What evidence exists relating the impact of different grassland management practices to soil carbon in livestock systems? A systematic map protocol.

Background: Grasslands are essential for providing vital resources in the livestock sector and delivering invaluable ecosystem services such as biodiversity and soil carbon (C) sequestration. Despite their critical importance, these ecosystems face escalating threats from human disturbances, human degradation, and climate change, compromising their ability to effectively stock C. Restoring degraded grasslands emerges as a pragmatic and cost-effective approach to tackling climate change. However, the successful implementation of grassland management toward this goal, faces significant challenges. A systematic mapping approach will help to compile a comprehensive global inventory of studies investigating the impact of differing grassland management practices on soil carbon. In addition, the potential for trade-offs with other greenhouse gas emissions further underlines the value of a systematic assessment. This approach aims to identify knowledge clusters (i.e., well-represented subtopics that are amenable to full synthesis) for potential systematic reviews and pinpoint knowledge gaps requiring further primary research efforts, all contributing to a better understanding of the evidence surrounding this topic. Methods: Following systematic evidence synthesis standards, we developed the question to address in the systematic map protocol using the PICO framework. We established a preliminary search string by combining search terms for the Population (Grasslands), Intervention (management) and Outcome (soil carbon) categories, as well as with one additional group (Study types-to focus on farm and field experiments). We will conduct a comprehensive literature search of relevant peer-reviewed and grey literature using Web of Science, Scopus, CABI platforms, Google Scholar, and specialised websites (e.g., Agrotrop). Searches will be conducted in the English, Spanish, Portuguese, French, German, and Mongolian languages, as per the linguistic capabilities of the research team. The comprehensiveness of the search will be assessed by comparing the literature collected to a test-list of forty relevant articles. The repeatability of the literature screening process will be ensured by a list of inclusion/exclusion criteria and inter-reviewer consistency statistical tests. Data extraction will be organised into four complementary sections (article information, PICO categories, study characteristics, measurable parameters), on which we will perform queries to produce the tables, figures and evidence maps that will compose the systematic map. The results will identify and describe knowledge gaps and clusters. Supplementary Information: The online version contains supplementary material available at 10.1186/s13750-024-00345-2.

Normative longitudinal EEG recordings during sleep stage II in the first year of age.

The electroencephalogram (EEG) is a fundamental diagnostic procedure that explores brain function. This manuscript describes the characteristics of a sample of healthy at-term infants. One hundred and three (103) infants from Mexico between 15 days and 12.5 months of age were recorded during physiological sleep. Referential EEG recordings were obtained using linked ear lobes as reference. The amplifier gain was 10,000, the bandwidth was set between 0.3 and 30 Hz, and the sample rate was 200 Hz. Sample windows of 2.56 s were marked for later quantitative analysis. To our knowledge, this is the first dataset of normal infants during the first year of age.

A High-Speed Acoustic Echo Canceller Based on Grey Wolf Optimization and Particle Swarm Optimization Algorithms.

Currently, the use of acoustic echo cancellers (AECs) plays a crucial role in IoT applications, such as voice control appliances, hands-free telephony and intelligent voice control devices, among others. Therefore, these IoT devices are mostly controlled by voice commands. However, the performance of these devices is significantly affected by echo noise in real acoustic environments. Despite good results being achieved in terms of echo noise reductions using conventional adaptive filtering based on gradient optimization algorithms, recently, the use of bio-inspired algorithms has attracted significant attention in the science community, since these algorithms exhibit a faster convergence rate when compared with gradient optimization algorithms. To date, several authors have tried to develop high-performance AEC systems to offer high-quality and realistic sound. In this work, we present a new AEC system based on the grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms to guarantee a higher convergence speed compared with previously reported solutions. This improvement potentially allows for high tracking capabilities. This aspect has special relevance in real acoustic environments since it indicates the rate at which noise is reduced.

Unraveling motor imagery brain patterns using explainable artificial intelligence based on Shapley values.

