Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> The detection of neural spike activity is a technical challenge that is a prerequisite for studying many types of brain function. Measuring the activity of individual neurons accurately can be difficult due to large amounts of background noise and the difficulty in distinguishing the action potentials of one neuron from those of others in the local area. This article reviews algorithms and methods for detecting and classifying action potentials, a problem commonly referred to as spike sorting. The article first discusses the challenges of measuring neural activity and the basic issues of signal detection and classification. It reviews and illustrates algorithms and techniques that have been applied to many of the problems in spike sorting and discusses the advantages and limitations of each and the applicability of these methods for different types of experimental demands. The article is written both for the physiologist wanting to use simple methods that will improve experimental yield and minimize the selection biases of traditional techniques and for those who want to apply or extend more sophisticated algorithms to meet new experimental challenges. <s> BIB001 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> Real time spike detection is an important requirement for developing brain machine interfaces (BMIs). We examined three classes of spike-detection algorithms to determine which is best suited for a wireless BMI with a limited transmission bandwidth and computational capabilities. The algorithms were analyzed by tabulating true and false detections when applied to a set of realistic artificial neural signals with known spike times and varying signal to noise ratios. A design-specific cost function was developed to score the relative merits of each detector; correct detections increased the score, while false detections and computational burden reduced it. Test signals both with and without overlapping action potentials were considered. We also investigated the utility of rejecting spikes that violate a minimum refractory period by occurring within a fixed time window after the preceding threshold crossing. Our results indicate that the cost-function scores for the absolute value operator were comparable to those for more elaborate nonlinear energy operator based detectors. The absolute value operator scores were enhanced when the refractory period check was used. Matched-filter-based detectors scored poorly due to their relatively large computational requirements that would be difficult to implement in a real-time system. <s> BIB002 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> This paper discusses a low-power spike detection circuit, which reduces bandwidth from neural recordings by only outputting a short pulse at each neural spike time. Communication bandwidth is dramatically reduced to the number of spikes. The principal idea is to use two low pass filters, one with a higher cutoff frequency to remove high frequency noise and the other with a lower cutoff frequency to create a local average. When the difference between the signal and the local average rises above a threshold, a spike is detected. The circuit uses subthreshold CMOS to keep the power consumption low enough for integration of many channels in an implanted device. This spike detection method shows promising results towards a robust and unsupervised algorithm that is lower power and more compact than existing spike detection methods. <s> BIB003 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> We compare the performance of algorithms for automatic spike detection in neural recording applications. Each algorithm sets a threshold based on an estimate of the background noise level. The adaptive spike detection algorithm is suitable for implementation in analog VLSI; results from a proof-of-concept chip using neural data are presented. We also present simulation results of algorithm performance on neural data and compare it to other methods of threshold level adjustment based on the root-mean-square (rms) voltage measured over a finite window. We show that the adaptive spike detection algorithm measures the background noise level accurately despite the presence of large-amplitude action potentials and multi-unit hash. Simulation results enable us to optimize the algorithm parameters, leading to an improved spike detector circuit that is currently being developed. <s> BIB004 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> In a prior work, a wavelet-based vector quantization (VQ) approach was proposed to perform lossy compression of electrocardiogram (ECG) signals. We investigate and fix its coding inefficiency problem in lossless compression and extend it to allow both lossy and lossless compression in a unified coding framework. The well-known 9/7 filters and 5/3 integer filters are used to implement the wavelet transform (WT) for lossy and lossless compression, respectively. The codebook updating mechanism, originally designed for lossy compression, is modified to allow lossless compression as well. In addition, a new and cost-effective coding strategy is proposed to enhance the coding efficiency of set partitioning in hierarchical tree (SPIHT) at the less significant bit representation of a WT coefficient. ECG records from the MIT/BIH Arrhythmia and