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1/f noise is a critical figure of merit for the performance of transistors and circuits. For two-dimensional devices (2D-FETs), and especially for applications in the GHz range where short-channel FETs are required, velocity saturation (VS) effect can result in the reduction of 1/f noise at high longitudinal electric fields. A new physics-based compact model is for the first time introduced for single- to few- layer 2D-FETs in this study, precisely validating 1/f noise experiments for various types of devices. The proposed model mainly accounts for the measured 1/f noise bias dependence as the latter is defined by different physical mechanisms. Thus, analytical expressions are derived, valid in all regions of operation in contrast to conventional approaches available in literature so far, accounting for carrier number fluctuation (DN), mobility fluctuation (Dmu}) and contact resistance (DR) effects based on the underlying physics that rules these devices. DN mechanism due to trapping/detrapping together with an intense Coulomb scattering effect, dominates 1/f noise from medium to strong accumulation region while Dmu, is also demonstrated to modestly contribute in subthreshold region. DR can also be significant in very high carrier density. The VS induced reduction of 1/f noise measurements at high electric fields, is also remarkably captured by the model. The physical validity of the model can also assist in extracting credible conclusions when conducting comparisons between experimental data from devices with different materials or dielectrics., Nanoscale, 2023, Advance Article

Neuroelectronic interfaces with the nervous system are an essential technology in state-of-the-art neuroscience research aiming to uncover the fundamental working mechanisms of the brain. Progress towards increased spatio-temporal resolution has been tightly linked to the advance of microelectronics technology and novel materials. Translation of these technologies to neuroscience has resulted in multichannel neural probes and acquisition systems enabling the recording of brain signals using thousands of channels. This review provides an overview of state-of-the-art neuroelectronic technologies, with emphasis on recording site architectures which enable the implementation of addressable arrays for high-channel-count neural interfaces. In this field, active transduction mechanisms are gaining importance fueled by novel materials, as they facilitate the implementation of high density addressable arrays., This work has been funded by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 732032(BrainCom) and Grant Agreement No. 881603 (Graphene Flagship) and co-funded by the European Regional Development Funds (ERDF) allocated to the Programa operatiu FEDER de Catalunya 2014–2020, with the support of the Secretaria d’Universitats i Recerca of the Departament d’Empresa i Coneixement of the Generalitat de Catalunya for emerging technology clusters devoted to the valorization and transfer of research results (GraphCAT 001-P-001702). The ICN2 is supported by the Severo Ochoa Centres of Excellence program, funded by the Spanish Research Agency (AEI, grant no. SEV-2017-0706), and by the CERCA Program/Generalitat de Catalunya.

Neuroprosthetic technology aims to restore nervous system functionality in cases of severe damage or degeneration by recording and stimulating the electrical activity of the neural tissue. One of the key factors determining the quality of the neuroprostheses is the electrode material used to establish electrical communication with the neural tissue, which is subject to strict electrical, electrochemical, and mechanical specifications as well as biological and microfabrication compatibility requirements. This work presents a nanoporous graphene-based thin film technology and its engineering to form flexible neural implants. Bench measurements show that the developed microelectrodes offer low impedance and high charge injection capacity throughout millions of pulses. In vivo electrode performance was assessed in rodents both from brain surface and intracortically showing high-fidelity recording performance, while stimulation performance was assessed with an intrafascicular implant that demonstrated low current thresholds and high selectivity for activating subsets of axons within the sciatic nerve. Furthermore, the tissue biocompatibility of the devices was validated by chronic epicortical and intraneural implantation. Overall, this works describes a novel graphene-based thin film microelectrode technology and demonstrates its potential for high-precision neural interfacing in both recording and stimulation applications.

There is growing interest in examining oscillations and brain signals outside traditional EEG bands (0.3–80 Hz), as these regimes contain useful electrographic biomarkers for the diagnosis, monitoring..., R.W. is funded by a Senior Research Fellowship awarded by the Worshipful Company of Pewterers. This work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 881603 (GrapheneCore3).