BACKGROUND AND OBJECTIVE: Motor imagery (MI) based brain-computer interfaces (BCIs) are widely used in rehabilitation due to the close relationship that exists between MI and motor execution (ME). However, the underlying brain mechanisms of MI remain not well understood. Most MI-BCIs use the sensorimotor rhythms elicited in the primary motor cortex (M1) and somatosensory cortex (S1), which consist of an event-related desynchronization followed by an event-related synchronization. Consequently, this has resulted in systems that only record signals around M1 and S1. However, MI could involve a more complex network including sensory, association, and motor areas. In this study, we hypothesize that the superior accuracies achieved by new deep learning (DL) models applied to MI decoding rely on focusing on a broader MI activation of the brain. Parallel to the success of DL, the field of explainable artificial intelligence (XAI) has seen continuous development to provide explanations for DL networks success. The goal of this study is to use XAI in combination with DL to extract information about MI brain activation patterns from non-invasive electroencephalography (EEG) signals. METHODS: We applied an adaptation of Shapley additive explanations (SHAP) to EEGSym, a state-of-the-art DL network with exceptional transfer learning capabilities for inter-subject MI classification. We obtained the SHAP values from two public databases comprising 171 users generating left and right hand MI instances with and without real-time feedback. RESULTS: We found that EEGSym based most of its prediction on the signal of the frontal electrodes, i.e. F7 and F8, and on the first 1500 ms of the analyzed imagination period. We also found that MI involves a broad network not only based on M1 and S1, but also on the prefrontal cortex (PFC) and the posterior parietal cortex (PPC). We further applied this knowledge to select a 8-electrode configuration that reached inter-subject accuracies of 86.5% ± 10.6% on the Physionet dataset and 88.7% ± 7.0% on the Carnegie Mellon University's dataset. CONCLUSION: Our results demonstrate the potential of combining DL and SHAP-based XAI to unravel the brain network involved in producing MI. Furthermore, SHAP values can optimize the requirements for out-of-laboratory BCI applications involving real users.

Discovery of dual-action phenolic 4-arylidene-isoquinolinones with antioxidant and α-glucosidase inhibition activities.

A multicomponent-derived synthesis of arylidene isoquinolinones decorated with phenolic moieties is described. The series demonstrated good DPPH trapping and, in the case of sinapic acid-containing analogs, excellent activity against lipoperoxidation; EPR also demonstrated that one derivative scavenged hydroxyl radicals. In addition, some compounds showed excellent inhibition of α-glucosidase activity and, according to both Lineweaver-Burk plots and molecular docking, they act as non-competitive or mixed inhibitors. In vitro assay also demonstrated that two compounds significantly reduced the plasma glucose levels after sucrose administration. In summary, the studied isoquinolinones become novel compounds with dual action (antioxidant and α-glucosidase inhibition) against diabetes and related metabolic diseases, whose optimization would lead to more potent candidates.

Synthesis, antibiofilm activity and molecular docking of N-acylhomoserine lactones containing cinammic moieties.

We prepared a series of cinnamoyl-containing furanones by an affordable and short synthesis. The nineteen compounds hold a variety of substituents including electron-donating, electron-withdrawing, bulky and meta-substituted phenyls, as well as heterocyclic rings. Compounds showed antibiofilm activity in S. aureus, K. pneumoniae and, more pronounced, against P. aeruginosa. The disruption of quorum sensing (QS) was tested using the violacein test and molecular docking predicted the antagonism of LasR as a plausible mechanism of action. The trimethoxylated and diene derivatives showed the best antibiofilm and anti-QS properties, thus becoming candidates for further modifications.

Assessing the Potential of Brain-Computer Interface Multiplayer Video Games using c-VEPs: A Pilot Study.

Video games have become a common and widespread form of entertainment, while non-invasive brain-computer interfaces (BCI) are emerging as potential alternative communication technologies. Combining BCIs and video games can enhance the gaming experience and make it accessible to motor-disabled individuals. Recently, code-modulated visual evoked potentials (c-VEP) have been proposed as a novel control signal able to achieve high performance with short calibration times. However, there are still no video games that use c-VEPs as a control signal. The aim of this pilot study is to develop an implementation of the 'Connect 4' multiplayer video game using a c-VEP-based BCI and test it with 10 healthy users. Participants were paired to compete in matches and carried out individual tasks. The results showed that the participants were able to control the game with an average accuracy of 94.10% and a selection time of 5.25 seconds per command, outperforming previous approaches. This suggests that the proposed video game is feasible and c-VEPs can provide smooth BCI control.

Nonparametric Early Stopping Detection for c-VEP-based Brain-Computer Interfaces: A Pilot Study.

