High-density Electrode Arrays Research Articles

Micro-Fabrication of Components for a High-Density Sub-Retinal Visual Prosthesis.

We present a retrospective of unique micro-fabrication problems and solutions that were encountered through over 10 years of retinal prosthesis product development, first for the Boston Retinal Implant Project initiated at the Massachusetts Institute of Technology and at Harvard Medical School’s teaching hospital, the Massachusetts Eye and Ear—and later at the startup company Bionic Eye Technologies, by some of the same personnel. These efforts culminated in the fabrication and assembly of 256+ channel visual prosthesis devices having flexible multi-electrode arrays that were successfully implanted sub-retinally in mini-pig animal models as part of our pre-clinical testing program. We report on the processing of the flexible multi-layered, planar and penetrating high-density electrode arrays, surgical tools for sub-retinal implantation, and other parts such as coil supports that facilitated the implantation of the peri-ocular device components. We begin with an overview of the implantable portion of our visual prosthesis system design, and describe in detail the micro-fabrication methods for creating the parts of our system that were assembled outside of our hermetically-sealed electronics package. We also note the unique surgical challenges that sub-retinal implantation of our micro-fabricated components presented, and how some of those issues were addressed through design, materials selection, and fabrication approaches.

Differential modulation of NREM sleep regulation and EEG topography by chronic sleep restriction in mice

Compensatory elevation in NREM sleep EEG delta power has been typically observed following prolonged wakefulness and widely used as a sleep homeostasis indicator. However, recent evidence in human and rodent chronic sleep restriction (CSR) studies suggests that NREM delta power is not progressively increased despite of accumulated sleep loss over days. In addition, there has been little progress in understanding how sleep EEG in different brain regions responds to CSR. Using novel high-density EEG electrode arrays in the mouse model of CSR where mice underwent 18-h sleep deprivation per day for 5 consecutive days, we performed an extensive analysis of topographical NREM sleep EEG responses to the CSR condition, including period-amplitude analysis of individual slow waves. As previously reported in our analysis of REM sleep responses, we found different patterns of changes: (i) progressive decrease in NREM sleep duration and consolidation, (ii) persistent enhancement in NREM delta power especially in the frontal and parietal regions, and (iii) progressive increases in individual slow wave slope and frontal fast oscillation power. These results suggest that multiple sleep-wake regulatory systems exist in a brain region-specific manner, which can be modulated independently, especially in the CSR condition.

Time-Variant Linear Discriminant Analysis Improves Hand Gesture and Finger Movement Decoding for Invasive Brain-Computer Interfaces.

Invasive brain-computer interfaces yield remarkable performance in a multitude of applications. For classification experiments, high-gamma bandpower features and linear discriminant analysis (LDA) are commonly used due to simplicity and robustness. However, LDA is inherently static and not suited to account for transient information that is typically present in high-gamma features. To resolve this issue, we here present an extension of LDA to the time-variant feature space. We call this method time-variant linear discriminant analysis (TVLDA). It intrinsically provides a feature reduction stage, which makes external approaches thereto obsolete, such as feature selection techniques or common spatial patterns (CSPs). As well, we propose a time-domain whitening stage which equalizes the pronounced 1/f-shape of the typical brain-wave spectrum. We evaluated our proposed architecture based on recordings from 15 epilepsy patients with temporarily implanted subdural grids, who participated in additional research experiments besides clinical treatment. The experiments featured two different motor tasks involving three high-level gestures and individual finger movement. We used log-transformed bandpower features from the high-gamma band (50–300 Hz, excluding power-line harmonics) for classification. On average, whitening improved the classification performance by about 11%. On whitened data, TVLDA outperformed LDA with feature selection by 11.8%, LDA with CSPs by 13.9%, and regularized LDA with vectorized features by 16.4%. At the same time, TVLDA only required one or two internal features to achieve this. TVLDA provides stable results even if very few trials are available. It is easy to implement, fully automatic and deterministic. Due to its low complexity, TVLDA is suited for real-time brain-computer interfaces. Training is done in less than a second. TVLDA performed particularly well in experiments with data from high-density electrode arrays. For example, the three high-level gestures were correctly identified at a rate of 99% over all subjects. Similarly, the decoding accuracy of individual fingers was 96% on average over all subjects. To our knowledge, these mean accuracies are the highest ever reported for three-class and five-class motor-control BCIs.