This paper presents a 1024-channel neural read-out integrated circuit (ROIC) for solution-gated GFET sensing probes in massive muECoG brain mapping. The proposed time-domain multiplexing of GFET-only arrays enables low-cost and scalable hybrid headstages. Low-power CMOS circuits are presented for the GFET analog frontend, including a CDS mechanism to improve preamplifier noise figures and 10-bit 10-kS/s A/D conversion. The 1024-channel ROIC has been fabricated in a standard 1.8-V 0.18-mum CMOS technology with 0.012 mm 2 and 36 mu W per channel. An automated methodology for the in-situ calibration of each GFET sensor is also proposed. Experimental ROIC tests are reported using a custom FPGA-based muECoG headstage with 16times 32 and 32times 32 GFET probes in saline solution and agar substrate. Compared to state-of-art neural ROICs, this work achieves the largest scalability in hybrid platforms and it allows the recording of infra-slow neural signals.

Objective. The development of experimental methodology utilizing graphene micro-transistor arrays to facilitate and advance translational research into cortical spreading depression (CSD) in the awake brain. Approach. CSDs were reliably induced in awake nontransgenic mice using optogenetic methods. High-fidelity DC-coupled electrophysiological mapping of propagating CSDs was obtained using flexible arrays of graphene soultion-gated field-effect transistors (gSGFETs). Main results. Viral vectors targetted channelrhopsin expression in neurons of the motor cortex resulting in a transduction volume 1 mm3. 5-10 s of continous blue light stimulation induced CSD that propagated across the cortex at a velocity of 3.0 0.1 mm min-1. Graphene micro-transistor arrays enabled high-density mapping of infraslow activity correlated with neuronal activity suppression across multiple frequency bands during both CSD initiation and propagation. Localized differences in the CSD waveform could be detected and categorized into distinct clusters demonstrating the spatial resolution advantages of DC-coupled recordings. We exploited the reliable and repeatable induction of CSDs using this preparation to perform proof-of-principle pharmacological interrogation studies using NMDA antagonists. MK801 (3 mg kg-1) suppressed CSD induction and propagation, an effect mirrored, albeit transiently, by ketamine (15 mg kg-1), thus demonstrating this models' applicability as a preclinical drug screening platform. Finally, we report that CSDs could be detected through the skull using graphene micro-transistors, highlighting additional advantages and future applications of this technology. Significance. CSD is thought to contribute to the pathophysiology of several neurological diseases. CSD research will benefit from technological advances that permit high density electrophysiological mapping of the CSD waveform and propagation across the cortex. We report an in vivo assay that permits minimally invasive optogenetic induction, combined with multichannel DC-coupled recordings enabled by gSGFETs in the awake brain. Adoption of this technological approach could facilitate and transform preclinical investigations of CSD in disease relevant models., This work has been funded by the European Union's Horizon 2020 research and innovation programme under Grant Agreement Nos. 785219 and 881603 (Graphene Flagship) and co-funded by the European Regional Development Funds (ERDF) allocated to the Programa operatiu FEDER de Catalunya 2014–2020, with the support of the Secretaria d'Universitats i Recerca of the Departament d'Empresa i Coneixement of the Generalitat de Catalunya for emerging technology clusters devoted to the valorization and transfer of research results (GraphCAT 001-P-001702). RW is funded by a Senior Research Fellowship awarded by the Worshipful Company of Pewterers. EMC was awarded an EMBO Short-Term Fellowship ASTF 8157 to spend time in RW's lab. DR is a Biotechnology and Biological Sciences Research Council (BBSRC) LIDo sponsored PhD student. JAG and EDC acknowledge the Ministerio de Ciencia, Innovación y Universidades, la Agencia Estatal de Investigación (AEI) y el Fondo Europeo de Desarrollo Regional (FEDER/UE) for the FIS2017-85787-R research project. This work has made use of the Spanish ICTS Network MICRONANOFABS partially supported by MICINN and the ICTS 'NANBIOSIS', more specifically by the Micro-NanoTechnology Unit of the CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN) at the IMB-CNM. Equipment used was partially funded by Fondo Europeo de Desarrollo Regional (FEDER/UE) FICTS14/20-2-23. IMF is funded by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 665919.

