Real-time estimation and biofeedback of single-neuron firing rates using local field potentials
Open accessAbstract
The long-term stability and low-frequency composition of local field potentials (LFPs) offer important advantages for robust and efficient neuroprostheses. However, cortical LFPs recorded by multi-electrode arrays are often assumed to contain only redundant information arising from the activity of large neuronal populations. Here we show that multichannel LFPs in monkey motor cortex each contain a slightly different mixture of distinctive slow potentials that accompany neuronal firing. As a result, the firing rates of individual neurons can be estimated with surprising accuracy. We implemented this method in a real-time biofeedback brain–machine interface, and found that monkeys could learn to modulate the activity of arbitrary neurons using feedback derived solely from LFPs. These findings provide a principled method for monitoring individual neurons without long-term recording of action potentials.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The mismatch between tremor and voluntary motion amplitudes can push adaptation towards the dominant components of voluntary movement rather than tremor.
“This mismatch can push the adaptation towards the dominant components of the voluntary movement rather than that of the tremor [19].”
From A Quaternion Weighted Fourier Linear Combiner for Modeling Physiological Tremor (2016) · cites this paper as [19]
The Results section on generalisation across BMI tasks discusses wrist movement and LFP-based firing-rate estimates, not tremor or adaptation driven by movement amplitude. Searches for ‘tremor’ and for voluntary-movement amplitude and adaptation found no passage addressing the claim. Page numbers were not provided in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe amplitude of typical physiological tremor is approximately 50 times smaller than that of voluntary motion.
“The amplitude of typical physiological tremor is however approximately 50 times smaller than that of the voluntary motion.”
From A Quaternion Weighted Fourier Linear Combiner for Modeling Physiological Tremor (2016) · cites this paper as [19]
The Methods section describes a wrist torque-controlled task, and the Results discuss wrist movement and neural signals, but neither gives a physiological-tremor amplitude. Searches for “physiological tremor,” “tremor,” and the claimed 50-fold comparison found no passage in our copy that supports the comparison with voluntary motion. Page numbers were not available. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes
Buzsáki, G., Anastassiou, C. A., & Koch, C. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3241 · Cited by 4,225 · see citing works
Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential
Leski, S., Lindén, H., Tetzlaff, T., Pettersen, K. H., & Einevoll, G. T. (2013) · PubMed Central · doi:10.18154/rwth-conv-084442 · Cited by 3,662 · see citing works
Neuronal ensemble control of prosthetic devices by a human with tetraplegia
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., Branner, A., Chen, D., Penn, R. D., & Donoghue, J. P. (2006) · Nature · doi:10.1038/nature04970 · Cited by 3,295 · see citing works
Response of brain tissue to chronically implanted neural electrodes
Polikov, V. S., Tresco, P. A., & Reichert, W. M. (2005) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2005.08.015 · Cited by 1,896 · see citing works
Learning to control a brain–machine interface for reaching and grasping by primates
Carmena, J. M., Lebedev, M. A., Crist, R. E., O'Doherty, J. E., Santucci, D. M., Dimitrov, D. F., Patil, P. G., Henriquez, C. S., & Nicolelis, M. A. L. (2003) · PLoS Biology · doi:10.1371/journal.pbio.0000042 · Cited by 1,761 · see citing works
Direct Cortical Control of 3D Neuroprosthetic Devices
Taylor, D. M., Tillery, S. I. H., & Schwartz, A. B. (2002) · Science · doi:10.1126/science.1070291 · Cited by 1,542 · see citing works
The Fourier approach to the identification of functional coupling between neuronal spike trains
Rosenberg, J. R., Amjad, A., Breeze, P., Brillinger, D. R., & Halliday, D. M. (1989) · Progress in Biophysics and Molecular Biology · doi:10.1016/0079-6107(89)90004-7 · Cited by 875 · see citing works
Modelling and analysis of local field potentials for studying the function of cortical circuits
Einevoll, G. T., Kayser, C., Logothetis, N. K., & Panzeri, S. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3599 · Cited by 857 · see citing works
Operant Conditioning of Cortical Unit Activity
Fetz, E. E. (1969) · Science · doi:10.1126/science.163.3870.955 · Cited by 631 · see citing works
Failure mode analysis of silicon-based intracortical microelectrode arrays in non-human primates
Barrese, J. C., Rao, N., Paroo, K., Triebwasser, C., Vargas-Irwin, C., Franquemont, L., & Donoghue, J. P. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/6/066014 · Cited by 600 · see citing works
How Local Is the Local Field Potential?