Brain-computer interface (BCI) systems based on code-modulated visual evoked potentials (c-VEP) stand out for achieving excellent command selection accuracies with very short calibration times. One of the natural steps to democratize their use in plug-and-play environments is to develop early stopping algorithms. These methods allow real-time detection of the minimum number of code repetitions needed to provide reliable selections. However, such techniques are scarce in the current state-of-the-art for c-VEP-based BCI systems based on the classical circular shifting paradigm. Here, a novel nonparametric early stopping method is proposed, which approximates the distribution of unattended commands to a normal distribution and issues a selection when the correlation of the command is considered an outlier. The proposal has been evaluated offline with 15 healthy users, achieving an average accuracy of 97.08% and a speed of 1.37 s/command. Likewise, the algorithm has also been evaluated with an additional user in an online way, as a proof of concept to validate its technical feasibility, achieving an average accuracy of 96.88% with a speed of 1.67 s/command. These results suggest that the real time application of the proposed algorithm is feasible, significantly reducing the required selection time without compromising accuracy.

Embodied strategies for public speaking anxiety: evaluation of the Corp-Oral program.

Background: Public speaking is an indispensable skill that can profoundly influence success in both professional and personal spheres. Regrettably, managing anxiety during a speech poses a significant challenge for many of the population. This research assessed the impacts of a Corp-Oral program, designed to manage public speaking anxiety in university students, based on, body awareness, embodied message techniques, simulation, embodied visualization, body transformation, and gesture enhancement. Methods: Thirty-six students (61% women; Mage = 20.22, SD = 1.23 years) were randomly assigned to either an experimental group (n = 18), which underwent the Corp-Oral program, or a control group (n = 18). Self-perceived anxiety, heart rate, and electroencephalography were measured in a pre-test and a post-test. Results: The study reveals that the Corp-Oral program significantly (p < 0.005) reduced both physiological responses (heart rate) and self-reported measures of anxiety. The alteration was more noticeable in self-reported anxiety measures (a decrease of 33.217%) than in heart rate (a decrease of 4.659%). During the speech, the experimental group exhibited increased cortical activation in areas related to emotional regulation, consciousness, sensorimotor integration, and movement control. A significant increase in frontal alpha asymmetry was observed for the experimental group in the post-test, but there were no significant variations in the theta/beta ratio. Conclusion: These findings underline the benefit of managing public speaking anxiety not merely by reducing it but by channeling it through embodied strategies. These strategies could lead to greater action awareness that would cushion the physiological effect of the anxiety response and help generate a better self-perception of the anxiety state.

Third National Registry of Cardiac Rehabilitation Programs in Mexico (RENAPREC III-2022) / Tercer Registro Nacional de Programas de Rehabilitación Cardiaca en México (RENAPREC III-2022)

Abstract Introduction: In Mexico, cardiac rehabilitation (CR) as an interdisciplinary intervention with therapeutic impact in patients with heart disease is growing. There is the need to know actual conditions of CR in our country. Objectives: The objective of this National Registry is to follow-up those existing and new CR units in Mexico through the comparison between the two previous registries, RENAPREC-2009 and RENAPREC II-2015 studies. This is a descriptive study focused on diverse CR activities such as assistance training, and certification of health professionals, barriers, reference, population attended, interdisciplinarity, permanence over time, growth prospects, regulations, post-pandemic condition, integrative characteristics, and scientific research. Results: Data were collected from 45 CR centers in the 32 states, 75.5% are private practice units, 67% are new, 33% were part of RENAPREC II-2015, and 17 have continued since 2009. With a better distribution of CR units along the territory, the median reference of candidates for CR programs is 9% with a significant reduction into tiempo of enrollment to Phase II admission (19 ± 11 days). Regarding to previous registries, the coverance of Phases I, II, and III is 71%, 100%, and 93%, respectively; and a coverance increases in evaluation, risk stratification, and prescription, more comprehensive attendance and prevention strategies. Conclusions: CR in Mexico has grown in the past 7 years. Even there is still low reference and heterogeneity in specific processes, there are strengths such as interdisciplinarity, scientific professionalization of specialists, national diversification, and an official society that are consolidated over time.

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Influence of spatial frequency in visual stimuli for cVEP-based BCIs: evaluation of performance and user experience.