Organic electrochemical transistors (OECTs) from poly(3,4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS) are used as amplifying transducers for bioelectronics. Although the impact on performance of device geometry parameters such as channel area and thickness has been widely explored, the overlap between the semiconductor film and the source and drain contacts has not been considered. Here we vary this overlap and explore its impact on transconductance and noise. We show that increasing contact overlap does not alter the magnitude of the steady-state transconductance but it does decreases the cut-off frequency. Noise was found to be independent of contact overlap and to vary according to the charge noise model. The results show that high-quality contacts can be established in PEDOT:PSS OECTs with minimal overlap.

Graphene active sensors have demonstrated promising capabilities for the detection of electrophysiological signals in the brain. Their functional properties, together with their flexibility as well as their expected stability and biocompatibility have raised them as a promising building block for large-scale sensing neural interfaces. However, in order to provide reliable tools for neuroscience and biomedical engineering applications, the maturity of this technology must be thoroughly studied. Here, we evaluate the performance of 64-channel graphene sensor arrays in terms of homogeneity, sensitivity and stability using a wireless, quasi-commercial headstage and demonstrate the biocompatibility of epicortical graphene chronic implants. Furthermore, to illustrate the potential of the technology to detect cortical signals from infra-slow to high-gamma frequency bands, we perform proof-of-concept long-term wireless recording in a freely behaving rodent. Our work demonstrates the maturity of the graphene-based technology, which represents a promising candidate for chronic, wide frequency band neural sensing interfaces., This work has been funded by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 732032 (BrainCom) and Graphene Flagship Grant Agreements No. 785219 (GrapheneCore2) and 881603 (GrapheneCore3). The ICN2 is supported by the Severo Ochoa Centres of Excellence program, funded by the Spanish Research Agency (AEI, grant no. SEV-2017-0706), and by the CERCA Program/Generalitat de Catalunya. R.G.C. is supported by the International Ph.D Program La Caixa-Severo Ochoa (Programa Internacional de Becas “la Caixa”-Severo Ochoa). This work has made use of the Spanish ICTS Network MICRONANOFABS partially supported by MICINN and the ICTS “NANBIOSIS”, more specifically by the Micro-NanoTechnology Unit of the CIBER in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN) at the IMB-CNM. This work is within the project FIS2017-85787-R funded by the “Ministerio de Ciencia, Innovación y Universidades” of Spain, the “Agencia Estatal de Investigación (AEI)”, and the “Fondo Europeo de Desarrollo Regional (FEDER/UE)”. A.S. and G.S. were also supported by Bundesministerium für Bildung und Forschung [grant number 01GQ0440]. R.G.C. acknowledges that this work has been done in the framework of the Ph.D in Electrical and Telecommunication Engineering at the Universitat Autònoma de Barcelona.

Mapping the entire frequency bandwidth of brain electrophysiological signals is of paramount importance for understanding physiological and pathological states. The ability to record simultaneously DC-shifts, infraslow oscillations (, This work has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 881603 (GrapheneCore3). ICN2 is supported by the Severo Ochoa Centres of Excellence programme, funded by the Spanish Research Agency (AEI, grant no. SEV-2017–0706), and by the CERCA Programme/Generalitat de Catalunya. A.B.C. is supported by the International PhD Programme La Caixa-Severo Ochoa (Programa Internacional de Becas ‘la Caixa’-Severo Ochoa). This work has made use of the Spanish ICTS Network MICRONANOFABS, partially supported by MICINN and the ICTS ‘NANBIOSIS’, more specifically by the Micro-NanoTechnology Unit of the CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN) at the IMB-CNM. We also acknowledge funding from the Generalitat de Catalunya (2017 SGR 1426), and the 2DTecBio project (FIS2017-85787-R) funded by the Ministerio de Ciencia, Innovación y Universidades of Spain, the Agencia Estatal de Investigación (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER/UE). Part of this work was co-funded by the European Regional Development Funds (ERDF) allocated to the Programa operatiu FEDER de Catalunya 2014–2020, with the support of the Secretaria d’Universitats i Recerca of the Departament d’Empresa i Coneixement of the Generalitat de Catalunya for emerging technology clusters devoted to the valorization and transfer of research results (GraphCAT 001-P-001702). A.B.C. acknowledges that this work has been carried out within the framework of a PhD in Electrical and Telecommunication Engineering at the Universitat Autònoma de Barcelona. R.C.W. is funded by a Senior Research Fellowship awarded by the Worshipful Company of Pewterers. D.R. is a Biotechnology and Biological Sciences Research Council (BBSRC) LIDo sponsored PhD student. We thank M. Walker and L. Lemieux (UCL Queen Square Institute of Neurology) for their comments on the manuscript.