Effect of Segmentation Parameters on Classification Accuracy of High-Density EMG recordings.

Electromyography (EMG) based control systems rely on the accurate identification of patterns extracted from signal features to predict the corresponding movement. The selection of segmentation window parameters and their impact on overall accuracy of classifiers has been previously studied for systems with a low number of EMG channels (<; 16). In this study a High-density EMG electrode array was used to evaluate the impact of the parameters when a high number of channels (128) was recorded. Findings show that in combination with high channel counts the impact of window length and overlap were marginal (<; 2% and <; 1% respectively). The number of channels was found to have direct correlation with achieved accuracy, with an improvement of up to 19.5 ± 4.5% in classification accuracy when increasing from 4 to 128 channels.

Neuroethical considerations of high-density electrode arrays.

The development of implantable electrode arrays that broadly and seamlessly integrate with brain tissue will require innovation that responsibly considers clinically relevant neuroethical concerns.

(Invited) Novel Three-Dimensional Fuzzy Graphene (3DFG)-Based Platform for Interrogation of Excitable Cells

Investigating and modulating electrical activity of cardiomyocytes and neurons is essential to understanding their physiology, regeneration of excitable cells/tissues, and treatment of various disorders. Recently, microelectrode arrays (MEAs) have gained tremendous popularity since they enable multi-site recordings at sub-msec temporal resolution, provide two-way interaction with cells i.e. recording and stimulation, and have well-established microfabrication methods. However, currently used planar MEAs are not able to record and stimulate at sub-cellular resolution due to significant increase in electrode impedance which reduces the signal-to-noise ratio (SNR) of recorded signal. Currently there is a need to (i) enable high density electrode arrays, (ii) provide high spatial resolution, and (iii) prevent averaging of signals from multiple cells. To address these needs, we have developed three-dimensional out-of-plane grown fuzzy graphene (3DFG)-based MEA. Out-of-plane growth of single-to-few layers of graphene flakes leverages graphene's outstanding surface-to-volume ratio. The high surface area leads to significant reduction in impedance, thus enabling electrical recordings with high SNR and development of ultra-microelectrode arrays to perform sub-cellular measurements. Furthermore, the high flake density of graphene in 3DFG lead to high photo-absorbance in UV-NIR range. This opens up avenues to enable photo-thermal stimulation of cells with high precision and sub-cellular resolution. Our presented approach would greatly impact our basic understanding of signal transduction in complex cellular assemblies. Furthermore, it would provide a platform for developing and screening of therapeutics, and treatment of various cardiac and neurological diseases.

Two degrees of freedom, dynamic, hand-wrist EMG-force using a minimum number of electrodes

Few studies have related the surface electromyogram (EMG) of forearm muscles to two degree of freedom (DoF) hand-wrist forces; ones that have, used large high-density electrode arrays that are impractical for most applied biomechanics research. Hence, we researched EMG-force in two DoFs—hand open-close paired with one wrist DoF—using as few as four conventional electrodes, comparing equidistant placement about the forearm to optimized site selection. Nine subjects produced 1-DoF and 2-DoF uniformly distributed random forces (bandlimited to 0.75 Hz) up to 30% maximum voluntary contraction (MVC). EMG standard deviation (EMGσ) was related to force offline using linear dynamic regression models. For 1-DoF forces, average RMS errors using two optimally-sited electrodes ranged from 8.3 to 9.0 %MVC, depending on the DoF. For 2-DoFs, overall performance was best when training from both 1- and 2-DoF trials, giving average RMS errors using four optimally-sited electrodes of 9.2 %MVC for each DoF pair (hand open-close paired with one wrist DoF). For each model, additional optimally-sited electrodes showed little statistical improvement. Electrodes placed equidistant performed noticeably poorer than an equal number of electrodes that were optimally sited. The results suggest that reliable 2-DoF hand-wrist EMG-force with a small number of electrodes may be feasible.

High-density extracellular probes reveal dendritic backpropagation and facilitate neuron classification.