European ST-T Databases are selected as test data. In terms of the coding efficiency for lossless compression, experimental results show that the proposed codec improves the direct SPIHT approach and the prior work by about 33% and 26%, respectively. <s> BIB005 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> Two thin-film microelectrode arrays with integrated circuitry have been developed for extracellular neural recording in behaving animals. An eight-site probe for simultaneous neural recording and stimulation has been designed that includes on-chip amplifiers that can be individually bypassed, allowing direct access to the iridium sites for electrical stimulation. The on-probe amplifiers have a gain of 38.9 dB, an upper-cutoff frequency of 9.9 kHz, and an input-referred noise of 9.2 /spl mu/V /sub rms/ integrated from 100 Hz to 10 kHz. The low-frequency cutoff of the amplifier is tunable to allow the recording of field potentials and minimize stimulus artifact. The amplifier consumes 68 /spl mu/W from /spl plusmn/1.5 V supplies and occupies 0.177 mm/sup 2/ in 3 /spl mu/m features. In vivo recordings have shown that the preamplifiers can record single-unit activity 1 ms after the onset of stimulation on sites as close as 20 /spl mu/m to the stimulating electrode. A second neural recording array has been developed which multiplexes 32 neural signals onto four output data leads. Providing gain on this array eliminates the need for bulky head-mounted circuitry and reduces motion artifacts. The time-division multiplexing circuitry has crosstalk between consecutive channels of less than 6% at a sample rate of 20 kHz per channel. Amplified, time-division-multiplexed multichannel neural recording allows the large-scale recording of neuronal activity in freely behaving small animals with minimum number of interconnect leads. <s> BIB006 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> Suppose we are given a vector $f$ in $\R^N$. How many linear measurements do we need to make about $f$ to be able to recover $f$ to within precision $\epsilon$ in the Euclidean ($\ell_2$) metric? Or more exactly, suppose we are interested in a class ${\cal F}$ of such objects--discrete digital signals, images, etc; how many linear measurements do we need to recover objects from this class to within accuracy $\epsilon$? This paper shows that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal $f \in {\cal F}$ decay like a power-law (or if the coefficient sequence of $f$ in a fixed basis decays like a power-law), then it is possible to reconstruct $f$ to within very high accuracy from a small number of random measurements. <s> BIB007 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> Many applications in signal processing require the efficient representation and processing of data. The traditional approach to efficient signal representation is compression. In recent years, there has been a new approach to compression at the sensing level. Compressed sensing (CS) is an emerging field which is based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper, we propose an application of compressed sensing in the field of biomedical signal processing, particularly electroencophelogram (EEG) collection and storage. A compressed sensing framework is introduced for efficient representation of multichannel, multiple trial EEG data. The proposed framework is based on the revelation that EEG signals are sparse in a Gabor frame. The sparsity of EEG signals in a Gabor frame is utilized for compressed sensing of these signals. A simultaneous orthogonal matching pursuit algorithm is shown to be effective in the joint recovery of the original multiple trail EEG signals from a small number of projections. <s> BIB008 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> The new theory of compressive sensing enables direct analog-to-information conversion of compressible signals at sub-Nyquist acquisition rates. The authors develop new theory, algorithms, performance bounds, and a prototype implementation for an analog-to-information converter based on random demodulation. The architecture is particularly apropos for wideband signals that are sparse in the time-frequency plane. End-to-end simulations of a complete transistor-level implementation prove the concept under the effect of circuit nonidealities. <s> BIB009 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> A mixed-signal front-end processor for multichannel neuronal recording is described. It receives 12 differential-input channels of implanted recording electrodes. A programmable cutoff High Pass Filter (HPF) blocks dc and low-frequency input drift at about 1 Hz. The signals are band-split at about 200 Hz to low-frequency Local Field Potential (LFP) and high-frequency spike data (SPK), which is band limited by a programmable-cutoff LPF, in a range of 8-13 kHz. Amplifier offsets are compensated by 5-bit calibration digital-to-analog converters (DACs). The SPK and LFP channels provide variable amplification rates of up to 5000 and 500, respectively. The analog signals are converted into 10-bit digital form, and streamed out over a serial digital bus at up to 8 Mbps. A threshold filter suppresses inactive portions of the signal and emits only spike segments of programmable length. A prototype has been fabricated on a 0.35-mum CMOS process and tested successfully, demonstrating