Poor metal contact interfaces are one of the main limitations preventing unhampered access to the full potential of two-dimensional materials in electronics. Here we present graphene solution-gated field-effect-transistors (gSGFETs) with strongly improved linearity, homogeneity and sensitivity for small sensor sizes, resulting from ultraviolet ozone (UVO) contact treatment. The contribution of channel and contact region to the total device conductivity and flicker noise is explored experimentally and explained with a theoretical model. Finally, in-vitro recordings of flexible microelectrocorticography (μ-ECoG) probes were performed to validate the superior sensitivity of the UVO-treated gSGFET to brain-like activity. These results connote an important step towards the fabrication of high-density gSGFET μ-ECoG arrays with state-of-the-art sensitivity and homogeneity, thus demonstrating the potential of this technology as a versatile platform for the new generation of neural interfaces.

Low-frequency noise (LFN) variability in graphene transistors (GFETs) is for the first time researched in this work under both experimental and theoretical aspects. LFN from an adequate statistical sample of long-channel solution-gated single-layer GFETs is measured in a wide range of operating conditions while a physics-based analytical model is derived that accounts for the bias dependence of LFN variance with remarkable performance. LFN deviations in GFETs stem from the variations of the parameters of the physical mechanisms that generate LFN, which are the number of traps (Ntr) for the carrier number fluctuation effect (ΔN) due to trapping/detrapping process and the Hooge parameter (αH) for the mobility fluctuations effect (Δμ). ΔN accounts for an M-shape of normalized LFN variance versus gate bias with a minimum at the charge neutrality point (CNP) as it was the case for normalized LFN mean value while Δμ contributes only near the CNP for both variance and mean value. Trap statistical nature of the devices under test is experimentally shown to differ from classical Poisson distribution noticed at silicon-oxide devices, and this might be caused both by the electrolyte interface in GFETs under study and by the premature stage of the GFET technology development which could permit external factors to influence the performance. This not fully advanced GFET process growth might also cause pivotal inconsistencies affecting the scaling laws in GFETs of the same process., This work was funded by the European Union's Horizon 2020 research and innovation program under Grant Agreement No. GrapheneCore2 785219 and No. GrapheneCore3 881603, Marie Skłodowska-Curie Grant Agreement No. 665919 and Grant Agreement No. 732032 (BrainCom). We also acknowledge financial support by Spanish government under the projects TEC2015-67462-C2-1-R, RTI2018-097876-B-C21 (MCIU/AEI/FEDER, UE) and project 001-P-001702-GraphCat: Communitat Emergent de grafè a Catalunya, co-funded by FEDER within the framework of Programa Operatiu FEDER de Catalunya 2014–2020. The ICN2 is also supported by the Severo Ochoa Centres of Excellence programme, funded by the Spanish Research Agency (AEI, grant no. SEV-2017-0706).

