, Y., Bienenstock, E., Donoghue, J.P., Black, M.J., 2006. Bayesian population decoding of motor cortical activity using a Kalman filter. Neural Comput., 18(1): 80–118. [doi:10.1162/089976606774841585]CrossRefPubMedGoogle Scholar
  29. Ye, X.S., Wang, P., Liu, J., 2008. A portable telemetry system for brain stimulation and neuronal activity recording in freely behaving small animals. J. Neurosci. Methods, 174(2):186–193. [doi:10.1016/j.jneumeth.2008.07.002]CrossRefPubMedGoogle Scholar
  30. Zhang, K., Ginzburg, I., McNaughton, B.L., 1998. Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. J. Neurophysiol., 79(2):1017–1044.PubMedGoogle Scholar

Copyright information

© Zhejiang University and Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yi Yu
    • 1
    • 2
    • 3
  • Shao-min Zhang
    • 1
    • 2
    • 3
  • Huai-jian Zhang
    • 1
    • 2
    • 3
  • Xiao-chun Liu
    • 1
    • 2
    • 4
  • Qiao-sheng Zhang
    • 1
    • 2
    • 3
  • Xiao-xiang Zheng
    • 1
    • 2
    • 3
  • Jian-hua Dai
    • 1
    • 2
    • 4
  1. 1.Qiushi Academy for Advanced StudiesZhejiang UniversityHangzhouChina
  2. 2.College of Biomedical Engineering and Instrument ScienceZhejiang UniversityHangzhouChina
  3. 3.Key Laboratory of Biomedical Engineering of Ministry of EducationZhejiang UniversityHangzhouChina
  4. 4.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina

Personalised recommendations