Low Information Transfer Research Articles

The role of eye movement signals in non-invasive brain-computer interface typing system.

Brain-Computer Interfaces (BCIs) have shown great potential in providing communication and control for individuals with severe motor disabilities. However, traditional BCIs that rely on electroencephalography (EEG) signals suffer from low information transfer rates and high variability across users. Recently, eye movement signals have emerged as a promising alternative due to their high accuracy and robustness. Eye movement signals are the electrical or mechanical signals generated by the movements and behaviors of the eyes, serving to denote the diverse forms of eye movements, such as fixations, smooth pursuit, and other oculomotor activities like blinking. This article presents a review of recent studies on the development of BCI typing systems that incorporate eye movement signals. We first discuss the basic principles of BCI and the recent advancements in text entry. Then, we provide a comprehensive summary of the latest advancements in BCI typing systems that leverage eye movement signals. This includes an in-depth analysis of hybrid BCIs that are built upon the integration of electrooculography (EOG) and eye tracking technology, aiming to enhance the performance and functionality of the system. Moreover, we highlight the advantages and limitations of different approaches, as well as potential future directions. Overall, eye movement signals hold great potential for enhancing the usability and accessibility of BCI typing systems, and further research in this area could lead to more effective communication and control for individuals with motor disabilities.

Feasibility of a Wireless Implantable Multi-electrode System for High-bandwidth Prosthetic Interfacing: Animal and Cadaver Study.

Currently used prosthetic solutions in upper extremity amputation have limited functionality, owing to low information transfer rates of neuromuscular interfacing. Although surgical innovations have expanded the functional potential of the residual limb, available interfaces are inefficacious in translating this potential into improved prosthetic control. There is currently no implantable solution for functional interfacing in extremity amputation which offers long-term stability, high information transfer rates, and is applicable for all levels of limb loss. In this study, we presented a novel neuromuscular implant, the the Myoelectric Implantable Recording Array (MIRA). To our knowledge, it is the first fully implantable system for prosthetic interfacing with a large channel count, comprising 32 intramuscular electrodes. The purpose of this study was to evaluate the MIRA in terms of biocompatibility, functionality, and feasibility of implantation to lay the foundations for clinical application. This was achieved through small- and large-animal studies as well as test surgeries in a human cadaver. We evaluated the biocompatibility of the system's intramuscular electromyography (EMG) leads in a rabbit model. Ten leads as well as 10 pieces of a biologically inert control material were implanted into the paravertebral muscles of four animals. After a 3-month implantation, tissue samples were taken and histopathological assessment performed. The probes were scored according to a protocol for the assessment of the foreign body response, with primary endpoints being inflammation score, tissue response score, and capsule thickness in µm. In a second study, chronic functionality of the full system was evaluated in large animals. The MIRA was implanted into the shoulder region of six dogs and three sheep, with intramuscular leads distributed across agonist and antagonist muscles of shoulder flexion. During the observation period, regular EMG measurements were performed. The implants were removed after 5 to 6 months except for one animal, which retained the implant for prolonged observation. Primary endpoints of the large-animal study were mechanical stability, telemetric capability, and EMG signal quality. A final study involved the development of test surgeries in a fresh human cadaver, with the goal to determine feasibility to implant relevant target muscles for prosthetic control at all levels of major upper limb amputation. Evaluation of the foreign body reaction revealed favorable biocompatibility and a low-grade tissue response in the rabbit study. No differences regarding inflammation score (EMG 4.60 ± 0.97 [95% CI 4.00 to 5.20] versus control 4.20 ± 1.48 [95% CI 3.29 to 5.11]; p = 0.51), tissue response score (EMG 4.00 ± 0.82 [95% CI 3.49 to 4.51] versus control 4.00 ± 0.94 [95% CI 3.42 to 4.58]; p > 0.99), or thickness of capsule (EMG 19.00 ± 8.76 µm [95% CI 13.57 to 24.43] versus control 29.00 ± 23.31 µm [95% CI 14.55 to 43.45]; p = 0.29) were found compared with the inert control article (high-density polyethylene) after 3 months of intramuscular implantation. Throughout long-term implantation of the MIRA in large animals, telemetric communication remained unrestricted in all specimens. Further, the implants retained the ability to record and transmit intramuscular EMG data in all animals except for two sheep where the implants became dislocated shortly after implantation. Electrode impedances remained stable and below 5 kΩ. Regarding EMG signal quality, there was little crosstalk between muscles and overall average signal-to-noise ratio was 22.2 ± 6.2 dB. During the test surgeries, we found that it was possible to implant the MIRA at all major amputation levels of the upper limb in a human cadaver (the transradial, transhumeral, and glenohumeral levels). For each level, it was possible to place the central unit in a biomechanically stable environment to provide unhindered telemetry, while reaching the relevant target muscles for prosthetic control. At only the glenohumeral level, it was not possible to reach the teres major and latissimus dorsi muscles, which would require longer lead lengths. As assessed in a combination of animal model and cadaver research, the MIRA shows promise for clinical research in patients with limb amputation, where it may be employed for all levels of major upper limb amputation to provide long-term stable intramuscular EMG transmission. In our study, the MIRA provided high-bandwidth prosthetic interfacing through intramuscular electrode sites. Its high number of individual EMG channels may be combined with signal decoding algorithms for accessing spinal motor neuron activity after targeted muscle reinnervation, thus providing numerous degrees of freedom. Together with recent innovations in amputation surgery, the MIRA might enable improved control approaches for upper limb amputees, particularly for patients with above-elbow amputation where the mismatch between available control signals and necessary degrees of freedom for prosthetic control is highest.