Kajikawa, Y., & Schroeder, C. E. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.029 · Cited by 584 · see citing works
A high-performance neural prosthesis enabled by control algorithm design
Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Fan, J. M., Churchland, M. M., Kaufman, M. T., Kao, J. C., Ryu, S. I., et al. (2012) · Nature Neuroscience · doi:10.1038/nn.3265 · Cited by 564 · see citing works
Local Origin of Field Potentials in Visual Cortex
Katzner, S., Nauhaus, I., Benucci, A., Bonin, V., Ringach, D. L., & Carandini, M. (2009) · Neuron · doi:10.1016/j.neuron.2008.11.016 · Cited by 552 · see citing works
Belitski, A., Gretton, A., Magri, C., Murayama, Y., Montemurro, M. A., Logothetis, N. K., & Panzeri, S. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.0009-08.2008 · Cited by 420 · see citing works
Stimulus contrast modulates functional connectivity in visual cortex
Nauhaus, I., Busse, L., Carandini, M., & Ringach, D. L. (2008) · Nature Neuroscience · doi:10.1038/nn.2232 · Cited by 380 · see citing works
Selecting the signals for a brain–machine interface
Andersen, R. A., Musallam, S., & Pesaran, B. (2004) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2004.10.005 · Cited by 329 · see citing works
Rickert, J., de Oliveira, S. C., Vaadia, E., Aertsen, A., Rotter, S., & Mehring, C. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.0816-05.2005 · Cited by 317 · see citing works
Oscillatory phase coupling coordinates anatomically dispersed functional cell assemblies
Canolty, R. T., Ganguly, K., Kennerley, S. W., Cadieu, C. F., Koepsell, K., Wallis, J. D., & Carmena, J. M. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1008306107 · Cited by 292 · see citing works
Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities
Kipke, D. R., Shain, W. G., Buzsáki, G., Fetz, E. E., Henderson, J. M., Hetke, J. F., & Schalk, G. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.3879-08.2008 · Cited by 283 · see citing works
Closed-Loop Decoder Adaptation Shapes Neural Plasticity for Skillful Neuroprosthetic Control
Orsborn, A. L., Moorman, H. G., Overduin, S. A., Shanechi, M. M., Dimitrov, D. F., & Carmena, J. M. (2014) · Neuron · doi:10.1016/j.neuron.2014.04.048 · Cited by 280 · see citing works
Subthreshold membrane resonance in neocortical neurons
Hutcheon, B., Miura, R. M., & Puil, E. (1996) · Journal of Neurophysiology · doi:10.1152/jn.1996.76.2.683 · Cited by 261 · see citing works
Reversible large-scale modification of cortical networks during neuroprosthetic control
Ganguly, K., Dimitrov, D. F., Wallis, J. D., & Carmena, J. M. (2011) · Nature Neuroscience · doi:10.1038/nn.2797 · Cited by 259 · see citing works
Spike-and-Wave Oscillations Based on the Properties of GABABReceptors
Alain Destexhe, A. (1998) · Journal of Neuroscience · doi:10.1523/jneurosci.18-21-09099.1998 · Cited by 259 · see citing works
Spatial Spread of the Local Field Potential and its Laminar Variation in Visual Cortex
Xing, D., Yeh, C.-I., & Shapley, R. M. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.2573-09.2009 · Cited by 246 · see citing works
Okun, M., Naim, A., & Lampl, I. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.5062-09.2010 · Cited by 229 · see citing works
Restoration of function after brain damage using a neural prosthesis
Guggenmos, D. J., Azin, M., Barbay, S., Mahnken, J. D., Dunham, C., Mohseni, P., & Nudo, R. J. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1316885110 · Cited by 221 · see citing works
Inferring Spike Trains From Local Field Potentials
Rasch, M. J., Gretton, A., Murayama, Y., Maass, W., & Logothetis, N. K. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00919.2007 · Cited by 210 · see citing works
Accurate decoding of reaching movements from field potentials in the absence of spikes