Code-modulated visual evoked potentials (c-VEPs) are an innovative control signal utilized in brain-computer interfaces (BCIs) with promising performance. Prior studies on steady-state visual evoked potentials (SSVEPs) have indicated that the spatial frequency of checkerboard-like stimuli influences both performance and user experience. Spatial frequency refers to the dimensions of the individual squares comprising the visual stimulus, quantified in cycles (i.e., number of black-white squares pairs) per degree of visual angle. However, the specific effects of this parameter on c-VEP-based BCIs remain unexplored. Therefore, the objective of this study is to investigate the role of spatial frequency of checkerboard-like visual stimuli in a c-VEP-based BCI. Sixteen participants evaluated selection matrices with eight spatial frequencies: C001 (0 c/°, 1×1 squares), C002 (0.15 c/°, 2×2 squares), C004 (0.3 c/°, 4×4 squares), C008 (0.6 c/°, 8×8 squares), C016 (1.2 c/°, 16×16 squares), C032 (2.4 c/°, 32×32 squares), C064 (4.79 c/°, 64×64 squares), and C128 (9.58 c/°, 128×128 squares). These conditions were tested in an online spelling task, which consisted of 18 trials each conducted on a 3×3 command interface. In addition to accuracy and information transfer rate (ITR), subjective measures regarding comfort, ocular irritation, and satisfaction were collected. Significant differences in performance and comfort were observed based on different stimulus spatial frequencies. Although all conditions achieved mean accuracy over 95% after 2.1 s of trial duration, C016 stood out in terms user experience. The proposed condition not only achieved a mean accuracy of 96.53% and 164.54 bits/min with a trial duration of 1.05s, but also was reported to be significantly more comfortable than the traditional C001 stimulus. Since both features are key for BCI development, higher spatial frequencies than the classical black-to-white stimulus might be more adequate for c-VEP systems. Hence, we assert that the spatial frequency should be carefully considered in the development of future applications for c-VEP-based BCIs.

Combining brain-computer interfaces and multiplayer video games: an application based on c-VEPs.

Introduction and objective: Video games are crucial to the entertainment industry, nonetheless they can be challenging to access for those with severe motor disabilities. Brain-computer interfaces (BCI) systems have the potential to help these individuals by allowing them to control video games using their brain signals. Furthermore, multiplayer BCI-based video games may provide valuable insights into how competitiveness or motivation affects the control of these interfaces. Despite the recent advancement in the development of code-modulated visual evoked potentials (c-VEPs) as control signals for high-performance BCIs, to the best of our knowledge, no studies have been conducted to develop a BCI-driven video game utilizing c-VEPs. However, c-VEPs could enhance user experience as an alternative method. Thus, the main goal of this work was to design, develop, and evaluate a version of the well-known 'Connect 4' video game using a c-VEP-based BCI, allowing 2 users to compete by aligning 4 same-colored coins vertically, horizontally or diagonally. Methods: The proposed application consists of a multiplayer video game controlled by a real-time BCI system processing 2 electroencephalograms (EEGs) sequentially. To detect user intention, columns in which the coin can be placed was encoded with shifted versions of a pseudorandom binary code, following a traditional circular shifting c-VEP paradigm. To analyze the usability of our application, the experimental protocol comprised an evaluation session by 22 healthy users. Firstly, each user had to perform individual tasks. Afterward, users were matched and the application was used in competitive mode. This was done to assess the accuracy and speed of selection. On the other hand, qualitative data on satisfaction and usability were collected through questionnaires. Results: The average accuracy achieved was 93.74% ± 1.71%, using 5.25 seconds per selection. The questionnaires showed that users felt a minimal workload. Likewise, high satisfaction values were obtained, highlighting that the application was intuitive and responds quickly and smoothly. Conclusions: This c-VEP based multiplayer video game has reached suitable performance on 22 users, supported by high motivation and minimal workload. Consequently, compared to other versions of "Connect 4" that utilized different control signals, this version has exhibited superior performance.

Methemoglobinemia in Hemodialysis Patients due to Acute Chlorine Intoxication: A Case Series Calling Attention on an Old Problem.

INTRODUCTION: Hemodialysis uses municipal water that must be strictly purified and sterilized to be used for that procedure. Large amounts of decontaminants are often used, such as chlorine, and if these compounds are not subsequently removed they can be transferred to the blood of patients causing complications including methemoglobinemia. METHODS: In this case series study, dialysis patients in one unit were evaluated. We reviewed clinical characteristics and laboratory findings obtained on the day when the water supply was disinfected with chlorine, with the aim to quantify methemoglobin concentrations. Our objective was to characterize the clinical presentation and management of patients who presented with methemoglobinemia on a specific index day. We also reviewed reported cases in the literature regarding this underreported complication. RESULTS: Eight patients who presented with chlorine intoxication were evaluated. The methemoglobin concentrations were between 1.3% and 7.9% (reference value 0-1%). We believe this to be caused by water containing 0.78 mg/L of total chlorine. Seven patients presented with cyanosis, 4 with dizziness, 6 with dark brown blood, 4 with dyspnea, and 4 with headache and hemolytic anemia. Subjects were treated with supplemental oxygen, methylene blue, intravenous vitamin C, blood transfusions, and increased doses of erythropoietin. No patient died, and all continued with their usual hemodialysis sessions. CONCLUSION: Acute chlorine intoxication transferred by the water used during hemodialysis sessions can present with methemoglobinemia accompanied by cyanosis, oxygen desaturation, and hemolytic anemia. Chlorine levels should be carefully monitored in the water used for hemodialysis treatment.