Sensor arrays used to detect electrophysiological signals from the brain are paramount in neuroscience. However, the number of sensors that can be interfaced with macroscopic data acquisition systems currently limits their bandwidth. This bottleneck originates in the fact that, typically, sensors are addressed individually, requiring a connection for each of them. Herein, we present the concept of frequency-division multiplexing (FDM) of neural signals by graphene sensors. We demonstrate the high performance of graphene transistors as mixers to perform amplitude modulation (AM) of neural signals in situ, which is used to transmit multiple signals through a shared metal line. This technology eliminates the need for switches, remarkably simplifying the technical complexity of state-of-the-art multiplexed neural probes. Besides, the scalability of FDM graphene neural probes has been thoroughly evaluated and their sensitivity demonstrated in vivo. Using this technology, we envision a new generation of high-count conformal neural probes for high bandwidth brain machine interfaces., This work has been funded by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement 732032 (BrainCom), Grant Agreement 85219 and 881603 (Graphene Flagship). The ICN2 is supported by the Severo Ochoa Centres of Excellence programme, funded by the Spanish Research Agency (AEI, Grant SEV-2017-0706), and by the CERCA Programme/Generalitat de Catalunya. R.G.C. and N.S. acknowledge that this work has been done in the framework of the Ph.D. in Electrical and Telecommunication Engineering at the Universitat Autonoma de Barcelona. R.G.C is supported by the International Ph.D. Programme La Caixa - Severo Ochoa (Programa Internacional de Becas “la Caixa”-Severo Ochoa). This work has made use of the Spanish ICTS Network MICRONANOFABS partially supported by MICINN and the ICTS “NANBIOSIS”, more specifically by the Micro-NanoTechnology Unit of the CIBER in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN) at the IMB-CNM. This work is within the project 2DTecBio (FIS2017-85787-R) funded by the “Ministerio de Ciencia, Innovacion y Universidades” of Spain, the “Agencia Estatal de Investigacion (AEI)”, and the “Fondo Europeo de Desarrollo Regional (FEDER/UE)” and has received funding from Generalitat de Cataluña 2017 SGR 1426.

Altres ajuts: this work has made use of the Spanish ICTS Network MICRONANOFABS partially supported by MICINN and the ICTS 'NANBIOSIS', more specifically by the Micro-NanoTechnology Unit of the CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBERBBN) at the IMB-CNM. Electrocorticography (ECoG) is a well-established technique to monitor electrophysiological activity from the surface of the brain and has proved crucial for the current generation of neural prostheses and brain-computer interfaces. However, existing ECoG technologies still fail to provide the resolution necessary to accurately map highly localized activity across large brain areas, due to the rapidly increasing size of connector footprint with sensor count. This work demonstrates the use of a flexible array of graphene solution-gated field-effect transistors (gSGFET), exploring the concept of multiplexed readout using an external switching matrix. This approach does not only allow for an increased sensor count, but due to the use of active sensing devices (i.e. transistors) over microelectrodes it makes additional buffer transistors redundant, which drastically eases the complexity of device fabrication on flexible substrates. The presented results pave the way for upscaling the gSGFET technology towards large-scale, high-density μECoG-arrays, eventually capable of resolving neural activity down to a single neuron level, while simultaneously mapping large brain regions.

The use of graphene transistors for transducing neural activity has demonstrated the potential to extend the spatiotemporal resolution of electrophysiological methods to lower frequencies, providing a new tool to understand the role of the infra-slow activity.