a 3-muV noise level. Special interface system incorporating an embedded CPU core in a programmable logic device accompanied by real-time software has been developed to allow connectivity to a computer host <s> BIB010 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> A fully implantable neural data acquisition system is a key component of a clinically viable cortical brain–machine interface. We present the design and implementation of a single-chip device that serves the processing needs of such a system. Our device processes 96 channels of multi-unit neural data and performs all digital processing necessary for bidirectional wireless communication. The implementation utilizes a single programmable logic device that is responsible for performing data reduction on the 96 channels of neural data, providing a bidirectional telemetry interface to a transceiver and performing command interpretation and system supervision. The device takes as input neural data sampled at 31.25 kHz and outputs a line-encoded serial bitstream containing the information to be transmitted by the transceiver. Data can be output in one of the following four modes: (1) streaming uncompressed data from a single channel, (2) extracted spike waveforms from any subset of the 96 channels, (3) 1 ms bincounts for each channel or (4) streaming data along with extracted spikes from a single channel. The device can output up to 2000 extracted spikes per second with latencies suitable for a brain–machine interface application. This device provides all of the digital processing components required by a fully implantable system. <s> BIB011 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> A 64-channel neural processor has been developed for use in an implantable neural recording microsystem. In the Scan Mode, the processor is capable of detecting neural spikes by programmable positive, negative, or window thresholding. Spikes are tagged with their associated channel addresses and formed into 18-bit data words that are sent serially to the external host. In the Monitor Mode, two channels can be selected and viewed at high resolution for studies where the entire signal is of interest. The processor runs from a 3-V supply and a 2-MHz clock, with a channel scan rate of 64 kS/s and an output bit rate of 2 Mbps <s> BIB012 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> Time-frequency domain signal processing of neural recordings, from high-density microelectrode arrays implanted in the cortex, is highly desired to ease the bandwidth bottleneck associated with data transfer to extra-cranial processing units. Because of its energy compactness features, discrete wavelet transform (DWT) has been shown to provide efficient data compression for neural records without compromising the information content. This paper describes an area-power minimized hardware implementation of the lifting scheme for multilevel, multichannel DWT with quantized filter coefficients and integer computation. Performance tradeoffs and key design decisions for implantable neuroprosthetics are presented. A 32-channel 4-level version of the circuit has been custom designed in 0.18-mum CMOS and occupies only 0.22 mm2 area and consumes 76 muW of power, making it highly suitable for implantable neural interface applications requiring wireless data transfer. <s> BIB013 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> We present a multichip structure assembled with a medical-grade stainless-steel microelectrode array intended for neural recordings from multiple channels. The design features a mixed-signal integrated circuit (IC) that handles conditioning, digitization, and time-division multiplexing of neural signals, and a digital IC that provides control, bandwidth reduction, and data communications for telemetry toward a remote host. Bandwidth reduction is achieved through action potential detection and complete capture of waveforms by means of onchip data buffering. The adopted architecture uses high parallelism and low-power building blocks for safety and long-term implantability. Both ICs are fabricated in a CMOS 0.18-mum process and are subsequently mounted on the base of the microelectrode array. The chips are stacked according to a vertical integration approach for better compactness. The presented device integrates 16 channels, and is scalable to hundreds of recording channels. Its performance was validated on a testbench with synthetic neural signals. The proposed interface presents a power consumption of 138 muW per channel, a size of 2.30 mm2, and achieves a bandwidth reduction factor of up to 48 with typical recordings. <s> BIB014 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> We present a low-power complementary metal-oxide semiconductor (CMOS) analog integrated biopotential detector intended for neural recording in wireless multichannel implants. The proposed detector can achieve accurate automatic discrimination of action potential (APs) from the background activity by means of an energy-based preprocessor and a linear delay element. This strategy improves detected waveforms integrity and prompts for better performance in neural prostheses. The delay element is implemented with a low-power continuous-time filter using a ninth-order equiripple allpass transfer function. All circuit building blocks use subthreshold OTAs employing