Different neuron types serve distinct roles in neural processing. Extracellular electrical recordings are extensively used to study brain function but are typically blind to cell identity. Morphoelectrical properties of neurons measured on spatially dense electrode arrays have the potential to distinguish neuron types. We used high-density silicon probes to record from cortical and subcortical regions of the mouse brain. Extracellular waveforms of each neuron were detected across many channels and showed distinct spatiotemporal profiles among brain regions. Classification of neurons by brain region was improved with multichannel compared with single-channel waveforms. In visual cortex, unsupervised clustering identified the canonical regular-spiking (RS) and fast-spiking (FS) classes but also indicated a subclass of RS units with unidirectional backpropagating action potentials (BAPs). Moreover, BAPs were observed in many hippocampal RS cells. Overall, waveform analysis of spikes from high-density probes aids neuron identification and can reveal dendritic backpropagation. NEW & NOTEWORTHY It is challenging to identify neuron types with extracellular electrophysiology in vivo. We show that spatiotemporal action potentials measured on high-density electrode arrays can capture cell type-specific morphoelectrical properties, allowing classification of neurons across brain structures and within the cortex. Moreover, backpropagating action potentials are reliably detected in vivo from subpopulations of cortical and hippocampal neurons. Together, these results enhance the utility of dense extracellular electrophysiology for cell-type interrogation of brain network function.

Global activity shaping strategies for a retinal implant

Objective. Retinal prostheses provide visual perception via electrical stimulation of the retina using an implanted array of electrodes. The retinal activation resulting from each electrode is not point-like; instead each electrode introduces a spread of retinal activation that may overlap with activations from other electrodes. With most conventional stimulation strategies this overlap leads to image blur. Here we propose a ‘shaping’ algorithm that uses multiple electrodes to manipulate the current between electrodes in a desired way. Approach. We assume a forward model for the conversion of electrode strengths to retinal activation. Three alternative global shaping algorithms are developed by calculating reverse models under different assumptions: linear inversion using singular value decomposition to produce the pseudoinverse, a linearly constrained quadratic program, and a binary quadratic program to partition the target pattern. The algorithms were assessed using both the mean squared error between the resulting images and desired images, as well as their adherence to the maximum allowed electrode currents. Main results. Under wide activation spreads the linear inversion algorithm gave improved solutions but faced two limitations: under low-noise conditions the electrode amplitudes exceeded their set limit; the set of solutions did not include the possibility of using negative local currents to induce retinal activation. The linearly constrained quadratic program and binary quadratic program respectively addressed these problems, but required much greater computation time. Significance. This provides a framework for improving the resolution of future retinal implants, especially those with high density electrode arrays.

Active High-Density Electrode Arrays: Technology and Applications in Neuronal Cell Cultures.

Active high-density electrode arrays realized with complementary metal-oxide-semiconductor (CMOS) technology provide electrophysiological recordings from several thousands of closely spaced microelectrodes. This has drastically advanced the spatiotemporal recording resolution of conventional multielectrode arrays (MEAs). Thus, today's electrophysiology in neuronal cultures can exploit label-free electrical readouts from a large number of single neurons within the same network. This provides advanced capabilities to investigate the properties of self-assembling neuronal networks, to advance studies on neurotoxicity and neurodevelopmental alterations associated with human brain diseases, and to develop cell culture models for testing drug- or cell-based strategies for therapies.Here, after introducing the reader to this neurotechnology, we summarize the results of different recent studies demonstrating the potential of active high-density electrode arrays for experimental applications. We also discuss ongoing and possible future research directions that might allow for moving these platforms forward for screening applications.

Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis.

In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity. One specific need and still open problem is how to represent the time- and frequency-content carried by hundreds of dipoles with diverging orientation in each ROI with one unique representative time-series. The main aim of this paper is to provide a method to compute a signal that explains most of the variability of the data contained in each ROI before computing, for instance, time-varying connectivity. As the representative time-series for a ROI, we propose to use the first singular vector computed by a singular-value decomposition of all dipoles belonging to the same ROI. We applied this method to two real datasets (visual evoked potentials and epileptic spikes) and evaluated the time-course and the frequency content of the obtained signals. For each ROI, both the time-course and the frequency content of the proposed method reflected the expected time-course and the scalp-EEG frequency content, representing most of the variability of the sources (~ 80%) and improving connectivity results in comparison to other procedures used so far. We also confirm these results in a simulated dataset with a known ground truth.