Flint, R. D., Lindberg, E. W., Jordan, L. R., Miller, L. E., & Slutzky, M. W. (2012) · Journal of Neural Engineering · doi:10.1088/1741-2560/9/4/046006 · Cited by 202 · see citing works
Flint, R. D., Wright, Z. A., Scheid, M. R., & Slutzky, M. W. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/5/056005 · Cited by 180 · see citing works
Compact Movable Microwire Array for Long-Term Chronic Unit Recording in Cerebral Cortex of Primates
Jackson, A., & Fetz, E. E. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00569.2007 · Cited by 174 · see citing works
Self-citationBerens, P. (2008) · Frontiers in Systems Neuroscience · doi:10.3389/neuro.06.002.2008 · Cited by 162 · see citing works
Bansal, A. K., Vargas-Irwin, C. E., Truccolo, W., & Donoghue, J. P. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00532.2010 · Cited by 148 · see citing works
Einevoll, G. T., Pettersen, K. H., Devor, A., Ulbert, I., Halgren, E., & Dale, A. M. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00845.2006 · Cited by 144 · see citing works
A Common Structure Underlies Low-Frequency Cortical Dynamics in Movement, Sleep, and Sedation
Hall, T. M., de Carvalho, F., & Jackson, A. (2014) · Neuron · doi:10.1016/j.neuron.2014.07.022 · Cited by 134 · see citing works
Self-citationPerreault, E. J., Kirsch, R. F., & Acosta, A. M. (1999) · Biological Cybernetics · doi:10.1007/s004220050529 · Cited by 127 · see citing works
Engelhard, B., Ozeri-Engelhard, N., Israel, Z. H., Bergman, H., & Vaadia, E. (2013) · Neuron · doi:10.1016/j.neuron.2012.11.015 · Cited by 116 · see citing works
A Neocortical Delta Rhythm Facilitates Reciprocal Interlaminar Interactions via Nested Theta Rhythms
Carracedo, L. M., Kjeldsen, H., Cunnington, L. G., Jenkins, A., Schofield, I. S., Cunningham, M. O., Davies, C. H., Traub, R. D., & Whittington, M. A. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.0735-13.2013 · Cited by 113 · see citing works
Bridging the divide between neuroprosthetic design, tissue engineering and neurobiology
Leach, J. B. (2010) · Frontiers in Neuroengineering · doi:10.3389/neuro.16.018.2009 · Cited by 111 · see citing works
Optimizing the Decoding of Movement Goals from Local Field Potentials in Macaque Cortex
Markowitz, D. A., Wong, Y. T., Gray, C. M., & Pesaran, B. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.4165-11.2011 · Cited by 110 · see citing works
Mehring, C., Nawrot, M. P., de Oliveira, S. C., Vaadia, E., Schulze‐Bonhage, A., Aertsen, A., & Tonio Ball, A. (2004) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2005.09.016 · Cited by 102 · see citing works
O'Leary, J. G., & Hatsopoulos, N. G. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00106.2006 · Cited by 98 · see citing works
Volitional Control of Neural Activity Relies on the Natural Motor Repertoire
Hwang, E. J., Bailey, P. M., & Andersen, R. A. (2013) · Current Biology · doi:10.1016/j.cub.2013.01.027 · Cited by 91 · see citing works
Closed-loop control of spinal cord stimulation to restore hand function after paralysis
Zimmermann, J. B., & Andrew, J. O. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00087 · Cited by 88 · see citing works
Influence of spiking activity on cortical local field potentials
Waldert, S., Lemon, R., & Kraskov, A. (2013) · The Journal of Physiology · doi:10.1113/jphysiol.2013.258228 · Cited by 86 · see citing works
From Neurons to Circuits: Linear Estimation of Local Field Potentials
Rasch, M. J., Logothetis, N. K., & Kreiman, G. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.2390-09.2009 · Cited by 75 · see citing works
The utility of multichannel local field potentials for brain–machine interfaces
Hwang, E. J., & Andersen, R. A. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/4/046005 · Cited by 69 · see citing works
So, K., Dangi, S., Orsborn, A. L., Gastpar, M., & Carmena, J. M. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/2/026002 · Cited by 69 · see citing works