Experimental Demonstration of Delay-Bounded Wireless Network Based on Precise Time Synchronization

Low latency and reliable information transfer are highly demanded in fifth generation (5G) and beyond 5G wireless communications. A novel delay-bounded wireless media access control (MAC) protocol called Carrier Sense Multiple Access with Arbitration Point (CSMA/AP) was established to strictly ensure the upper boundary of communication delay. CSMA/AP enables collision-free and delay-bounded communications with a simple arbitration mechanism exploiting the precise time synchronization achieved by Wireless Two-Way Interferometry (Wi-Wi). Experimental demonstration and proving the feasibility in wireless environments are among the most critical steps before any further discussion of CSMA/AP and extension to various applications can take in place. In this work described in this paper, we experimentally demonstrated the fundamental principles of CSMA/AP by constructing a star-topology wireless network using software-defined radio terminals combined with precise time synchronization devices. We show that CSMA/AP was successfully operated, even with dynamic changes of the spatial position of the terminal or the capability to accommodate mobility, thanks to the real-time adaption to the dynamically changing environment by Wi-Wi. We also experimentally confirmed that the proposed CSMA/AP principle cannot be executed without Wi-Wi, which validates the importance of precise time synchronization. This study paves the way toward realizing delay-bounded wireless communications for future low-latency and highly reliable critical applications.

Low latency information transfer based on precision time synchronization via wireless interferometry

Low latency information transfer based on precision time synchronization via wireless interferometry

Performance improvement of P300-based home appliances control classification using convolution neural network

P300 speller-based brain-computer interface (BCI) is an immediate correspondence from a human brain to a computer that depends on the translation of mind reactions produced by stimulus of a subject utilizing a P300 speller. No muscle movements are required for this communication. The present study helps the disabled people (viz.patients with spinal cord injury, spastic cerebral palsy, locomotive diseases, etc.) ease their lives by accessing light, fan, mobile device, door, television, electric heater, etc. A novel 2 × 3 matrix consisting of home appliances visuals has been proposed as a P300 paradigm. Once the matrix visualization is completed, auditory feedback was provided and the chosen command was executed. It enables six menus to be navigated for hadling six electronic systems with up to 30 control commands. The objective of this research is to develop a P300-based BCI system for operating different electronic appliances at home. The existing BCI-based P300 spellers with standard machine learning methods suffers from low information transfer rate (ITR) and poor classification accuracy. With the application of convolution neural network (CNN) for classification, the proposed system improves the performance in terms of P300 classification & target appliances detection. The proposed system is designed to be user-convenient & cost-effective in terms of hardware design. The experiments have been performed on the dataset acquired from 30 target item selections from nine subjects using 16 channel actiCAP Xpress EEG recorders. The experimental result demonstrates that the proposed CNN model achieved 90% classification accuracy. It also offers ITR of 22.3 bits per minute which is substantially greater than the existing methods. The presented approach shows its novelty in the real-time applications.

