A Digitally Assisted, Signal Folding Neural Recording Amplifier
Abstract
A novel signal folding and reconstruction scheme for neural recording applications that exploits the 1/f(n) characteristics of neural signals is described in this paper. The amplified output is 'folded' into a predefined range of voltages by using comparison and reset circuits along with the core amplifier. After this output signal is digitized and transmitted, a reconstruction algorithm can be applied in the digital domain to recover the amplified signal from the folded waveform. This scheme enables the use of an analog-to-digital convertor with less number of bits for the same effective dynamic range. It also reduces the transmission data rate of the recording chip. Both of these features allow power and area savings at the system level. Other advantages of the proposed topology are increased reliability due to the removal of pseudo-resistors, lower harmonic distortion and low-voltage operation. An analysis of the reconstruction error introduced by this scheme is presented along with a behavioral model to provide a quick estimate of the post reconstruction dynamic range. Measurement results from two different core amplifier designs in 65 nm and 180 nm CMOS processes are presented to prove the generality of the proposed scheme in the neural recording applications. Operating from a 1 V power supply, the amplifier in 180 nm CMOS has a gain of 54.2 dB, bandwidth of 5.7 kHz, input referred noise of 3.8 μVrms and power dissipation of 2.52 μW leading to a NEF of 3.1 in spike band. It exhibits a dynamic range of 66 dB and maximum SNDR of 43 dB in LFP band. It also reduces system level power (by reducing the number of bits in the ADC by 2) as well as data rate to 80% of a conventional design. In vivo measurements validate the ability of this amplifier to simultaneously record spike and LFP signals.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Modern circuit designs and IC fabrication technologies have produced innovative designs that allow designers to relax the size-functionality compromises.
“However, modern circuit designs [77, 92] and IC fabrication technologies [18, 86] have resulted in innovative designs, enabling designers to relax such compromises.”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [18]
Cites
Brain–machine interfaces: past, present and future
Lebedev, M. A., & Nicolelis, M. A. L. (2006) · Trends in Neurosciences · doi:10.1016/j.tins.2006.07.004 · Cited by 1,875 · see citing works
A low-power low-noise cmos for amplifier neural recording applications
Harrison, R. R., & Charles, C. (2003) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2003.811979 · Cited by 1,695 · 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
Real-time prediction of hand trajectory by ensembles of cortical neurons in primates
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) · Nature · doi:10.1038/35042582 · Cited by 1,491 · see citing works
Dry-Contact and Noncontact Biopotential Electrodes: Methodological Review
Chi, Y. M., Jung, T., & Cauwenberghs, G. (2010) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2010.2084078 · Cited by 1,069 · see citing works
A low-power integrated circuit for a wireless 100-electrode neural recording system.
Harrison, R. R., Watkins, P. T., Kier, R. J., Lovejoy, R. O., Black, D. J., Greger, B., & Solzbacher, F. (2007) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2006.886567 · Cited by 782 · see citing works
A Low-Power Integrated Circuit for a Wireless 100-Electrode Neural Recording System
Harrison, R. R., Watkins, P., Kier, R. J., Lovejoy, R. O., Black, D., Normann, R. A., & Solzbacher, F. (2006) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc.2006.1696288 · Cited by 731 · see citing works
An Energy-Efficient Micropower Neural Recording Amplifier
Wattanapanitch, W., Fee, M., & Sarpeshkar, R. (2007) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2007.907868 · Cited by 441 · see citing works