LTS cells in cerebral cortex and their role in generating spike-and-wave oscillations
Alain Destexhe, A., Contreras, D., & Steriade, M. (2001) · Neurocomputing · doi:10.1016/s0925-2312(01)00348-4 · Cited by 69 · see citing works
Intention estimation in brain–machine interfaces
Fan, J. M., Nuyujukian, P., Kao, J. C., Chestek, C. A., Ryu, S. I., & Shenoy, K. V. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/1/016004 · Cited by 68 · see citing works
Westwick, D. T., Pohlmeyer, E. A., Solla, S. A., Miller, L. E., & Perreault, E. J. (2005) · Neural Computation · doi:10.1162/089976606775093855 · Cited by 65 · see citing works
Wang, D., Zhang, Q., Li, Y., Wang, Y., Zhu, J., Zhang, S., & Zheng, X. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/3/036009 · Cited by 58 · see citing works
Relationships between spike-free local field potentials and spike timing in human temporal cortex
Zanos, S. P., Zanos, T. P., Marmarelis, V. Z., Ojemann, G. A., & Fetz, E. E. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00663.2011 · Cited by 54 · see citing works
Interfacing With the Computational Brain
Andrew, J. O., & Fetz, E. E. (2011) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2011.2158586 · Cited by 54 · see citing works
Dine, J., Kühne, C., Deussing, J. M., & Eder, M. (2014) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2014.00002 · Cited by 16 · see citing works
Flagged first, then by citations
1 of the 55 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Memristor networks for real-time neural activity analysis
Zhu, X., Wang, Q., & Lu, W. D. (2020) · Nature Communications · doi:10.1038/s41467-020-16261-1 · Cited by 227 · see citing works
Low-frequency cortical activity is a neuromodulatory target that tracks recovery after stroke
Ramanathan, D., Guo, L. T., Gulati, T., Davidson, G., Hishinuma, A. K., Won, S.-J., Knight, R. T., Chang, E. F., Swanson, R. A., & Ganguly, K. (2018) · Nature Medicine · doi:10.1038/s41591-018-0058-y · Cited by 139 · see citing works
Long Term Recordings with Immobile Silicon Probes in the Mouse Cortex
Okun, M., Lak, A., Carandini, M., & Harris, K. D. (2016) · PLoS ONE · doi:10.1371/journal.pone.0151180 · Cited by 85 · see citing works
Decoding Local Field Potentials for Neural Interfaces
Andrew, J. O., & Hall, T. M. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2612001 · Cited by 77 · see citing works
Self-citationClosed-loop optogenetic control of the dynamics of neural activity in non-human primates
Zaaimi, B., Turnbull, M., Hazra, A., Wang, Y., Gándara, C., McLeod, F., McDermott, E. E., Escobedo‐Cousin, E., Idil, A. S., Bailey, R. G., et al. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00945-8 · Cited by 63 · see citing works
Self-citationA network engineering perspective on probing and perturbing cognition with neurofeedback
Bassett, D. S., & Khambhati, A. N. (2017) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13338 · Cited by 60 · see citing works
A 64-Channel Versatile Neural Recording SoC With Activity-Dependent Data Throughput
Liu, Y., Luan, S., Williams, I. A., Rapeaux, A., & Constandinou, T. G. (2017) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2017.2759339 · Cited by 58 · see citing works
Towards a Distributed, Chronically-Implantable Neural Interface
Ahmadi, N., Cavuto, M. L., Feng, P., Leene, L. B., Maslik, M., Mazza, F., Savolainen, O. W., Szostak, K. M., Bouganis, C.-S., Ekanayake, J., et al. (2019) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2019.8716998 · Cited by 56 · see citing works
Self-citationScherer, M., Steiner, L. A., Kalia, S. K., Hodaie, M., Kühn, A. A., Lozano, A. M., Hutchison, W. D., & Milosevic, L. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2205881119 · Cited by 53 · see citing works
On-Probe Neural Interface ASIC for Combined Electrical Recording and Optogenetic Stimulation