Brain–computer interface‐based single trial P300 detection for home environment application

P300 speller-based brain–computer interface (BCI) is an immediate correspondence between the human brain and computer that depends on the translation of mind reactions produced by the stimulus of a subject utilising the P300 speller. No muscle movements are required for this communication. As a P300 paradigm, a novel 2 × 3 matrix consisting of visual home appliances is proposed, which helps disabled people ease their lives by accessing mobile, light, fan, door, television, electric heater etc. In most of the current P300-based BCIs, 5–15 trials work better and the low information transfer rate (ITR) is a major issue in its adaptation in real-time. The objective of this Letter is to improve accuracy as well as an ITR for real-time home appliance control applications. To address this, the authors proposed a single trial weighted ensemble of compact convolution neural network and obtained an ITR of 46.45 bits per minute and an average target appliance accuracy of 93.22% for the BCI-based home environment system. The experimental findings confirmed the feasibility of the proposed method and thus can provide guidance for future use of the system for paralysed patients.

Discrimination of SSVEP responses using a kernel based approach.

Brain Computer Interfaces based on Steady State Visual Evoked Potentials have gained increased attention due to their low training requirements and higher information transfer rates. In this work, a method based on sparse kernel machines is proposed for the discrimination of Steady State Visual Evoked Potentials responses. More specifically, a new kernel based on Partial Least Squares is introduced to describe the similarities between EEG trials, while the estimation of regression weights is performed using the Sparse Bayesian Learning framework. The experimental results obtained on two benchmarking datasets, have shown that the proposed method provides significantly better performance compared to state of the art approaches of the related literature.

Optimal signal processing algorithm at the background of non-Gaussian flicker noise

The problem of receiving and processing ultra-low-power signals of information transmission systems is being solved. High requirements for energy efficiency on the one hand and a low information transfer rate allows the use of signals with a small spectrum width, including flicker noise spectral regions. A non-Gaussian flicker noise model is used based on a stochastic differential equation with a nonlinear drift coefficient. An optimal signal processing algorithm is being developed against the background of the sum of flicker noise and thermal noise based on an estimated-correlation-compensation approach. The analysis of the effectiveness of optimal signal processing against a background of non-Gaussian flicker noise and thermal noise is carried out.

Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning.

The performance of an existing Devanagari script (DS) input-based P300 speller with conventional machine learning techniques suffers from low information transfer rate (ITR). This occurs due to its required large size of display, i.e., 8 × 8 row-column (RC) paradigm which exhibits issues like crowding effect, adjacency, fatigue, task difficulty, and required large number of trials for character recognition. For P300 detection, deep learning algorithms have shown the state of art performance compared to the conventional machine learning algorithms in the recent past. Therefore, authors have been motivated to develop a deep learning architecture for DS-based P300 speller which can detect the target characters more accurately and in less number of trials. For this, two proven deep learning algorithms, stacked autoencoder (SAE) and deep convolution neural network (DCNN) have been adopted. For further bettering their performances, batch normalization and innovative double batch training is included here to achieve accelerated training and alleviate the problem of overfitting. Additionally, a leaky ReLU activation function has also been used in DCNN to overcome dying ReLU problem. The experiments have been performed on self-generated dataset of 20 Devanagari words with 79 characters acquired from 10 subjects using 16 channel actiCAP Xpress EEG recorder. The experimental results illustrated that the proposed DCNN is able to detect 88.22% correct targets in just three trials. Moreover, it also provides ITR of 20.58 bits per minutes, which is significantly higher than existing techniques.

VEP-based brain-computer interfaces modulated by Golay complementary series for improving performance.

The goal of a brain-computer interface (BCI) is to enable communication by pure brain activity without neural and muscle control. However, the practical use of BCIs is limited by low information transfer rate. Recently, code modulation visual evoked potential (c-VEP) based BCIs have exhibited great potential in establishing high-rate communication between the brain and the external world. This study aims at exploring a more effective modulation code than the commonly used pseudorandom M sequence for c-VEP based BCIs (c-VEP BCIs) in order to increase the detection accuracy of stimulus targets and the resulting information transfer rate. Golay complementary sequence pair is used for constructing the modulation code of c-VEP BCIs due to their superior autocorrelation property. The modulation code is created by concatenating a pair of Golay complementary sequences. Sixteen target stimuli are modulated by the Golay code and its time shift versions. Through offline analysis on data recorded from seven subjects and online test on five subjects, the Golay code modulated BCI yielded higher detection accuracy and information transfer rate than those achieved by M sequence. The Golay code modulated BCI demonstrates a high performance compared with the M sequence modulated systems, and it is applicable to persons with motor disabilities.