dedicated circuit techniques for achieving ultra low-power and high dynamic range. The proposed circuit function in the submicrowatt range as the implemented CMOS 0.18- microm chip dissipates 780 nW, and it features a size of 0.07 mm(2). So it is suitable for massive integration in a multichannel device with modest overhead. The fabricated detector succeeds to automatically detect APs from underlying background activity. Testbench validation results obtained with synthetic neural waveforms are presented. <s> BIB015 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> We present the design of an adaptive neural spike detector that dynamically adjusts the spike detection threshold based on the signal to noise ratio of the neural data sets. We propose a self-learning architecture, with a threshold-lock loop that feeds back a spike sorting performance index to the FSM inside the adaptive spike detector. The FSM references this performance index and dynamically determines an optimum threshold level for the incoming neural data sets. The architecture enables an autonomous operation without any manual adjustment from users. The simulation results demonstrate that the adaptive spike detector successfully locks to a threshold level, which is optimum from a spike-sorting standpoint. <s> BIB016 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> This letter presents progress toward an energy efficient neural data acquisition transponder for brain-computer interfaces. The transponder utilizes a four-channel time-multiplexed analog front-end and an energy efficient short-range backscattering RF link to transmit digitized wireless data. In addition, a low-complexity autonomous and adaptive digital neural signal processor is proposed to minimize wireless bandwidth and overall power dissipation. <s> BIB017 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> A 3D microsystem for multi-site penetrating extracellular neural recording from the brain is presented. A 16 times 16-channel neural recording interface integrated prototype fabricated in 0.35 mum CMOS occupies 3.5 mm times 4.5 mm area. Each recording channel dissipates 15 muW of power with input-referred noise of 7 muVrms over 5 kHz bandwidth. A switched-capacitor delta read-out data compression circuit trades recording accuracy for the output data rate. An array of 1.5 mm platinum-coated microelectrodes is bonded directly onto the die. Results of in vitro experimental recordings from intact mouse hippocampus validate the circuit design and the on-chip electrode bonding technology. <s> BIB018 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> A signal-agnostic compressed sensing (CS) acquisition system is presented that addresses both the energy and telemetry bandwidth constraints of wireless sensors. The CS system enables continuous data acquisition and compression that are suitable for a variety of biophysical signals. A hardware efficient realization of the CS sampling demonstrates data compression up to 40x on an EEG signal while maintaining low perceptual loss in the reconstructed signal. The proposed system also simultaneously relaxes the noise and resolution constraints of the analog front end (AFE) and ADC by nearly an order of magnitude. The CS sampling hardware is implemented in a 90 nm CMOS process and consumes 1.9 µW at 0.6 V and 20 kS/s. <s> BIB019 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> We present the design and implementation of linear-phase delay filters for ultra-low-power signal processing in neural recording implants. We use these filters as low-distortion delay elements along with an automatic biopotential detector to perform integral waveform extraction and efficient power management. The presented delay elements are realized employing continuous-time OTA-C filters featuring 9th-order equiripple transfer functions with constant group delay. Such analog delay enables processing neural waveforms with reduced overhead compared to a digital delay since it does not requires sampling and digitization. It uses an allpass transfer function for achieving wider constant-delay bandwidth than all-pole does. Two filters realizations are compared for implementing the delay element: the Cascaded structure and the Inverse follow-the-leader feedback filter. Their respective strengths and drawbacks are assessed by modeling parasitics and non-idealities of OTAs, and by transistor-level simulations. A budget of 200 nA is used in both filters. Experimental measurements with the chosen filter topology are presented and discussed. <s> BIB020 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> In this paper, we review the state-of-the-art in neural interface recording architectures. Through this we identify schemes which show the trade-off between data information quality (lossiness), computation (i.e. power and area requirements) and the number of channels. We further extend these tradeoffs by band-limiting the signal through reducing the front-end amplifier bandwidth. We therefore explore the possibility of band-limiting the spectral content of recorded neural signals (to save power) and investigate the effect this has on subsequent processing (spike detection accuracy). We identify the spike detection method most robust to such signals, optimize the threshold levels and modify this to exploit such a strategy. <s> BIB021 