A 128-Channel 6mW Wireless Neural Recording IC with On-the-Fly Spike Sorting and UWB Tansmitter

Chae, Moosung; Liu, Wentai; Yang, Zhi; Chen, Tung-Chien; Kim, Jungsuk; Sivaprakasam, Mohanasankar; Yuce, Mehmet Rasit

doi:10.1109/isscc.2008.4523099

Cited by 136 publications

(113 citation statements)

References 9 publications

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“…This frequency range is the operating bandwidth of our impulse-UWB test setup that has also been used in some recent implantable systems [23]. In the case of breast tissue, the matched band is narrow, corresponding to low , as expected from the characteristics shown in Fig.…”

Section: A Antenna Configurationmentioning

confidence: 77%

Dielectric Loaded Impedance Matching for Wideband Implanted Antennas

Dissanayake

Esselle

Yuce

2009

IEEE Trans. Microwave Theory Techn.

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Abstract-In implanted biomedical devices, due to the presence of surrounding dissipative biological tissue, the antenna suffers poor impedance matching. This causes degradation in the performance of a wideband or ultra-wideband (UWB) implanted device. Moreover, the electrical properties of tissue change from organ to organ, and possibly from time to time. In this paper, it is shown that loading of antennas with suitable insulators can deliver broadband matching across a range of dissipative medium properties. An impedance-matched UWB antenna designed to operate inside a lossy medium, which has varying electromagnetic properties within the range expected in biological tissues, is presented. The operating bandwidth of the proposed design is 3.5-4.5 GHz, which is an interference-free subset of the unlicensed UWB band in the US. It is demonstrated that once the dielectric loading is applied, the conventional procedure for antenna design in free space can be followed. The proposed implantable small capsule-shaped slot antenna has been characterized using numerical simulations. Details of a proof-of-concept experiment are presented.Index Terms-Biomedical electronic, biomedical engineering, implanted device, ultra-wideband (UWB) antenna.

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Section: A Antenna Configurationmentioning

confidence: 77%

Dielectric Loaded Impedance Matching for Wideband Implanted Antennas

Dissanayake

Esselle

Yuce

2009

IEEE Trans. Microwave Theory Techn.

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“…A band-pass digital filter is first performed to reject the low-frequency field potential and high-frequency thermal noise. Then, the nonlinear energy operator (NEO) spike detector [9,10] calculates the energy function for the neural waveform. Once the result reaches the threshold at the peak of the convex curve, a find-spike-event signal is sent.…”

Section: Neural Signal Processing Platform Andmentioning

confidence: 99%

On-chip principal component analysis with a mean pre-estimation method for spike sorting

Chen

Liu

et al. 2009

2009 IEEE International Symposium on Circuits and Systems

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Abstract-Principal component analysis (PCA) spike sorting hardware in an integrated neural recording system is highly desired for wireless neuroprosthetic devices. However, a large memory is required to store thousands of spike events during the PCA training procedure, which impedes the on-chip implementation for the PCA training engine. In this paper, a mean pre-estimation method is proposed to save 99.01% memory requirement by breaking the algorithm dependency. According to the simulation result, 100 dB signal-to-error power ratio can be preserved for the resulting principal components. According to the implementation result, 6.07 mm 2 silicon area is required after a 283.16 mm 2 area saving for the proposed PCA training hardware.

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“…For example, an amplitude threshold should be trained in order to accurately detect the spikes in the online spike detection engine. Many on-line processing hardware units for spike sorting have been proposed [3], [4]. The principal component analysis, one of the training algorithms for the spike feature extraction, has also been designed in VLSI hardware [5].…”

Section: Introductionmentioning

confidence: 99%

Density-based hardware-oriented classification for spike sorting microsystems

Cheng

Chen

Chang

et al. 2011

2011 5th International IEEE/EMBS Conference on Neural Engineering

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Abstract-Successful proof-of-concept laboratory experiments on cortically-controlled brain computer interface motivate continued development for neural prosthetic microsystems (NPMs). One of the research directions is to realize realtime spike sorting processors (SSPs) on the NPM. The SSP detects the spikes, extracts the features, and then performs the classification algorithm in realtime in order to differentiate the spikes for the different firing neurons. Several architectures have been designed for the spike detection and feature extraction. However, the classification hardware is missing. To complete the SSP, a density-based hardware-oriented classification algorithm is proposed for hardware implementation. The traditional classification algorithms require a considerable memory space to store all the training features during the processing iteration, which results in a considerable power and area for the hardware. The proposed one is designed based on the density map of the spike features. The density map can be accumulated on-line with the coming of the spike features. Therefore the algorithm can save significant memory space, and is good for efficient hardware implementation.

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A 128-Channel 6mW Wireless Neural Recording IC with On-the-Fly Spike Sorting and UWB Tansmitter

Cited by 136 publications

References 9 publications

Dielectric Loaded Impedance Matching for Wideband Implanted Antennas

Dielectric Loaded Impedance Matching for Wideband Implanted Antennas

On-chip principal component analysis with a mean pre-estimation method for spike sorting

Density-based hardware-oriented classification for spike sorting microsystems

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