Ramezani, R., Jackson, A., Constandinou, T. G., Degenaar, P. A., Liu, Y., Dehkhoda, F., Soltan, A., Haci, D., Zhao, H., Firfilionis, D., et al. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2018.2818818 · Cited by 52 · see citing works
Self-citationMyoelectric control with abstract decoders
Dyson, M., Barnes, J., & Nazarpour, K. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aacbfe · Cited by 52 · see citing works
Self-citationSequential Neural Activity in Primary Motor Cortex during Sleep
Xu, W., Carvalho, F. D., & Jackson, A. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1408-18.2019 · Cited by 50 · see citing works
Self-citationA Quaternion Weighted Fourier Linear Combiner for Modeling Physiological Tremor
Adhikari, K., Tatinati, S., Ang, W. T., Veluvolu, K. C., & Nazarpour, K. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2530564 · Cited by 48 · see citing works
Self-citationFormento, E., Botros, P., & Carmena, J. M. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac35ac · Cited by 47 · see citing works
Inferring entire spiking activity from local field potentials
Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2021) · Scientific Reports · doi:10.1038/s41598-021-98021-9 · Cited by 45 · see citing works
Physiological properties of brain-machine interface input signals
Slutzky, M. W., & Flint, R. D. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00070.2017 · Cited by 45 · see citing works
Murphy, M. D., Guggenmos, D. J., Bundy, D. T., & Nudo, R. J. (2016) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2015.00497 · Cited by 44 · see citing works
Learning, Generalization, and Scalability of Abstract Myoelectric Control
Dyson, M., Dupan, S. S. G., Jones, H., & Nazarpour, K. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3000310 · Cited by 43 · see citing works
Self-citationKhamechian, M. B., Kozyrev, V., Treue, S., Esghaei, M., & Daliri, M. R. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1819827116 · Cited by 34 · see citing works
Building population models for large-scale neural recordings: Opportunities and pitfalls
Hurwitz, C., Kudryashova, N., Onken, A., & Hennig, M. H. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.07.003 · Cited by 34 · see citing works
Challenges in Scaling Down of Free-Floating Implantable Neural Interfaces to Millimeter Scale
Yang, K.-W. K., Oh, K., & Ha, S. (2020) · IEEE Access · doi:10.1109/access.2020.3007517 · Cited by 32 · see citing works
Brain signal predictions from multi-scale networks using a linearized framework
Hagen, E., Magnússon, S. H., Ness, T. V., Halnes, G., Babu, P. N., Linssen, C., Morrison, A., & Einevoll, G. T. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010353 · Cited by 23 · see citing works
A CMOS-based neural implantable optrode for optogenetic stimulation and electrical recording
Zhao, H., Dehkhoda, F., Ramezani, R., Sokolov, D., Degenaar, P. A., Liu, Y., & Constandinou, T. G. (2015) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2015.7348357 · Cited by 21 · see citing works
Bio‐Voltage Memristors: From Physical Mechanisms to Neuromorphic Interfaces
Wang, S., Wang, R., Cao, Y., Ma, X., Wang, H., & Hao, Y. (2023) · Advanced Electronic Materials · doi:10.1002/aelm.202200972 · Cited by 21 · see citing works
Autonomous SoC for Neural Local Field Potential Recording in mm-Scale Wireless Implants
Leene, L. B., Maslik, M., Feng, P., Szostak, K. M., Mazza, F., & Constandinou, T. G. (2018) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2018.8351147 · Cited by 19 · see citing works
Micoulaud‐Franchi, J., Batail, J.-M. V., Fovet, T., Philip, P., Cermolacce, M., Jaumard‐Hakoun, A., & Vialatte, F. (2019) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-019-09440-4 · Cited by 19 · see citing works