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> This paper reports a multi-channel neural recording system-on-chip (SoC) with digital data compression and wireless telemetry. The circuit consists of a 16 amplifiers, an analog time division multiplexer, an 8-bit SAR AD converter, a digital signal processor (DSP) and a wireless narrowband 400-MHz binary FSK transmitter. Even though only 16 amplifiers are present in our current die version, the whole system is designed to work with 64 channels demonstrating the feasibility of a digital processing and narrowband wireless transmission of 64 neural recording channels. A digital data compression, based on the detection of action potentials and storage of correspondent waveforms, allows the use of a 1.25-Mbit/s binary FSK wireless transmission. This moderate bit-rate and a low frequency deviation, Manchester-coded modulation are crucial for exploiting a narrowband wireless link and an efficient embeddable antenna. The chip is realized in a 0.35- em CMOS process with a power consumption of 105 eW per channel (269 eW per channel with an extended transmission range of 4 m) and an area of 3.1 × 2.7 mm2. The transmitted signal is captured by a digital TV tuner and demodulated by a wideband phase-locked loop (PLL), and then sent to a PC via an FPGA module. The system has been tested for electrical specifications and its functionality verified in in-vivo neural recording experiments. <s> BIB022 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> This paper presents a sub-Nyquist rate sampling receiver architecture that exploits signal sparsity by employing compressive sensing (CS) techniques. The receiver serves as an analog-to-information conversion system that works at sampling rates much lower than the Nyquist rate. A parallel-path structure that employs current mode sampling techniques is used. The receiver performance is quantified analytically. Useful and fundamental design guidelines that are unique to CS are provided based on the analytical tools. Simulations with a 90-nm CMOS process verify the theoretical derivations and the circuit implementations. Based on these results, it is shown that an instantaneous receiver signal bandwidth of 1.5 GHz and a signal-to-noise-plus-distortion ratio of 44 dB are achievable. The receiver power consumption is estimated to be 120.8 mW. A comparison with state-of-the-art high-speed analog-to-digital conversions reveals that the proposed approach improves the figure of merit by a factor of three if the signal exhibits a 4% sparsity. <s> BIB023 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field. <s> BIB024 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> This work introduces the use of compressed sensing (CS) algorithms for data compression in wireless sensors to address the energy and telemetry bandwidth constraints common to wireless sensor nodes. Circuit models of both analog and digital implementations of the CS system are presented that enable analysis of the power/performance costs associated with the design space for any potential CS application, including analog-to-information converters (AIC). Results of the analysis show that a digital implementation is significantly more energy-efficient for the wireless sensor space where signals require high gain and medium to high resolutions. The resulting circuit architecture is implemented in a 90 nm CMOS process. Measured power results correlate well with the circuit models, and the test system demonstrates continuous, on-the-fly data processing, resulting in more than an order of magnitude compression for electroencephalography (EEG) signals while consuming only 1.9 μW at 0.6 V for sub-20 kS/s sampling rates. The design and measurement of the proposed architecture is presented in the context of medical sensors, however the tools and insights are generally applicable to any sparse data acquisition. <s> BIB025 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> High-density wireless intracranial neural recording is a promising technology enabling the autonomous diagnosis and therapy of brain diseases. Increasing the number of recording channels is accompanied by the increased amount of data resulting in an unacceptable transmission power. A comprehensive study of possible compressed sensing methods in the context of neural signals has been done, and the compression of signals originating from different channels in the spatial domain has been implemented at the system and circuit levels. Results of the simulations in a UMC 0.18μm CMOS technology and subsequent reconstructions show the possibility of compressing with ratios as high as 2.6 with a recovery SNR of at least 10dB using extremely compact and low-power circuits. The power efficiency and limited area per channel confirm the relevance of the proposed approach for multi-channel high-density neural interfaces. <s> BIB026 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> This paper presents a novel approach to acquire multichannel wireless intracranial neural data based on a compressive sensing scheme. The designed circuits are extremely compact and low-power which confirms the relevance of the proposed approach for multichannel high-density neural interfaces. The proposed compression model enables the acquisition system to record from a large number of channels by reducing the transmission power per