You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Multi-Channel Neural Recording Implants: A Review 
Chapter Title:  DC-Coupled Neural Amplifiers 
1. Abstract of Cited Paper (BIB001):  We present in this paper a low-power bioamplifier suitable for massive integration in dense multichannel recording devices. This bioamplifier achieves reduced-size compared to previous designs by means of active low-frequency suppression. An active integrator located in the feedback path of a low-noise amplifier is employed for placing a highpass cutoff frequency within the transfer function. A very long integrating time constant is achieved using a small integrated capacitor and a MOS-bipolar equivalent resistor. This configuration rejects unwanted low-frequency contents without the need for input RC networks or large feedback capacitors. Therefore, the bioamplifier high-input impedance and small size are preserved. The bioamplifier, implemented in a 0.18-mum CMOS process, has been designed for neural recording of action potentials, and optimised through a transconductance-ef-ficiency design methodology for micropower operation. Measured performance and results obtained from in vivo recordings are presented. The integrated bioamplifier provides a midband gain of 50 dB, and achieves an input-referred noise of 5.6 muVrms. It occupies less than 0.050 mm2 of chip area and dissipates 8.6 muW. 
2. Abstract of Cited Paper (BIB002):  A crucial and important part of a medical diagnostics system is the monitoring of the biopotential signals. This paper describes a complete low-power EEG acquisition ASIC that is suitable for miniaturized ambulatory EEG measurement systems. The aim is not only to improve the patients' comfort but also to extend the device applications. 
3. Abstract of Cited Paper (BIB003):  A wirelessly powered 0.125 mm2 65 nm CMOS IC for Brain-Machine Interface applications integrates four 1.5 μW amplifiers (6.5 μVrms input-referred noise with 10 kHz bandwidth) with power conditioning and communication circuitry. The multi-node backscatter frequency locks to a wireless interrogator using a frequency-domain multiple access communication scheme. The full system, verified with wirelessly powered in vivo recordings, consumes 10.5 μW and operates at 1 mm range in air with 50 mW transmit power. 
4. Abstract of Cited Paper (BIB004):  We review integrated circuits for low-frequency noise and offset rejection as a motivation for the presented digitally-assisted neural amplifier design methodology. Conventional AC-coupled neural amplifiers inherently reject input DC offset but have key limitations in area, linearity, DC drift, and spectral accuracy. Their chopper stabilization reduces low-frequency intrinsic noise at the cost of degraded area, input impedance and design complexity. DC-coupled implementations with digital high-pass filtering yield improved area, linearity, drift, and spectral accuracy and are inherently suitable for simple chopper stabilization. As a design example, a 56-channel 0.13 μm CMOS intracranial EEG interface is presented. DC offset of up to ±50 mV is rejected by a digital low-pass filter and a 16-bit delta-sigma DAC feeding back into the folding node of a folded-cascode LNA with CMRR of 65 dB. A bank of seven column-parallel fully differential SAR ADCs with ENOB of 6.6 are freely moving ratsshared among 56 channels resulting in 0.018 mm ::: 2 ::: effective channel area. Compensation-free direct input chopping yields integrated input-referred noise of 4.2 μV ::: rms ::: over the bandwidth of 1 Hz to 1 kHz. The 8.7 mm ::: 2 ::: chip dissipating 1.07 mW has been validated in vivo in online intracranial EEG monitoring in freely moving rats.

Literature Review:
The schematic shown in Figure 4a is the first neural amplifier that uses an LPF in the feedback path which is based on the block diagram of Figure 2 BIB002 . The high-cutoff frequency of this architecture is also realized by the frequency response of OTA 1 and its midband gain is obtained from the DC gain of this OTA. As a result of this feature, no large capacitor ratio is required to obtain high midband gains. However, the midband gain is affected by strong process variations. Utilizing an integrator as an active LPF in the feedback path results in a lower area since a smaller capacitor is required due to the Miller effect compared to the structures that utilize passive LPF. However, to reduce the IRN, the capacitors C I and C L should be increased, which results in additional area. In addition, this topology consumes huge amount of power in OTA 2 (feedback path) which in turn reduces the NEF in this topology. A single ended configuration is utilized in Figure 4a . This topology has a lower CMRR and PSRR compared to a fully differential configuration; however, it occupies a lower area, since it only has one Miller capacitor. Moreover, the resistance of the pseudoresistor shown in Figure 4a varies due to the high voltage swing at the output and, consequently, alters the high-pass pole. The variation in the open-loop gain of the OTA 1 can also change the high-pass pole. A DC-coupled neural amplifier topology similar to Figure 4a is shown in Figure 4b BIB001 . Unlike Figure 4a , the architecture in Figure 4b utilizes a fully deferential architecture and a passive LPF. Since this architecture does not exploit the miller effect, a huge feedback capacitor is used to obtain a high-pass pole at low frequencies. Passive off-chip elements are used in the architecture of BIB001 that is not suitable for multi-channel and implantable applications. Since no OTA is utilized in the feedback path, the noise and NEF are reduced at the cost of a huge feedback capacitor. Note that the feedback capacitor is implemented off chip, due to the high area. Figure 4c shows a neural amplifier that utilizes the block diagram of Figure 2 as well to remove the DC offset of the input signal BIB003 . However, in this method, the currents are compared in the input instead of voltages, unlike the two previous topologies. This figure shows an AC-coupled chopper stabilized instrumentation amplifier (ACCIA), which utilizes a current balancing instrumentation amplifier (CBIA) block. The coarse-fine servoloop is implemented using a coarse transconductance (CGM), a fine transconductance (FGM), and an integrator as shown in Figure 4c . In BIB003 , the authors have minimized the output range of analog fine servo by designing the coarse servo digitally. With this technique, the power consumption of the fine servo is reduced and the power-noise performance of the ACCIA is improved when compared to the previous ACCIA reported by the same group in  . This design also uses a CHS technique to reduce noise. However, this design still consumes a large area and has high power consumption, which is not attractive for multi-channel applications. The idea of utilizing a digital LPF instead of analog in the feedback path of Figure 2 in order to avoid utilizing huge capacitors and high-power OTA is proposed in  BIB004 and shown in Figure 4d . In this method, the low-cutoff frequency can be determined more accurately. Also, it can result in lower area and less power consumption in advanced technologies, and a comparison between the input signal and the feedback signal is performed by utilizing a DAC and an array of transistors in a differential pair. This structure modulates and changes the width of the transistor based on the offset voltage to maintain constant IRN and CMRR. Nevertheless, measurement results show that these parameters vary with the offset voltage variation. The digital LPF in  is designed off chip. The off-chip implementation of the filter creates an undesired delay to the low-frequency signal path. This limits the order of the filter to first order and makes it difficult to stabilize the feedback loop. In BIB004 , a digital LPF is implemented on chip for four channels to eliminate the delay issue. The low cut-off frequency of this design can be programmed down to 40 Hz. The authors in BIB005 also exploit a digital on-chip filter to implement a 56 channels neural recording implant. In this design, comparing the input signal and the feedback signal is performed directly in the currents passing through the differential pair with a current steering I-DAC. This design uses a CHS to reduce the flicker noise, but due to the high power consumption of the blocks and relatively high bandwidth (10 Hz-5 KHz) compared to f C ( 100 Hz, flicker noise corner frequency), a high NEF (=7) is observed. Also, in addition to the complexity of this design, the midband gain is very sensitive to the technology process deviation.