A regioselective synthesis of 3,4-diaryl-1H-pyrazoles through a 1,3-dipolar cycloaddition of tosylhydrazones and nitroalkenes.

A procedure for the selective synthesis of 3,4-diaryl-1H-pyrazoles through a 1,3-dipolar cycloaddition is reported. The transformation occurred under mild conditions using affordable tosylhydrazones and nitroalkenes commencing from benzaldehydes/heteroaromatic aldehydes as starting materials. Due to the versatility of this protocol, we prepared a vast collection of 3,4-diaryl-1H-pyrazoles, which included the incorporation of heterocyclic rings at the pyrazole core. Two-dimensional NMR techniques (2D-NOESY and HMBC) confirmed the regioselectivity of the transformation and correlated well with DFT calculations. Accordingly, the analysis of the transition states indicated that the 3,4-diaryl product corresponded to the product with the lowest activation energy and led to the most stable product. Finally, the series was evaluated against three cancer cell lines, with compound 8f being the most remarkable analog in terms of activity and extraordinary selectivity towards PC-3 compared to the other cell lines (including COS-7).

A Real-Time FPGA-Based Metaheuristic Processor to Efficiently Simulate a New Variant of the PSO Algorithm.

Nowadays, high-performance audio communication devices demand superior audio quality. To improve the audio quality, several authors have developed acoustic echo cancellers based on particle swarm optimization algorithms (PSO). However, its performance is reduced significantly since the PSO algorithm suffers from premature convergence. To overcome this issue, we propose a new variant of the PSO algorithm based on the Markovian switching technique. Furthermore, the proposed algorithm has a mechanism to dynamically adjust the population size over the filtering process. In this way, the proposed algorithm exhibits great performance by reducing its computational cost significantly. To adequately implement the proposed algorithm in a Stratix IV GX EP4SGX530 FPGA, we present for the first time, the development of a parallel metaheuristic processor, in which each processing core simulates the different number of particles by using the time-multiplexing technique. In this way, the variation of the size of the population can be effective. Therefore, the properties of the proposed algorithm along with the proposed parallel hardware architecture potentially allow the development of high-performance acoustic echo canceller (AEC) systems.

[Third National Registry of Cardiac Rehabilitation Programs in Mexico (RENAPREC III-2022)]. / Tercer Registro Nacional de Programas de Rehabilitación Cardiaca en México (RENAPREC III-2022).

Introduction: In Mexico, cardiac rehabilitation (CR) as an interdisciplinary intervention with therapeutic impact in patients with heart disease is growing. There is the need to know actual conditions of CR in our country. Objectives: The objective of this National Registry is to follow-up those existing and new CR units in Mexico through the comparison between the two previous registries, RENAPREC-2009 and RENAPREC II-2015 studies. This is a descriptive study focused on diverse CR activities such as assistance training, and certification of health professionals, barriers, reference, population attended, interdisciplinarity, permanence over time, growth prospects, regulations, post-pandemic condition, integrative characteristics, and scientific research. Results: Data were collected from 45 CR centers in the 32 states, 75.5% are private practice units, 67% are new, 33% were part of RENAPREC II-2015, and 17 have continued since 2009. With a better distribution of CR units along the territory, the median reference of candidates for CR programs is 9% with a significant reduction into tiempo of enrollment to Phase II admission (19 ± 11 days). Regarding to previous registries, the coverance of Phases I, II, and III is 71%, 100%, and 93%, respectively; and a coverance increases in evaluation, risk stratification, and prescription, more comprehensive attendance and prevention strategies. Conclusions: CR in Mexico has grown in the past 7 years. Even there is still low reference and heterogeneity in specific processes, there are strengths such as interdisciplinarity, scientific professionalization of specialists, national diversification, and an official society that are consolidated over time.