channel. Our main contributions are the twofold. First, a CMOS compressive sensing system to realize multichannel intracranial neural recording is described. Second, we explain a joint sparse decoding algorithm to recover the multichannel neural data. The idea has been implemented at system as well as circuit levels. The simulation results reveal that the multichannel intracranial neural data can be acquired by compression ratios as high as four. <s> BIB027 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> This paper introduces an area- and power-efficient approach for compressive recording of cortical signals used in an implantable system prior to transmission. Recent research on compressive sensing has shown promising results for sub-Nyquist sampling of sparse biological signals. Still, any large-scale implementation of this technique faces critical issues caused by the increased hardware intensity. The cost of implementing compressive sensing in a multichannel system in terms of area usage can be significantly higher than a conventional data acquisition system without compression. To tackle this issue, a new multichannel compressive sensing scheme which exploits the spatial sparsity of the signals recorded from the electrodes of the sensor array is proposed. The analysis shows that using this method, the power efficiency is preserved to a great extent while the area overhead is significantly reduced resulting in an improved power-area product. The proposed circuit architecture is implemented in a UMC 0.18 ${\mbi \mu}$ m CMOS technology. Extensive performance analysis and design optimization has been done resulting in a low-noise, compact and power-efficient implementation. The results of simulations and subsequent reconstructions show the possibility of recovering fourfold compressed intracranial EEG signals with an SNR as high as 21.8 dB, while consuming 10.5 ${\mbi \mu}$ W of power within an effective area of 250 ${\mbi \mu}$ m $\, \times \,$ 250 ${\mbi \mu}$ m per channel. <s> BIB028 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> Widely utilized in the field of Neuroscience, implantable neural recording devices could capture neuron activities with an acquisition rate on the order of megabytes per second. In order to efficiently transmit neural signals through wireless channels, these devices require compression methods that reduce power consumption. Although recent Compressed Sensing (CS) approaches have successfully demonstrated their power, their full potential is yet to be explored. Built upon our previous on-chip CS implementation, we propose an energy efficient multi-mode CS framework that focuses on improving the off-chip components, including (i) a two-stage sensing strategy, (ii) a sparsifying dictionary directly using data, (iii) enhanced compression performance from Full Signal CS mode and Spike Restoration mode to Spike CS + Restoration mode and; (iv) extension of our framework to the Tetrode CS recovery using joint sparsity. This new framework achieves energy efficiency, implementation simplicity and system flexibility simultaneously. Extensive experiments are performed on simulation and real datasets. For our Spike CS + Restoration mode, we achieve a compression ratio of 6% with a reconstruction SNDR > 10 dB and a classification accuracy > 95% for synthetic datasets. For real datasets, we get a 10% compression ratio with ~ 10 dB for Spike CS + Restoration mode. <s> BIB029 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> This paper presents a 12-channel, low-power, high efficiency neural signal acquisition front-end for local field potential and action potential signals recording. The proposed neural front-end integrates low noise instrumentation amplifiers, low-power filter stages with configurable gain and cut-off frequencies, a successive approximation register (SAR) ADC, and a realtime compressed sensing processing unit. A capacitor coupled instrumentation amplifier integrated input impedance boosting has been designed, dissipating 1μA quiescent current. An input referred noise of 1.63μV was measured in the frequency band of 1Hz to 7kHz. The noise efficiency factor (NEF) of the amplifier is 0.76. The SAR ADC achieves an ENOB of 10.6-bit at a sampling rate of 1MS/s. A compressed sensing processing unit with configurable compression ratio, up to 8x, was integrated in the design. The design has been fabricated in 180nm CMOS, occupying 4.5mm×1.5mm silicon area. A portable neural recorder has been built with the custom IC and a commercial low-power wireless module. A 4.6g lithium battery supports the device for a continuous compressed sensing recording up to 70 hours. <s> BIB030 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> Objective. This paper describes a low power closed-loop compressive sensing (CS) based neural recording system. This system provides an efficient method to reduce data transmission bandwidth for implantable neural recording devices. By doing so, this technique reduces a majority of system power consumption which is dissipated at data readout interface. The design of the system is scalable and is a viable option for large scale integration of electrodes or recording sites onto a single device. Approach. The entire system consists of an application-specific integrated circuit (ASIC) with 4 recording readout channels with CS circuits, a