Technologically useful and robust graphene-based interfaces for devices require the introduction of highly selective, stable, and covalently bonded functionalities on the graphene surface, whilst essentially retaining the electronic properties of the pristine layer. This work demonstrates that highly controlled, ultrahigh vacuum covalent chemical functionalization of graphene sheets with a thiol-terminated molecule provides a robust and tunable platform for the development of hybrid nanostructures in different environments. We employ this facile strategy to covalently couple two representative systems of broad interest: metal nanoparticles, via S–metal bonds, and thiol-modified DNA aptamers, via disulfide bridges. Both systems, which have been characterized by a multitechnique approach, remain firmly anchored to the graphene surface even after several washing cycles. Atomic force microscopy images demonstrate that the conjugated aptamer retains the functionality required to recognize a target protein. This methodology opens a new route to the integration of high-quality graphene layers into diverse technological platforms, including plasmonics, optoelectronics, or biosensing. With respect to the latter, the viability of a thiol-functionalized chemical vapor deposition graphene-based solution-gated field-effect transistor array was assessed., This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 696656 (Graphene Flagship-core 1) and no 785219 (Graphene Flagship −core 2); UE FP7 ideas: ERC (grant ERC-2013-SYG-610256 Nanocosmos) and Spanish MINECO grants MAT2014-54231-C4-1-P, MAT2014-54231-C4-4-P, MAT2017-85089-C2-1-R, MAT2014-59772-C2-2-P, and BIO2016-79618-R (funded by EU under the FEDER programme), as well as the Nanoavansens program from the Community of Madrid (S2013/MIT-3029). This work has made use of the Spanish ICTS Network MICRONANOFABS partially supported by MINECO and also the ICTS NANBIOSIS, more specifically the Micro-Nano Technology Unit of the CIBER in Bioengineering, Biomaterials & Nanomedicine (CIBER-BBN) at the IMB-CNM. We are grateful to Matthias Muntwiler for his assistance with experiments in the PEARL beamline in the SLS facility. Finally, we acknowledge the TEM and ICP services at the CNB and ICMM institutes, respectively. CSS acknowledges the MINECO for a Juan de la Cierva Incorporación grant (IJCI-2014-19291). M. Marciello is grateful to the Comunidad de Madrid (CM) and European Social Fund (ESF) for supporting her research work through the I+D Collaborative Programme in Biomedicine NIETO-CM (B2017-BMD3731).

6 Figuras, Developing new standardized tools to characterize brain recording devices is critical to evaluate neural probes and for translation to clinical use. The signal-to-noise ratio (SNR) measurement is the gold standard for quantifying the performance of brain recording devices. Given the drawbacks with the SNR measure, our first objective was to devise a new method to calculate the SNR of neural signals to distinguish signal from noise. Our second objective was to apply this new SNR method to evaluate electrodes of three different materials (platinum black, Pt; carbon nanotubes, CNTs; and gold, Au) co-localized in tritrodes to record from the same cortical area using specifically designed multielectrode arrays. Hence, we devised an approach to calculate SNR at different frequencies based on the features of cortical slow oscillations (SO). Since SO consist in the alternation of silent periods (Down states) and active periods (Up states) of neuronal activity, we used these as noise and signal, respectively. The spectral SNR was computed as the power spectral density (PSD) of Up states (signal) divided by the PSD of Down states (noise). We found that Pt and CNTs electrodes have better recording performance than Au electrodes for the explored frequency range (5-1500 Hz). Together with two proposed SNR estimators for the lower and upper frequency limits, these results substantiate our SNR calculation at different frequency bands. Our results provide a new validated SNR measure that provides rich information of the performance of recording devices at different brain activity frequency bands (, This work was supported by Ministerio de Ciencia, Innovación y Universidades (Spain), BFU2017-85048-R and PCIN-2015-162-C02-01 (FLAG ERA) to MVSV, and by CERCA Programme/Generalitat de Catalunya.

Brain-computer interfaces and neural prostheses based on the detection of electrocorticography (ECoG) signals are rapidly growing fields of research. Several technologies are currently competing to be the first to reach the market; however, none of them fulfill yet all the requirements of the ideal interface with neurons. Thanks to its biocompatibility, low dimensionality, mechanical flexibility, and electronic properties, graphene is one of the most promising material candidates for neural interfacing. After discussing the operation of graphene solution-gated field-effect transistors (SGFET) and characterizing their performance in saline solution, it is reported here that this technology is suitable for μ-ECoG recordings through studies of spontaneous slow-wave activity, sensory-evoked responses on the visual and auditory cortices, and synchronous activity in a rat model of epilepsy. An in-depth comparison of the signal-to-noise ratio of graphene SGFETs with that of platinum black electrodes confirms that graphene SGFET technology is approaching the performance of state-of-the art neural technologies.