Chauvière, L., Pothof, F., Gansel, K. S., Klon-Lipok, J., Aarts, A. A. A., Holzhammer, T., Paul, O., Singer, W., & Ruther, P. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00464 · Cited by 19 · see citing works
Beudel, M., Sadnicka, A., Edwards, M. J., & de Jong, B. M. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00462 · Cited by 18 · see citing works
Vélez, A., & Carlson, B. A. (2016) · eLife · doi:10.7554/elife.16851 · Cited by 16 · see citing works
Volitional control of single-electrode high gamma local field potentials by people with paralysis
Milekovic, T., Bacher, D., Sarma, A. A., Simeral, J. D., Saab, J., Pandarinath, C., Yvert, B., Sorice, B. L., Blabe, C. H., Oakley, E. M., et al. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00131.2018 · Cited by 14 · see citing works
Hong, N., Kim, B., Lee, J., Choe, H. K., Jin, K. H., & Kang, H. (2024) · Nature Communications · doi:10.1038/s41467-024-44794-2 · Cited by 12 · see citing works
Continuous force decoding from deep brain local field potentials for Brain Computer Interfacing
Shah, S. A., Tan, H., & Brown, P. (2017) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2017.8008367 · Cited by 11 · see citing works
Emergent Low-Frequency Activity in Cortico-Cerebellar Networks with Motor Skill Learning
Fleischer, P. J., Abbasi, A., Fealy, A. W., Danielsen, N. P., Sandhu, R., Raj, P. R., & Gulati, T. (2023) · eNeuro · doi:10.1523/eneuro.0011-23.2023 · Cited by 10 · see citing works
Artefactual origin of biphasic cortical spike-LFP correlation
Okun, M. (2016) · Journal of Computational Neuroscience · doi:10.1007/s10827-016-0625-5 · Cited by 8 · see citing works
The significance of neural inter-frequency power correlations
Savolainen, O. W. (2021) · Scientific Reports · doi:10.1038/s41598-021-02277-0 · Cited by 6 · see citing works
Iwane, F., Dash, D., Salamanca-Giron, R., Hayward, W., Bönstrup, M., Buch, E. R., & Cohen, L. G. (2023) · Current Biology · doi:10.1016/j.cub.2023.06.040 · Cited by 6 · see citing works
Savolainen, O. W., & Constandinou, T. G. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176352 · Cited by 6 · see citing works
Decoding force from deep brain electrodes in Parkinsonian patients
Shah, S. A., Tan, H., & Brown, P. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7592025 · Cited by 5 · see citing works
An event-driven SoC for neural recording
Luan, S., Liu, Y., Williams, I. A., & Constandinou, T. G. (2016) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2016.7833817 · Cited by 5 · see citing works
Sequence action representations contextualize during early skill learning
Dash, D., Iwane, F., Hayward, W., Salamanca-Giron, R., Bönstrup, M., Buch, E. R., & Cohen, L. G. (2025) · eLife · doi:10.7554/elife.102475.4 · Cited by 4 · see citing works
Unraveling the roles of spatial working memory sustained and selective neurons in prefrontal cortex
Aliramezani, M., Constantinidis, C., & Daliri, M. R. (2025) · Communications Biology · doi:10.1038/s42003-025-08211-8 · Cited by 4 · see citing works
Predicting Single-Unit Activity from Local Field Potentials with LSTMs
Savolainen, O. W., & Constandinou, T. G. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175265 · Cited by 4 · see citing works
Selecting an effective amplitude threshold for neural spike detection
Zhang, Z., & Constandinou, T. G. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871955 · Cited by 4 · see citing works
Kaloti, A. S., Johnson, E. C., Bresee, C. S., Naufel, S. N., Perich, M. G., Jones, D. L., & Hartmann, M. J. Z. (2016) · PLoS ONE · doi:10.1371/journal.pone.0158399 · Cited by 4 · see citing works
Sequence action representations contextualize during early skill learningPreprint
Dash, D., Iwane, F., Hayward, W., Salamanca-Giron, R., Bönstrup, M., Buch, E. R., & Cohen, L. G. (2024) · eLife · doi:10.7554/elife.102475 · Cited by 4 · see citing works