High-Density Multichannel Electrode Array Improves the Accuracy of Cutaneous Electrogastrography Across Subjects With Wide-Ranging BMI

Introduction: The cutaneous electrogastrogram (EGG) is a noninvasive technique for measuring gastric myoelectric activity. The size, position and shape of the stomach vary considerably depending on body mass index, fed/fasting state and age (Lamart et al, 2013). In this study, we assessed how the placement of EGG electrodes can influence the ability to detect normal gastric slow waves. Methods: Eleven asymptomatic subjects (8M/3F, age 21—77 years) with an average BMI of 26 (range 21.8—36.3) were enrolled. Subjects were excluded if they had any active foregut symptoms on the PAGI—SYM questionnaire. We placed 100 EGG electrodes in a uniform 10x10 grid (2cm x 2cm spacing) on the abdominal wall extending inferiorly from the sternum at the top with reference and ground electrodes on the subject's right side. The locations of the umbilicus and xiphoid relative to the array were documented as anatomical references. After 30 minutes of fasting recording, each subject consumed a standardized meal and the study was continued for 90 minutes. The signal—to—noise ratio (SNR) for each electrode pair was calculated, defined as the average power between 0.04 Hz and 0.06 Hz divided by the average power in all other frequencies between 0.02 Hz and 0.20 Hz. We calculated the percentage of normal gastric slow waves, i.e., 2—4 cpm activity. The results from the highest SNR electrode pair were compared to those from the traditional single—channel EGG placement: one electrode halfway between the xiphoid and umbilicus and the second electrode placed 4—6 cm to the subject's left. Results: Using the highest SNR electrode pair, the percentage of normal slow waves was 92 ± 8%, and all 11 subjects were over the 70% threshold that defines normal. When using the traditional EGG electrode placement, however, this value dropped significantly to 68 ± 16%, and 6/11 subjects fell below 70% (see Table 1; Figure 1). Statistical significance of p = 4.4 x 10&#x2014;5 was determined by a two—sided t—test for the null hypothesis that the two related samples have identical average values. This effect was particularly pronounced for the high BMI subjects.1235_A Figure 1. Improved detection of gastric slow wave frequency with a high—density EGG array versus traditional lead placement.Conclusion: Standardization of cutaneous lead placement for EGG may be confounded by anatomical variability in stomach location coupled with an increased sensitivity to lead placement due to low signal amplitude. The results from this study suggest that multiple electrodes or a priori stomach localization can improve accurate detection of the gastric slow wave.1235_B Figure 2. Determination of normal gastric slow wave based on traditional EGG location versus highest EGG SNR location with a high density array.

Encoding of locomotion kinematics in the mouse cerebellum.

The cerebellum is involved in coordinating motor behaviour, but how the cerebellar network regulates locomotion is still not well understood. We characterised the activity of putative cerebellar Purkinje cells, Golgi cells and mossy fibres in awake mice engaged in an active locomotion task, using high-density silicon electrode arrays. Analysis of the activity of over 300 neurons in response to locomotion revealed that the majority of cells (53%) were significantly modulated by phase of the stepping cycle. However, in contrast to studies involving passive locomotion on a treadmill, we found that a high proportion of cells (45%) were tuned to the speed of locomotion, and 19% were tuned to yaw movements. The activity of neurons in the cerebellar vermis provided more information about future speed of locomotion than about past or present speed, suggesting a motor, rather than purely sensory, role. We were able to accurately decode the speed of locomotion with a simple linear algorithm, with only a relatively small number of well-chosen cells needed, irrespective of cell class. Our observations suggest that behavioural state modulates cerebellar sensorimotor integration, and advocate a role for the cerebellar vermis in control of high-level locomotor kinematic parameters such as speed and yaw.

Design of High-Density Electrodes For EEG Acquisition.

In a 256-channel electrode cap for electroencephalogram (EEG) acquisition, the inter-space between adjacent electrodes is around 20mm. Theoretical and experimental evidence predict that improving the density of electrode can get more information from the added electrodes. 10mm or less center distance, corresponding to 1000 electrodes on a full head EEG cap, might be a more proper density to current EEG analysis methods. To develop high-density electrode array with center distance equal or less than 10mm, one must make sure that the adjacent electrodes are electrical isolated. It is difficult to avoid short circuit when common wet electrodes are used to build high-density electrode array. The contact area (about 28mm2 with diameter of 6mm) and gelling method make short circuit easily happen. To provide more isolation space between adjacent electrodes, the contact area of the proposed electrode should be less than 8mm2. To restrict the diffusion of the electrolyte, a customized hydrogel is used to replace the conventional gel. Compared with common wet electrode and gel, preliminary tests indicate that the high-density hydrogel-Ag/AgCl electrodes perform well at the impedance, isolation, as well as data quality in EEG acquisition.