Introducción: En México, la Rehabilitación Cardíaca (RC) como intervención interdisciplinaria con impacto terapéutico en paciente con cardiopatía está en crecimiento. Existe la necesidad de conocer las condiciones actuales de la RC en nuestro país. Objetivo: El objetivo de este Registro es dar seguimiento comparativo de las unidades nuevas y existentes entre los registros anteriores, RENAPREC-2009 y RENAPREC II-2015. Se trata de un estudio descriptivo centrado en diversas actividades de la RC: formación asistencial y certificación de sus profesionales, barreras, referencia, población atendida, interdisciplinariedad, permanencia en el tiempo, perspectivas de crecimiento, normativa, condición pospandemia, características integradoras e investigación. Resultados: Se recolectaron datos de 45 centros en los 32 estados, 67% son nuevos 75.5% son de práctica privada, 33% fueron parte de RENAPREC II-2015 y 17 desde 2009. Con una mejor distribución de las unidades de RC a lo largo del territorio, la mediana de referencia de pacientes candidatos a RC es ahora del 9% con reducción significativa del tiempo de admisión a Fase II (19 ± 11 días). Respecto a registros anteriores las coberturas de las Fases I, II y III son del 71%, 100% y 93%, respectivamente; con un aumento de la cobertura en evaluación, estratificación de riesgo y prescripción, atención más integral y estrategias de prevención. Conclusiones: La RC en México ha crecido en los últimos 7 años. Si bien aún existe baja referencia y heterogeneidad en procesos específicos, existen fortalezas como la interdisciplinariedad, la profesionalización científica de los especialistas, la diversificación nacional y una sociedad oficial que se consolida en el tiempo.

ITACA: An open-source framework for Neurofeedback based on Brain-Computer Interfaces.

BACKGROUND AND OBJECTIVE: Neurofeedback (NF) is a paradigm that allows users to self-modulate patterns of brain activity. It is implemented with a closed-loop brain-computer interface (BCI) system that analyzes the user's brain activity in real-time and provides continuous feedback. This paradigm is of great interest due to its potential as a non-pharmacological and non-invasive alternative to treat non-degenerative brain disorders. Nevertheless, currently available NF frameworks have several limitations, such as the lack of a wide variety of real-time analysis metrics or overly simple training scenarios that may negatively affect user performance. To overcome these limitations, this work proposes ITACA: a novel open-source framework for the design, implementation and evaluation of NF training paradigms. METHODS: ITACA is designed to be easy-to-use, flexible and attractive. Specifically, ITACA includes three different gamified training scenarios with a choice of five brain activity metrics as real-time feedback. Among them, novel metrics based on functional connectivity and network theory stand out. It is complemented with five different computerized versions of widespread cognitive assessment tests. To validate the proposed framework, a computational efficiency analysis and an NF training protocol focused on frontal-medial theta modulation were conducted. RESULTS: Efficiency analysis proved that all implemented metrics allow an optimal feedback update rate for conducting NF sessions. Furthermore, conducted NF protocol yielded results that support the use of ITACA in NF research studies. CONCLUSIONS: ITACA implements a wide variety of features for designing, conducting and evaluating NF studies with the goal of helping researchers expand the current state-of-the-art in NF training.

MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research.

BACKGROUND AND OBJECTIVE: Neurotechnologies have great potential to transform our society in ways that are yet to be uncovered. The rate of development in this field has increased significantly in recent years, but there are still barriers that need to be overcome before bringing neurotechnologies to the general public. One of these barriers is the difficulty of performing experiments that require complex software, such as brain-computer interfaces (BCI) or cognitive neuroscience experiments. Current platforms have limitations in terms of functionality and flexibility to meet the needs of researchers, who often need to implement new experimentation settings. This work was aimed to propose a novel software ecosystem, called MEDUSA©, to overcome these limitations. METHODS: We followed strict development practices to optimize MEDUSA© for research in BCI and cognitive neuroscience, making special emphasis in the modularity, flexibility and scalability of our solution. Moreover, it was implemented in Python, an open-source programming language that reduces the development cost by taking advantage from its high-level syntax and large number of community packages. RESULTS: MEDUSA© provides a complete suite of signal processing functions, including several deep learning architectures or connectivity analysis, and ready-to-use BCI and neuroscience experiments, making it one of the most complete solutions nowadays. We also put special effort in providing tools to facilitate the development of custom experiments, which can be easily shared with the community through an app market available in our website to promote reproducibility. CONCLUSIONS: MEDUSA© is a novel software ecosystem for modern BCI and neurotechnology experimentation that provides state-of-the-art tools and encourages the participation of the community to make a difference for the progress of these fields. Visit the official website at https://www.medusabci.com/ to know more about this project.