Yıl 2018,
Cilt: 3 Sayı: 2, 23 - 27, 31.12.2018
Murat Okatan
Mehmet Kocatürk
Kaynakça
- [1] M. E. J. Obien, K. Deligkaris, T. Bullmann, D. J. Bakkum, U. Frey, "Revealing neuronal function through microelectrode array recordings", Front. Neurosci. Vol.9, 2015, p.423.
- [2] B. Amirikian and A. P. Georgopulos, “Directional tuning profiles of motor cortical cells”, Neuroscience Research, Vol.36, 2000, pp.73–79.
- [3] E. C. Leuthardt, G. Schalk, D. Moran, J. G. Ojemann, “The emerging world of motor neuroprosthetics: a neurosurgical perspective”, Neurosurgery, Vol.59, No.1, 2006, pp.1-14.
- [4] M. S. Lewicki, "A review of methods for spike sorting: the detection and classification of neural action potentials", Network: Comput. Neural Syst., Vol.9, 1998, pp.R53-R78.
- [5] R. Q. Quiroga et al. "Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering", Neural Comput., Vol.16, 2004, pp.1661-1687.
- [6] C. Vargas-Irwin and J. P. Donoghue, "Automated spike sorting using density grid contour clustering and subtractive waveform decomposition", J Neurosci Methods, Vol.164, 2007, pp.1-18.
- [7] S. Todorova et al., "To sort or not to sort: the impact of spike-sorting on neural decoding performance", J Neural Eng., Vol.11, 2014, p.056005.
- [8] E. R. Oby et al., “Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters”, J Neural Eng., Vol.13, 2016, p.036009.
- [9] M. Okatan and M. Kocatürk, "Truncation thresholds: a pair of spike detection thresholds computed using truncated probability distributions", Turk J Elec Eng & Comp Sci., Vol.25, 2017, pp.1436-1447.
- [10] M. Okatan, "A comparative study on the estimation of noise standard deviation using DATE and Truncation Thresholds", presented at the Signal Processing and Communications Applications Conference (SIU), 26th. pp: 1-4, 2018, (in Turkish with English abstract), DOI: 10.1109/SIU.2018.8404155.
- [11] M. Okatan, "Comparison of Truncation Thresholds with four different robust scale estimators", presented at the Signal Processing and Communications Applications Conference (SIU), 26th. pp: 1-4, 2018, (in Turkish with English abstract), DOI: 10.1109/SIU.2018.8404387.
- [12] M. Okatan and M Kocatürk, “High performance decoding of behavioral information from background activity in extracellular neural recordings” (Turkish), presented at the 2018 Medical Technologies National Conference (TIPTEKNO). pp:1-4, DOI: 10.1109/TIPTEKNO.2018.8597114.
- [13] M. Kocaturk, H. O. Gulcur, R. Canbeyli, “Toward building hybrid biological/in silico neural networks for motor neuroprosthetic control”, Frontiers in Neurorobotics 2015; Vol.9, 2015, p.8.
- [14] M. Okatan, “Truncation thresholds: a new method for separating signal and noise and its application in biomedical signal processing”, (In Turkish). presented at the EEMKON 2015. Biomedical Engineering Symposium, Istanbul, Proc. ISBN: 978-605-01-0922-1, 2016.
- [15] W. H. Press, B. B. Flannery, S. A. Teukolsky, W. T. Vetterling, Numerical Recipes in C: The Art of Scientific Computing. 2nd ed. Cambridge: Cambridge University Press, 1992.
- [16] P. Eusebi, "Diagnostic accuracy measures", Cerebrovascular Diseases Vol.36, 2013, pp.267-72.