real time off-chip CS recovery block and a recovery quality evaluation block that provides a closed feedback to adaptively adjust compression rate. Since CS performance is strongly signal dependent, the ASIC has been tested in vivo and with standard public neural databases. Main results. Implemented using efficient digital circuit, this system is able to achieve >10 times data compression on the entire neural spike band (500–6KHz) while consuming only 0.83uW (0.53 V voltage supply) additional digital power per electrode. When only the spikes are desired, the system is able to further compress the detected spikes by around 16 times. Unlike other similar systems, the characteristic spikes and inter-spike data can both be recovered which guarantes a >95% spike classification success rate. The compression circuit occupied 0.11mm2/electrode in a 180nm CMOS process. The complete signal processing circuit consumes <16uW/electrode. Significance. Power and area efficiency demonstrated by the system make it an ideal candidate for integration into large recording arrays containing thousands of electrode. Closed-loop recording and reconstruction performance evaluation further improves the robustness of the compression method, thus making the system more practical for long term recording. <s> BIB031 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> We report the design and implementation of an Analog-to-Information Converter (AIC) based on Compressed Sensing (CS). The system is realized in a CMOS $180~{\rm nm}$ technology and targets the acquisition of bio-signals with Nyquist frequency up to $100~{\rm kHz}$ . To maximize performance and reduce hardware complexity, we co-design hardware together with acquisition and reconstruction algorithms. The resulting AIC outperforms previously proposed solutions mainly thanks to two key features. First, we adopt a novel method to deal with saturations in the computation of CS measurements. This allows no loss in performance even when 60% of measurements saturate. Second, the system is able to adapt itself to the energy distribution of the input by exploiting the so-called rakeness to maximize the amount of information contained in the measurements. <s> BIB032 </s> Multi-Channel Neural Recording Implants: A Review <s> Data Compression <s> Reliable, multi-channel neural recording is critical to the neuroscience research and clinical treatment. However, most hardware development of fully integrated, multi-channel wireless neural recorders to-date, is still in the proof-of-concept stage. To be ready for practical use, the trade-offs between performance, power consumption, device size, robustness, and compatibility need to be carefully taken into account. This paper presents an optimized wireless compressed sensing neural signal recording system. The system takes advantages of both custom integrated circuits and universal compatible wireless solutions. The proposed system includes an implantable wireless system-on-chip (SoC) and an external wireless relay. The SoC integrates 16-channel low-noise neural amplifiers, programmable filters and gain stages, a SAR ADC, a real-time compressed sensing module, and a near field wireless power and data transmission link. The external relay integrates a 32 bit low-power microcontroller with Bluetooth 4.0 wireless module, a programming interface, and an inductive charging unit. The SoC achieves high signal recording quality with minimized power consumption, while reducing the risk of infection from through-skin connectors. The external relay maximizes the compatibility and programmability. The proposed compressed sensing module is highly configurable, featuring a SNDR of 9.78 dB with a compression ratio of 8×. The SoC has been fabricated in a 180 nm standard CMOS technology, occupying 2.1 mm $\times$ 0.6 mm silicon area. A pre-implantable system has been assembled to demonstrate the proposed paradigm. The developed system has been successfully used for long-term wireless neural recording in freely behaving rhesus monkey. <s> BIB033
Increasing the number of channels improves spatial resolution on the one hand, and on the other hand, increases the output data transfer rate and power consumption, especially in the wireless transmitter. One of the methods to decrease the power consumption is to decrease the data transfer rate by reducing the data redundancy, which is called data compression. Since the encoders of the compression methods are implanted on the brain, the method that has a simpler encoder in terms of consuming less area and power is desirable. As mentioned earlier in Section 3, CS is a compressing technique that efficiently acquires and reconstructs a bio-signal, especially EEG and iEEG signals. CS is only used for bio-signals that are sparse in time or other domains. Luckily, most of the bio-signals are sparse in the time, Gabor or wavelet domains BIB005 BIB008 , which makes them suitable to use CS. Data reconstruction can be achieved with far fewer samples compared to the Shannon-Nyquist sampling theorem by utilizing optimization methods and by exploiting the sparsity of the signal. The core of CS encodes an N-dimensional sampled input signal (X) into an M-dimensional sequence of measurement (Y) through a linear transformation by the M × N measurement matrix Φ, where Y = ΦX. In this matrix equation, M is less than N (M < N) that