Stančák, A., Cook, S., Wright, H., & Fallon, N. B. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.028 · Cited by 3 · see citing works
Cortico-striatal beta-oscillations as a marker of learned reward valuePreprint
Koloski, M. F., Hulyalkar, S., Tang, T., Wu, X., Fakhraei, L., Barnes, S. A., Mishra, J., & Ramanathan, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.24.513264 · Cited by 2 · see citing works
Silveira, C. R. A., Brunton, E., Spendiff, S., & Nazarpour, K. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512751 · Cited by 1 · see citing works
Self-citationShi, C., Zhang, C., Chen, J., & Yao, Z. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae051 · Cited by 1 · see citing works
Local field potentials get funny
Nolan, M. F. (2016) · The Journal of Physiology · doi:10.1113/jp272673 · Cited by 1 · see citing works
The Significance of Neural Inter-Frequency CorrelationsPreprint
Savolainen, O. W. (2021) · Research Square · doi:10.21203/rs.3.rs-329644/v1 · Cited by 1 · see citing works
Sequence action representations contextualize during rapid skill learningPreprint
Dash, D., Iwane, F., Hayward, W., Salamanca-Giron, R., Bönstrup, M., Buch, E. R., & Cohen, L. G. (2025) · eLife · doi:10.7554/elife.102475.2 · Cited by 1 · see citing works
Tomsett, R. (2015) · The Winnower
Cavallari, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.00082
Meditation, Exceptional Psychophysiological Control, and Parapsychology
Cardeña, E. (2019) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198808640.013.47
Gabrielli, F., Megemont, M., Dallel, R., Luccarini, P., & Monconduit, L. (2021) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2021.05.025
Sequence action representations contextualize during rapid skill learningPreprint
Dash, D., Iwane, F., Hayward, W., Salamanca-Giron, R., Bönstrup, M., Buch, E. R., & Cohen, L. G. (2024) · eLife · doi:10.7554/elife.102475.1
High‐Stability Ionic Conductive Filtering Transistors for Bio‐Inspired Signal Processing
Liu, W., Wang, J., Xie, P., Feng, X., Xu, Y., Jin, C., Shi, X., Li, R., Ho, J. C. Y., Yang, J., et al. (2025) · Small · doi:10.1002/smll.202502874
Sequence action representations contextualize during early skill learningPreprint
Dash, D., Iwane, F., Hayward, W., Salamanca-Giron, R., Bönstrup, M., Buch, E. R., & Cohen, L. G. (2025) · eLife · doi:10.7554/elife.102475.3
Author response: Sequence action representations contextualize during early skill learning
Dash, D., Iwane, F., Hayward, W., Salamanca-Giron, R., Bönstrup, M., Buch, E. R., & Cohen, L. G. (2025) · eLife · doi:10.7554/elife.102475.4.sa4
Megemont, M. (2019) · theses.fr (ABES) · doi:10.70675/c0dcb017z7bb2z46e9zb39azc730e2593a65
Soheilipour, N., Akhavan, A., Rouhani, E., & Rahimi, R. (2026) · Scientific Reports · doi:10.1038/s41598-026-54776-7
- Authors
- Hall, T. M., Nazarpour, K., & Jackson, A.
- Affiliations
- Newcastle University (GB)
- Venue
- Nature Communications
- Published
- 2014-11-14
- Volume
- 5
- Issue
- 1
- Pages
- 5462-5462
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC4243238
- OpenAlex
- W1995707034
- Cited by
- 61
- References
- 55
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 4 · 2023: 3 · 2022: 5 · 2021: 5 · 2020: 5 · 2019: 9 · 2018: 5 · 2017: 4 · 2016: 11 · 2015: 3
- FWCI
- 2.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- Wellcome Trust (086561)
- National Institute for Health and Care Research (ACF-2009-01-004)
- Medical Research Council (G0802195, K501396)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- local field potentials, biofeedback, brain-computer interface, motor cortex, microelectrode arrays, neural prostheses, slow potentials, monkeys, real-time feedback, neural decoding
- MeSH terms
- Action Potentials, Animals, Electrodes, Implanted, Female, Macaca mulatta, Neurons, Neurofeedback, Brain-Computer Interfaces
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