HIGH PERFORMANCE DECODING OF BEHAVIORAL INFORMATION FROM MEAN BACKGROUND ACTIVITY IN EXTRACELLULAR NEURAL RECORDINGS
Yıl 2018,
Cilt: 3 Sayı: 2, 23 - 27, 31.12.2018
Murat Okatan
Mehmet Kocatürk
Öz
We have previously shown that the standard deviation
of background activity in bandpass filtered extracellular neural recording
snippets is strongly modulated by behavior such that it can be used to decode
behavioral variables with up to 100% accuracy. Here we show that the mean
background activity is also strongly modulated by behavior and that it too can
be used to decode behavioral variables with up to 100% accuracy. To the best of
our knowledge, our method extracts the weakest signal that has ever been
extracted from extracellular neural recordings, which can still be used to decode
a behavioral variable with very high accuracy. Our results demonstrate that both
the standard deviation and the mean of the background activity can be exploited
in brain-machine interfaces.
Kaynakça
- [1] M. E. J. Obien, K. Deligkaris, T. Bullmann, D. J. Bakkum, U. Frey, "Revealing neuronal function through microelectrode array recordings", Front. Neurosci. Vol.9, 2015, p.423.
- [2] B. Amirikian and A. P. Georgopulos, “Directional tuning profiles of motor cortical cells”, Neuroscience Research, Vol.36, 2000, pp.73–79.
- [3] E. C. Leuthardt, G. Schalk, D. Moran, J. G. Ojemann, “The emerging world of motor neuroprosthetics: a neurosurgical perspective”, Neurosurgery, Vol.59, No.1, 2006, pp.1-14.
- [4] M. S. Lewicki, "A review of methods for spike sorting: the detection and classification of neural action potentials", Network: Comput. Neural Syst., Vol.9, 1998, pp.R53-R78.
- [5] R. Q. Quiroga et al. "Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering", Neural Comput., Vol.16, 2004, pp.1661-1687.
- [6] C. Vargas-Irwin and J. P. Donoghue, "Automated spike sorting using density grid contour clustering and subtractive waveform decomposition", J Neurosci Methods, Vol.164, 2007, pp.1-18.
- [7] S. Todorova et al., "To sort or not to sort: the impact of spike-sorting on neural decoding performance", J Neural Eng., Vol.11, 2014, p.056005.
- [8] E. R. Oby et al., “Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters”, J Neural Eng., Vol.13, 2016, p.036009.
- [9] M. Okatan and M. Kocatürk, "Truncation thresholds: a pair of spike detection thresholds computed using truncated probability distributions", Turk J Elec Eng & Comp Sci., Vol.25, 2017, pp.1436-1447.
- [10] M. Okatan, "A comparative study on the estimation of noise standard deviation using DATE and Truncation Thresholds", presented at the Signal Processing and Communications Applications Conference (SIU), 26th. pp: 1-4, 2018, (in Turkish with English abstract), DOI: 10.1109/SIU.2018.8404155.
- [11] M. Okatan, "Comparison of Truncation Thresholds with four different robust scale estimators", presented at the Signal Processing and Communications Applications Conference (SIU), 26th. pp: 1-4, 2018, (in Turkish with English abstract), DOI: 10.1109/SIU.2018.8404387.
- [12] M. Okatan and M Kocatürk, “High performance decoding of behavioral information from background activity in extracellular neural recordings” (Turkish), presented at the 2018 Medical Technologies National Conference (TIPTEKNO). pp:1-4, DOI: 10.1109/TIPTEKNO.2018.8597114.
- [13] M. Kocaturk, H. O. Gulcur, R. Canbeyli, “Toward building hybrid biological/in silico neural networks for motor neuroprosthetic control”, Frontiers in Neurorobotics 2015; Vol.9, 2015, p.8.
- [14] M. Okatan, “Truncation thresholds: a new method for separating signal and noise and its application in biomedical signal processing”, (In Turkish). presented at the EEMKON 2015. Biomedical Engineering Symposium, Istanbul, Proc. ISBN: 978-605-01-0922-1, 2016.
- [15] W. H. Press, B. B. Flannery, S. A. Teukolsky, W. T. Vetterling, Numerical Recipes in C: The Art of Scientific Computing. 2nd ed. Cambridge: Cambridge University Press, 1992.
- [16] P. Eusebi, "Diagnostic accuracy measures", Cerebrovascular Diseases Vol.36, 2013, pp.267-72.