represents the compressing of data from N sample to M sequence. There are an infinite number of feasible solutions for X, as the equation is underdetermined. Assuming X is sparse, the sparsest solution is often the correct solution with high probability. As presented in BIB007 , employing a random measurement matrix Φ, as a universal encoder as well as large enough input samples X is required to perform signal reconstruction of any sparse signal. A general approach to facilitating an efficient circuit implementation of Φ is to utilize a pseudo-random Bernoulli matrix where each entry φ m,n is ±1 BIB009 BIB023 . As shown in Figure 14 , a CS encoder can be implemented in both an analog and a digital domain. Figure 14a demonstrates the block diagram of an analog implementation of a CS encoder presented in BIB028 BIB032 BIB026 BIB027 . The sparsity of the EEG signal in the Gabor domain is utilized in BIB032 and the design in BIB028 BIB026 BIB027 exploits the spatial sparsity of the iEEG signals recorded from the electrodes of the sensor array. The CS core block diagram in the digital domain is shown in Figure 14b . The sparsity of the neural signal in the Gabor domain is utilized in BIB025 BIB029 BIB030 BIB031 BIB019 BIB033 and implements the CS encoder digitally. Another signal compression method, which is suitable for extracellular recordings, is presented in the literature BIB014 BIB020 . This compression method is based on the sparsity of APs in the time domain. Since most of the information corresponding to the extracellular activities that are sensed and captured by the microelectrodes are in the APs, and the duty cycle of the APs is between 2% and 20%, it is sufficient to detect only the APs and transfer them out of the body BIB024 . As the waveform of the APs captured by different neurons in a microelectrode are different, we can exploit the features of these waveforms for the subsequent processes such as spike sorting. These waveform features could be for the time of occurrence and the maximum and minimum amplitude value. Note that the best performance is achieved when complete waveform representations are available BIB001 BIB021 . An analog compression block is usually placed after the neural amplifier. Therefore, the neural signals have a relatively high voltage amplitude. One of the simplest methods for detecting and extracting the location of the APs is using a comparator in order to compare the neural signals with a threshold voltage  BIB010 . The APs are detected while the neural signal cross the voltage threshold. The implementation of the detection circuit is simple in both the analog and digital circuits. However, in this method, we can only capture a portion of the AP that is above the threshold and the rest are neglected. Furthermore, accurate detection of this method is only feasible for high SNR BIB002 . To optimize the detection rate, the threshold voltage is chosen very carefully based on the level of the noise in the channel. This noise consists of background neural noise, flicker noise and thermal noise. Another similar method which is more effective in raising the detection rate is based on exploiting a bilateral threshold to consider both positive and negative signal polarities as presented in BIB014 BIB006 BIB022 BIB011 BIB012 . Saving a signal in a buffer and transmitting it with a very short delay allows for the whole waveshape of the APs to be captured, without missing any portion of them. Employing an SRAM as a data buffering block and the bilateral threshold technique in BIB014 improves the accurate detection rate. In order to improve the SNR and, consequently, to increase the accurate detection rate of the APs, a pre-processor block is used in the designs in  BIB015 . In this method, on one hand the waveform of the neural activities is emphasized and on the other hand the noise is attenuated to increase the SNR. This method also helps to choose the threshold voltage easily to detect the APs accurately. In practice, the implementation methods of the pre-processor in the literature are different. For instance, in  , a pre-processor detector and spike sorting system are presented, which operates based on the variance of the neural signal. Another example is presented in BIB015 , where an energy-based pre-processor is implemented utilizing low-power current-mode circuits. Adaptive threshold is also another method to maximize the detection rate BIB003 BIB016 BIB004 . In this method, the threshold of detection is not a constant value and varies dynamically based on the SNR of the neural signal and the background noise. Moreover, compression and other processes such as spike detection, feature extraction, and spike sorting can be implemented digitally in digital signal processors (DSPs). Refs. BIB013  BIB017 BIB018 provide samples that exploit DSPs to carry out such processes. At the end of this section, it is necessary to mention that the application of these methods are based on the input signals. The CS method is the appropriate method for the EEG and iEEG signals due to its simplicity of implementation and no need to high SNR. However, for the extracellular neural activities which is important to extract the spikes for subsequent processes such as spike sorting, the data compression method based on the threshold is suitable and the adaptive threshold method is proposed.