Enabling real-time information service on telehealth system over cloud-based big data platform

We design a data coherence protocol for the PHR-based distributed system.We propose a flow estimating algorithm for the telehealth cloud system.We apply several predicting methods for the future bandwidth consumption.We present a telehealth framework for bandwidth balance on emergency. A telehealth system covers both clinical and nonclinical uses, which not only provides store-and-forward data services to be offline studied by relevant specialists, but also monitors the real-time physiological data through ubiquitous sensors to support remote telemedicine. However, the current telehealth systems do not consider the velocity and veracity of the big-data system in the medical context. Emergency events generate a large amount of the real-time data, which should be stored in the data center, and forwarded to remote hospitals. Furthermore, patients' information is scattered on the distributed data center, which cannot provide a high-efficient remote real-time service. In this paper, we proposes a probability-based bandwidth model in a telehealth cloud system, which helps cloud broker to provide a high performance allocation of computing nodes and links. This brokering mechanism considers the location protocol of Personal Health Record (PHR) in cloud and schedules the real-time signals with a low information transfer between different hosts. The broker uses several bandwidth evaluating methods to predict the near future usage of bandwidth in a telehealth context. The simulation results show that our model is effective at determining the best performing service, and the inserted service validates the utility of our approach.

Using a cVEP-Based Brain-Computer Interface to Control a Virtual Agent.

Brain-computer interfaces provide a means for controlling a device by brain activity alone. One major drawback of noninvasive BCIs is their low information transfer rate, obstructing a wider deployment outside the lab. BCIs based on codebook visually evoked potentials (cVEP) outperform all other state-of-the-art systems in that regard. Previous work investigated cVEPs for spelling applications. We present the first cVEP-based BCI for use in real-world settings to accomplish everyday tasks such as navigation or action selection. To this end, we developed and evaluated a cVEP-based on-line BCI that controls a virtual agent in a simulated, but realistic, 3-D kitchen scenario. We show that cVEPs can be reliably triggered with stimuli in less restricted presentation schemes, such as on dynamic, changing backgrounds. We introduce a novel, dynamic repetition algorithm that allows for optimizing the balance between accuracy and speed individually for each user. Using these novel mechanisms in a 12-command cVEP-BCI in the 3-D simulation results in ITRs of 50 bits/min on average and 68 bits/min maximum. Thus, this work supports the notion of cVEP-BCIs as a particular fast and robust approach suitable for real-world use.

RRTX: Asymptotically optimal single-query sampling-based motion planning with quick replanning

Dynamic environments have obstacles that unpredictably appear, disappear, or move. We present the first sampling-based replanning algorithm that is asymptotically optimal and single-query (designed for situation in which a priori offline computation is unavailable). Our algorithm, RRTX, refines and repairs the same search-graph over the entire duration of navigation (in contrast to previous single-query replanning algorithms that prune and then regrow some or all of the search-tree). Whenever obstacles change and/or the robot moves, a graph rewiring cascade quickly remodels the existing search-graph and repairs its shortest-path-to-goal sub-tree to reflect the new information. Both graph and tree are built directly in the robot’s state-space; thus, the resulting plan(s) respect the kinematics of the robot and continue to improve during navigation. RRTX is probabilistically complete and makes no distinction between local and global planning, yet it reacts quickly enough for real-time high-speed navigation through unpredictably changing environments. Low information transfer time is essential for enabling RRTX to react quickly in dynamic environments; we prove that the information transfer time required to inform a graph of size n about an ε-cost decrease is O(n log n) for RRTX—faster than other current asymptotically optimal single-query algorithms (we prove RRT* is and RRT# is (n log2n)). In static environments RRTX has the same amortized runtime as RRT and RRT*, Θ(log n), and is faster than RRT#, (log2n). In order to achieve O(log n) iteration time, each node maintains a set of O(log n) expected neighbors, and the search-graph maintains ε-consistency for a predefined ε. Experiments and simulations confirm our theoretical analysis and demonstrate that RRTX is useful in both static and dynamic environments.