Robust and accurate decoding of motoneuron behaviour and prediction of the resulting force output.

The spinal alpha motoneuron is the only cell in the human CNS whose discharge can be routinely recorded in humans. We have reengineered motor unit collection and decomposition approaches, originally developed in humans, to measure the neural drive to muscle and estimate muscle force generation in the in vivo cat model. Experimental, computational, and predictive approaches are used to demonstrate the validity of this approach across a wide range of modes to activate the motor pool. The utility of this approach is shown through the ability to track individual motor units across trials, allowing for better predictions of muscle force than the electromyography signal, and providing insights in to the stereotypical discharge characteristics in response to synaptic activation of the motor pool. This approach now allows for a direct link between the intracellular data of single motoneurons, the discharge properties of motoneuron populations, and muscle force generation in the same preparation. The discharge of a spinal alpha motoneuron and the resulting contraction of its muscle fibres represents the functional quantum of the motor system. Recent advances in the recording and decomposition of the electromyographic signal allow for the identification of several tens of concurrently active motor units. These detailed population data provide the potential to achieve deep insights into the synaptic organization of motor commands. Yet most of our understanding of the synaptic input to motoneurons is derived from intracellular recordings in animal preparations. Thus, it is necessary to extend the new electrode and decomposition methods to recording of motor unit populations in these same preparations. To achieve this goal, we use high-density electrode arrays and decomposition techniques, analogous to those developed for humans, to record and decompose the activity of tens of concurrently active motor units in a hindlimb muscle in the in vivo cat. Our results showed that the decomposition method in this animal preparation was highly accurate, with conventional two-source validation providing rates of agreement equal to or superior to those found in humans. Multidimensional reconstruction of the motor unit action potential provides the ability to accurately track the same motor unit across multiple contractions. Additionally, correlational analyses demonstrate that the composite spike train provides better estimates of whole muscle force than conventional estimates obtained from the electromyographic signal. Lastly, stark differences are observed between the modes of activation, in particular tendon vibration produced quantal interspike intervals at integer multiples of the vibration period.

Algorithm/Architecture Co-optimisation Technique for Automatic Data Reduction of Wireless Read-Out in High-Density Electrode Arrays

High-density electrode arrays used to read out neural activity will soon surpass the limits of the amount of data that can be transferred within reasonable energy budgets. This is true for wired brain implants when the required bandwidth becomes very high, and even more so for untethered brain implants that require wireless transmission of data. We propose an energy-efficient spike data extraction solution for high-density electrode arrays, capable of reducing the data to be transferred by over 85%. We combine temporal and spatial spike data analysis with low implementation complexity, where amplitude thresholds are used to detect spikes and the spatial location of the electrodes is used to extract potentially useful sub-threshold data on neighboring electrodes. We tested our method against a state-of-the-art spike detection algorithm, with prohibitively high implementation complexity, and found that the majority of spikes are extracted reliably. We obtain further improved quality results when ignoring very small spikes below 30% of the voltage thresholds, resulting in 91% accuracy. Our approach uses digital logic and is therefore scalable with an increasing number of electrodes.

High-density impedance-sensing array on complementary metal-oxide-semiconductor circuitry assisted by negative dielectrophoresis for single-cell-resolution measurement

A microarray biosensor that measures the electrical impedance of cell suspensions is presented. Single-cell–resolution measurement was facilitated by physically positioning individual cells on sensing electrodes by dielectrophoresis, obviating chemical or biological surface modification. The high-density (104 × 104) electrode array was incorporated with a complementary metal-oxide-semiconductor (CMOS) integrated chip (IC) to support impedance spectroscopy at frequencies ranging from 100 kHz to 1 MHz, achieving a rapid and cost-effective platform with a small form factor. Experiments with microbeads (10, 15, and 20 μm in diameter) and a live breast cancer cell line (MCF-7) have demonstrated that the developed prototype quantifies loaded microbeads or cells rapidly (<1 min), with a mapping accuracy over 95%, showing good agreement with optical observation. In addition, a new impedance model of a cancer cell suspended in buffer media was constructed to interpret the measurement results, and the simulation results obtained with the model showed good agreement with the experimental results.