Inference of hand movements from local field potentials in monkey motor cortex
Mehring, C., Rickert, J., Vaadia, E., de Oliveira, S. C., Aertsen, A., & Rotter, S. (2003) · Nature Neuroscience · doi:10.1038/nn1158 · Cited by 435 · see citing works
Cortical Local Field Potential Encodes Movement Intentions in the Posterior Parietal Cortex
Scherberger, H., Jarvis, M. R., & Andersen, R. A. (2005) · Neuron · doi:10.1016/j.neuron.2005.03.004 · Cited by 389 · see citing works
A three-dimensional microelectrode array for chronic neural recording
Hoogerwerf, A. C., & Wise, K. D. (1994) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.335862 · Cited by 372 · see citing works
A 128-Channel 6 mW Wireless Neural Recording IC With Spike Feature Extraction and UWB Transmitter
Chae, M. S., Yang, Z., Yuce, M. R., Hoang, L., & Liu, W. (2009) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2021607 · Cited by 369 · see citing works
Single unit recording capabilities of a 100 microelectrode array
Nordhausen, C. T., Maynard, E. M., & Normann, R. A. (1996) · Brain Research · doi:10.1016/0006-8993(96)00321-6 · Cited by 358 · see citing works
Muller, R., Gambini, S., & Rabaey, J. M. (2012) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2011.2163552 · Cited by 338 · see citing works
The Design of Integrated Circuits to Observe Brain Activity
Harrison, R. R. (2008) · Proceedings of the IEEE · doi:10.1109/jproc.2008.922581 · Cited by 330 · see citing works
Lewis, B. L. (2000) · Cerebral Cortex · doi:10.1093/cercor/10.12.1168 · Cited by 287 · see citing works
An ultralow-energy adc for smart dust
Scott, M. D., Boser, B. E., & Pister, K. S. J. (2003) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2003.813296 · Cited by 282 · see citing works
The 128-Channel Fully Differential Digital Integrated Neural Recording and Stimulation Interface
Shahrokhi, F., Abdelhalim, K., Serletis, D., Carlen, P. L., & Genov, R. (2010) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2010.2041350 · Cited by 263 · see citing works
Puig, M. V. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhh104 · Cited by 232 · see citing works
HermesE: A 96-Channel Full Data Rate Direct Neural Interface in 0.13 $\mu$m CMOS
Gao, H., Walker, R. M., Nuyujukian, P., Makinwa, K. A. A., Shenoy, K. V., Murmann, B., & Meng, T. H. Y. (2012) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2012.2185338 · Cited by 189 · see citing works
Reducing Motion Artifacts and Interference in Biopotential Recording
Webster, J. G. (1984) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1984.325244 · Cited by 185 · see citing works
Integrated circuit amplifiers for multi-electrode intracortical recording
Jochum, T., Denison, T., & Wolf, P. D. (2009) · Journal of Neural Engineering · doi:10.1088/1741-2560/6/1/012001 · Cited by 167 · see citing works
Energy Efficient Low-Noise Neural Recording Amplifier With Enhanced Noise Efficiency Factor
Majidzadeh, V., Schmid, A., & Leblebici, Y. (2011) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2010.2078815 · Cited by 165 · see citing works
Azin, M., Guggenmos, D. J., Barbay, S., Nudo, R. J., & Mohseni, P. (2011) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2011.2108770 · Cited by 164 · see citing works
Han, D., Zheng, Y., Rajkumar, R., Dawe, G. S., & Je, M. (2013) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2014.2298860 · Cited by 147 · see citing works
Self-citationA 5 $\mu$W/Channel Spectral Analysis IC for Chronic Bidirectional Brain–Machine Interfaces
Avestruz, A.-T., Santa, W. A., Carlson, D., Jensen, R. M., Stanslaski, S. R., Helfenstine, A., & Denison, T. (2008) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2008.2006460 · Cited by 143 · see citing works
A 100-Channel 1-mW Implantable Neural Recording IC
Zou, X., Liu, L., Cheong, J. H., Yao, L., Li, P., Cheng, M.-Y., Goh, W. L., Rajkumar, R., Dawe, G. S., Cheng, K.-W., et al. (2013) · IEEE Transactions on Circuits and Systems I: Regular Papers · doi:10.1109/tcsi.2013.2249175 · Cited by 132 · see citing works