Use of a Green Familiar Faces Paradigm Improves P300-Speller Brain-Computer Interface Performance.

BackgroundA recent study showed improved performance of the P300-speller when the flashing row or column was overlaid with translucent pictures of familiar faces (FF spelling paradigm). However, the performance of the P300-speller is not yet satisfactory due to its low classification accuracy and information transfer rate.ObjectiveTo investigate whether P300-speller performance is further improved when the chromatic property and the FF spelling paradigm are combined.MethodsWe proposed a new spelling paradigm in which the flashing row or column is overlaid with translucent green pictures of familiar faces (GFF spelling paradigm). We analyzed the ERP waveforms elicited by the FF and proposed GFF spelling paradigms and compared P300-speller performance between the two paradigms.ResultsSignificant differences in the amplitudes of four ERP components (N170, VPP, P300, and P600f) were observed between both spelling paradigms. Compared to the FF spelling paradigm, the GFF spelling paradigm elicited ERP waveforms of higher amplitudes and resulted in improved P300-speller performance.ConclusionsCombining the chromatic property (green color) and the FF spelling paradigm led to better classification accuracy and an increased information transfer rate. These findings demonstrate a promising new approach for improving the performance of the P300-speller.

SBCI-Headset—Wearable and Modular Device for Hybrid Brain-Computer Interface

Severely disabled people, like completely paralyzed persons either with tetraplegia or similar disabilities who cannot use their arms and hands, are often considered as a user group of Brain Computer Interfaces (BCI). In order to achieve high acceptance of the BCI by this user group and their supporters, the BCI system has to be integrated into their support infrastructure. Critical disadvantages of a BCI are the time consuming preparation of the user for the electroencephalography (EEG) measurements and the low information transfer rate of EEG based BCI. These disadvantages become apparent if a BCI is used to control complex devices. In this paper, a hybrid BCI is described that enables research for a Human Machine Interface (HMI) that is optimally adapted to requirements of the user and the tasks to be carried out. The solution is based on the integration of a Steady-state visual evoked potential (SSVEP)-BCI, an Event-related (de)-synchronization (ERD/ERS)-BCI, an eye tracker, an environmental observation camera, and a new EEG head cap for wearing comfort and easy preparation. The design of the new fast multimodal BCI (called sBCI) system is described and first test results, obtained in experiments with six healthy subjects, are presented. The sBCI concept may also become useful for healthy people in cases where a “hands-free” handling of devices is necessary.

A high-ITR SSVEP-based BCI speller

Spelling is an important application of brain-computer interfaces (BCIs). Previous BCI spellers were not suited for widespread use due to their low information transfer rate (ITR). In this study, we constructed a high-ITR BCI speller based on the steady-state visual evoked potential (SSVEP). A 45-target BCI speller was implemented with a frequency resolution of 0.2 Hz. A sampled sinusoidal stimulation method was used to present visual stimuli on a conventional LCD screen. The online results revealed that the proposed BCI speller had a good performance, reaching a high average accuracy (84.1% for 2 s stimulation time; 90.2% for 3 s stimulation time) and the corresponding high ITR (105 bits/min for 2 s stimulation time, 82 bits/min for 3 s stimulation time) during the low-frequency stimuli, while 88.7% and 61 bits/min were achieved for a 4 s time window during the high-frequency stimuli.

What limits the performance of current invasive brain machine interfaces?

The concept of a brain-machine interface (BMI) or a computer-brain interface is simple: BMI creates a communication pathway for a direct control by brain of an external device. In reality BMIs are very complex devices and only recently the increase in computing power of microprocessors enabled a boom in BMI research that continues almost unabated to this date, the high point being the insertion of electrode arrays into the brains of 5 human patients in a clinical trial run by Cyberkinetics with few other clinical tests still in progress. Meanwhile several EEG-based BMI devices (non-invasive BMIs) were launched commercially. Modern electronics and dry electrode technology made possible to drive the cost of some of these devices below few hundred dollars. However, the initial excitement of the direct control by brain waves of a computer or other equipment is dampened by large efforts required for learning, high error rates and slow response speed. All these problems are directly related to low information transfer rates typical for such EEG-based BMIs. In invasive BMIs employing multiple electrodes inserted into the brain one may expect much higher information transfer rates than in EEG-based BMIs because, in theory, each electrode provides an independent information channel. However, although invasive BMIs require more expensive equipment and have ethical problems related to the need to insert electrodes in the live brain, such financial and ethical costs are often not offset by a dramatic improvement in the information transfer rate. Thus the main topic of this review is why in invasive BMIs an apparently much larger information content obtained with multiple extracellular electrodes does not translate into much higher rates of information transfer? This paper explores possible answers to this question by concluding that more research on what movement parameters are encoded by neurons in motor cortex is needed before we can enjoy the next generation BMIs.