Wireless sEMG System with a Microneedle-Based High-Density Electrode Array on a Flexible Substrate

Surface electromyography (sEMG) signals reflect muscle contraction and hence, can provide information regarding a user’s movement intention. High-density sEMG systems have been proposed to measure muscle activity in small areas and to estimate complex motion using spatial patterns. However, conventional systems based on wet electrodes have several limitations. For example, the electrolyte enclosed in wet electrodes restricts spatial resolution, and these conventional bulky systems limit natural movements. In this paper, a microneedle-based high-density electrode array on a circuit integrated flexible substrate for sEMG is proposed. Microneedles allow for high spatial resolution without requiring conductive substances, and flexible substrates guarantee stable skin–electrode contact. Moreover, a compact signal processing system is integrated with the electrode array. Therefore, sEMG measurements are comfortable to the user and do not interfere with the movement. The system performance was demonstrated by testing its operation and estimating motion using a Gaussian mixture model-based, simplified 2D spatial pattern.

Direct Current Coupled Recordings of Cortical Spreading Depression Using Silicone Probes.

Electrophysiological assessment of infraslow (<0.1 Hz) brain activities such as cortical spreading depression (SD), which occurs in a number of pathologies including migraine, epilepsy, traumatic brain injury (TBI) and brain ischemia requires direct current (DC) coupled recordings of local field potentials (LFPs). Here, we describe how DC-coupled recordings can be performed using high-density iridium electrode arrays (silicone probes). We found that the DC voltage offset of the silicone probe is large and often exceeds the amplifier input range. Introduction of an offset compensation chain at the signal ground efficiently minimized the DC offsets. Silicone probe DC-coupled recordings across layers of the rat visual and barrel cortices revealed that epipial application of KCl, dura incision or pinprick TBI induced SD which preferentially propagated through the supragranular layers and further spread to the granular and infragranular layers attaining maximal amplitudes of ~−30 mV in the infragranular layers. SD at the superficial cortical layers was nearly two-fold longer than at the deep cortical layers. Continuous epipial KCl evoked multiple recurrent SDs which always started in the supragranular layers but often failed to propagate through the deeper cortical layers. Intracortical KCl injection into the infragranular layers evoked SD which also started in the supragranular layers and spread to the granular and infragranular layers, further indicating that the supragranular layers are particularly prone to SD. Thus, DC-coupled recordings with silicone probes after offset compensation can be successfully used to explore the spatial—temporal dynamics of SD and other slow brain activities.

Immediate cortical adaptation in visual and non-visual areas functions induced by monovision.

Monovision is an optical correction for presbyopes that consists of correcting one eye for far distance and the other for near distance, creating a superimposition of an in-focus with a blurred image. Brain adaptation to monovision was studied in unexperienced observers by measuring visual evoked potentials from 64-channels. The first clear effect of monovision on visual evoked potentials was the C1 amplitude reduction, indicating that the unilateral blurring induced by monovision reduces feed-forward activity in primary visual area. Monovision led also to an increased amplitude of the P1 and pP1 components, with the latter originating in prefrontal regions. This effect probably works as an attentional compensatory activity used to compensate for the degraded V1 signal. A common and often successful option to correct presbyopia with contact lenses is monovision. This is an unbalanced correction across the two eyes where one eye is corrected for far vision and the other eye is corrected for near vision. Monovision is therefore a form of acquired anisometropia that causes a superimposition of an in-focus image with a blurred image. In spite of this visual anisometropia, monovision has been successfully used for many decadesl however the brain mechanism supporting monovision is not well understood. The present study aimed to measure the visual evoked potentials with a high-density electrode array (64-channel) in a group of presbyopes and to provide a detailed spatiotemporal analysis of the cortical activity after a short period of adaptation to monovision with contact lenses. When compared with a balanced eye near correction, monovision produced both a clear reduction of the earliest visual evoked potential components, the C1 and the N1, and an amplitude increase of the P1 and pP1. These results indicate that the unilateral blurring induced by wearing monovision contact lenses reduces feed-forward activity in the primary visual area and feedback activity in extrastriate areas (C1 and N1 reduction). Interestingly, other brain activities in both extrastriate visual areas (the P1 component) and in the anterior insula (the pP1 component) appear to compensate for this dysfunction, increasing their activity during monovision. These changes confirm the presence of fluid brain adaptation in visual and non-visual areas during monocular interferences.