Self-citationLow-Power Circuits for Brain–Machine Interfaces
Sarpeshkar, R., Wattanapanitch, W., Arfin, S. K., Rapoport, B. I., Mandal, S., Baker, M. W., Fee, M. S., Musallam, S., & Andersen, R. A. (2008) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2008.2003198 · Cited by 91 · see citing works
Adaptive Resolution ADC Array for an Implantable Neural Sensor
O’Driscoll, S., Shenoy, K. V., & Meng, T. H. Y. (2011) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2011.2145418 · Cited by 63 · see citing works
Trantham‐Davidson, H., Szumlinski, K. K., McFarland, K., Kalivas, P. W., & Lavı́n, A. (2002) · Neuroscience · doi:10.1016/s0306-4522(02)00246-4 · Cited by 54 · see citing works
Preliminary Study of the Thermal Impact of a Microelectrode Array Implanted in the Brain
Kim, S., Normann, R. A., Harrison, R. R., et al. (2006) · doi:10.1109/iembs.2006.260307 · Cited by 53 · see citing works
800 nW 43 nV/√Hz neural recording amplifier with enhanced noise efficiency factor
Liu, L., Zou, X., Goh, W. L., Ramamoorthy, R., Dawe, G. S., & Je, M. (2012) · Electronics Letters · doi:10.1049/el.2012.0685 · Cited by 51 · see citing works
Self-citationBasu, A., Robucci, R., & Hasler, P. E. (2007) · IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications · doi:10.1109/tcsi.2007.905657 · Cited by 45 · see citing works
Self-citationBasu, A., & Hasler, P. E. (2010) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2010.2042626 · Cited by 37 · see citing works
Self-citationA signal folding neural amplifier exploiting neural signal statistics
Yi, C., Basu, A., & Je, M. (2012) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2012.6418456 · Cited by 6 · see citing works
Self-citationA digitally assisted, pseudo-resistor-less amplifier in 65nm CMOS for neural recording applications
Yi, C., Basu, A., & Je, M. (2012) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas.2012.6292033 · Cited by 5 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Implantable neurotechnologies: a review of integrated circuit neural amplifiers
Ng, K. A., Greenwald, E., Xu, Y. P., & Thakor, N. V. (2016) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-015-1431-3 · Cited by 83 · see citing works
High-density neural recording system design
Lee, H.-S., Eom, K., Park, M., Ku, S.-B., Lee, K., & Lee, H.-M. (2022) · Biomedical Engineering Letters · doi:10.1007/s13534-022-00233-z · Cited by 17 · see citing works
- Authors
- Chen, Y., Basu, A., Liu, L., Zou, X., Rajkumar, R., Dawe, G. S., & Je, M.
- Affiliations
- Nanyang Technological University (SG)
- Advanced Micro Devices (Singapore) (SG)
- National University of Singapore (SG)
- Agency for Science, Technology and Research (SG)
- Venue
- IEEE Transactions on Biomedical Circuits and Systems
- Published
- 2014-08
- Volume
- 8
- Issue
- 4
- Pages
- 528-542
- ISSN
- 1932-4545 (ISSN-L)
- 1940-9990
- OpenAlex
- W2087939279
- Cited by
- 43
- References
- 36
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 2 · 2022: 3 · 2021: 2 · 2020: 2 · 2019: 4 · 2018: 4 · 2017: 7 · 2016: 5 · 2015: 5 · 2014: 3
- FWCI
- 3.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Biomedical Circuits and Systems (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Nanyang Technological University
- MediaTek
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Analog and Mixed-Signal Circuit Design
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Radio Frequency Integrated Circuit Design
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
- Keywords
- signal reconstruction, 1/f noise, low-power analog front-end, neural spikes, local field potentials, behavioral modeling, reconstruction error, CMOS integrated circuit, in vivo neural recording
- MeSH terms
- Amplifiers, Electronic, Analog-Digital Conversion, Animals, Electrodes, Implanted, Electronics, Medical, Neurons, Rats, Sprague-Dawley, Rats
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