A Review of Hybrid Brain-Computer Interface Systems

Increasing number of research activities and different types of studies in brain-computer interface (BCI) systems show potential in this young research area. Research teams have studied features of different data acquisition techniques, brain activity patterns, feature extraction techniques, methods of classifications, and many other aspects of a BCI system. However, conventional BCIs have not become totally applicable, due to the lack of high accuracy, reliability, low information transfer rate, and user acceptability. A new approach to create a more reliable BCI that takes advantage of each system is to combine two or more BCI systems with different brain activity patterns or different input signal sources. This type of BCI, called hybrid BCI, may reduce disadvantages of each conventional BCI system. In addition, hybrid BCIs may create more applications and possibly increase the accuracy and the information transfer rate. However, the type of BCIs and their combinations should be considered carefully. In this paper, after introducing several types of BCIs and their combinations, we review and discuss hybrid BCIs, different possibilities to combine them, and their advantages and disadvantages.

Team Member Change, Flux in Coordination, and Performance: Effects of Strategic Core Roles, Information Transfer, and Cognitive Ability

We developed and tested the conditions under which team member change results in flux in team coordination and consequently affects team performance. Results showed that team member change caused high levels of flux in coordination when a member changed to a more strategically core role, or there was low information transfer during the change. Furthermore, coupling strategic “core role holder” change with the relative cognitive ability of a new member was associated with even greater levels of flux in coordination.

Implications of single-neuron gain scaling for information transmission in networks

AbstractSummary:Many neural systems are equipped with mechanisms to efficiently encode sensory information. To represent natural stimuli with time-varying statistical properties, neural systems should adjust their gain to the inputs' statistical distribution. Such matching of dynamic range to input statistics has been shown to maximize the information transmitted by the output spike trains (Brenner et al., 2000, Fairhall et al., 2001). Gain scaling has not only been observed as a system response property, but also in single neurons in developing somatosensory cortex stimulated with currents of different amplitude (Mease et al., 2010). While gain scaling holds for cortical neurons at the end of the first post-natal week, at birth these neurons lack this property. The observed improvement in gain scaling coincides with the disappearance of spontaneous waves of activity in cortex (Conheim et al., 2010).We studied how single-neuron gain scaling affects the dynamics of signal transmission in networks, using the developing cortex as a model. In a one-layer feedforward network, we showed that the absence of gain control made the network relatively insensitive to uncorrelated local input fluctuations. As a result, these neurons selectively and synchronously responded to large slowly-varying correlated input--the slow build up of synaptic noise generated in pacemaker circuits which most likely triggers waves. Neurons in gain scaling networks were more sensitive to the small-scale input fluctuations, and responded asynchronously to the slow envelope. Thus, gain scaling both increases information in individual neurons about private inputs and allows the population average to encode the slow fluctuations in the input. Paradoxically, the synchronous firing that corresponds to wave propagation is associated with low information transfer. We therefore suggest that the emergence of gain scaling may help the system to increase information transmission on multiple timescales as sensory stimuli become important later in development. Methods: Networks with one and two layers consisting of hundreds of model neurons were constructed. The ability of single neurons to gain scale was controlled by changing the ratio of sodium to potassium conductances in Hodgkin-Huxley neurons (Mainen et al., 1995). The response of single layer networks was studied with ramp-like stimuli with slopes that varied over several hundreds of milliseconds. Fast fluctuations were superimposed on this slowly-varying mean. Then the response to these networks was tested with continuous stimuli. Gain scaling networks captured the slow fluctuations in the inputs, while non-scaling networks simply thresholded the input. Quantifying information transmission confirmed that gain scaling neurons transmit more information about the stimulus. With the two-layer networks we simulated a cortical network where waves could spontaneously emerge, propagate and degrade, based on the gain scaling properties of the neurons in the network.