Recording infraslow brain signals (, This work was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 696656 (Graphene Flagship) and no. 732032 (BrainCom). This work has made use of the Spanish ICTS Network MICRONANOFABS partially supported by MINECO and the ICTS ‘NANBIOSIS’, more specifically by the Micro-NanoTechnology Unit of the CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN) at the IMB-CNM. E.M.C. acknowledges that this work has been done in the framework of the PhD in Electrical and Telecommunication Engineering at the Universitat Autònoma de Barcelona. E..C. thanks the Spanish Ministerio de Economía y Competitividad for the Juan de la Cierva postdoctoral grant IJCI-2015–25201. T. Durduran acknowledges support from Fundació CELLEX Barcelona, Ministerio de Economía y Competitividad /FEDER (PHOTODEMENTIA, DPI2015–64358-C2–1-R), the “Severo Ochoa” Programme for Centres of Excellence in R&D (SEV-2015–0522) and the Obra Social “la Caixa” Foundation (LlumMedBcn). M.V.S.V. acknowledges support from MINECO BFU2017-85048-R. ICN2 is supported by the Severo Ochoa programme fromSpanish MINECO (grant no. SEV-2017-0706).

arXiv:1611.05693v1.-- et al., Establishing a reliable communication interface between the brain and electronic devices is of paramount importance for exploiting the full potential of neural prostheses. Current microelectrode technologies for recording electrical activity, however, evidence important shortcomings, e.g. challenging high density integration. Solution-gated field-effect transistors (SGFETs), on the other hand, could overcome these shortcomings if a suitable transistor material were available. Graphene is particularly attractive due to its biocompatibility, chemical stability, flexibility, low intrinsic electronic noise and high charge carrier mobilities. Here, we report on the use of an array of flexible graphene SGFETs for recording spontaneous slow waves, as well as visually evoked and also pre-epileptic activity in vivo in rats. The flexible array of graphene SGFETs allows mapping brain electrical activity with excellent signal-to-noise ratio (SNR), suggesting that this technology could lay the foundation for a future generation of in vivo recording implants., JAG, BMB, and SD acknowledge support by the German Research Foundation (DFG) in the framework of the Priority Program 1459 Graphene, the Nanosystems Initiative Munich (NIM), and the Graphene Flagship (Contract No. 604391). JAG, BMB, SD, LR, AH and OK acknowledge support by the European Union under the NeuroCare FP7 project (Grant Agreement 280433). NTC, JW, and MVS-V acknowledge support by the EU FF7 FET CORTICONIC contract 600806. BMB, NTC, AGB, JW, SD, RV, MVS-V and JAG received funding for this project from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 696656. ICN2 acknowledges support from the Severo Ochoa Program (MINECO, Grant SEV-2013-0295).

This paper presents a 1024-channel ROIC for brain massive digital μECoG, which uses liquid-gate GFETs as active sensors. The proposed channel multiplexing technique at the recording array level allows strong reductions of the sensor-to-circuit connectivity, low-cost hybrid integration and low-power operation. A mixed-signal ROIC architecture and CMOS circuit solutions are introduced to compensate for GFET mismatching and for the preamplifier noise figure loss when upscaling the number of recording sites. The 1024-channel 10-bit 5-kHz 36-μ $W$ single-chip design is currently being integrated in 0.18-μm CMOS technology and post-layout simulation results are reported.

Graphene active sensors have demonstrated promising capabilities for the detection of electrophysiological signals in the brain. Their functional properties, together with their flexibility as well as their expected stability and biocompatibility have raised them as a promising building block for large-scale sensing neural interfaces. However, in order to provide reliable tools for neuroscience and biomedical engineering applications, the maturity of this technology must be thoroughly studied. Here, we evaluate the performance of 64-channel graphene sensor arrays in terms of homogeneity, sensitivity and stability using a wireless, quasi-commercial headstage and demonstrate the biocompatibility of epicortical graphene chronic implants. Furthermore, to illustrate the potential of the technology to detect cortical signals from infra-slow to high-gamma frequency bands, we perform proof-of-concept long-term wireless recording in a freely behaving rodent. Our work demonstrates the maturity of the graphene-based technology, which represents a promising candidate for chronic, wide frequency band neural sensing interfaces.