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Business Process OutsourcingTop 10 Best Bci Software of 2026
Ranked top 10 bci software for teams, comparing Salesforce Service Cloud, Microsoft Dynamics 365, Zendesk, Timeflux, EEGLAB, and EEG tools.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
Agents extract product evidence with provenance, then collect written reviews and transcribe video reviews and podcasts.
Framework: Features 40% · Ease 30% · Value 30%
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Timeflux is the best pick if you’re a research team that needs repeatable, API-integrated BCI inference from acquisition through closed-loop output, whereas EEGLAB fits when your lab stays in MATLAB and wants consistent preprocessing, decomposition, and review.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Timeflux
Processing-graph orchestration that keeps identical preprocessing and decoding logic across calibration and live runs.
Best for: Fits when research teams need repeatable, API-integrated BCI inference from acquisition to closed-loop output.
EEGLAB
Editor pickICA-based component exploration and rejection is integrated into dataset workflows and plotting tools.
Best for: Fits when labs need repeatable EEG preprocessing, decomposition, and review in Matlab.
EEGLAB
Editor pickEEGLAB’s MATLAB EEG data structure and event metadata flow through most preprocessing and analysis functions.
Best for: Fits when lab teams need scriptable EEG preprocessing and offline decoding experiments without a production-time runtime.
Comparison Table
Timeflux
API-firstOpen-source Python framework for real-time brain-computer interface and biosignal processing applications.
Processing-graph orchestration that keeps identical preprocessing and decoding logic across calibration and live runs.
Timeflux supports end-to-end BCI workflows that start at signal ingestion and proceed through processing steps like artifact rejection, filtering, and feature computation before reaching a decoder and output actions. It provides a consistent configuration-driven approach to building classification and inference pipelines, which reduces drift between calibration sessions and later runs. Integration is practical for teams that need to connect device outputs and stream decoded results to control logic, visualization, or experiment scripts through its automation and API interfaces.
A tradeoff is that Timeflux works best when teams can formalize their workflow into a processing graph and accept the configuration model as the primary way to extend behavior. It fits teams that run recurring calibration workflows and require repeatable real-time inference behavior across many participants, devices, and experimental variants.
- +Configuration-driven processing graphs make BCI pipelines reproducible
- +Real-time streaming supports closed-loop inference to external consumers
- +Extensibility via APIs supports custom operators and decoders
- +Run history and versioned configs improve experiment governance
- –Effective use depends on modeling workflow as a graph configuration
- –Device integration can require targeted adapters for each acquisition stack
BCI research teams
Run identical pipelines across participants
Fewer pipeline deviations
Neural engineering teams
Integrate new decoders and features
Faster decoder iteration
Show 2 more scenarios
Experimental control engineers
Drive stimulation or UI from inference
Stable closed-loop behavior
Streaming outputs feed downstream control logic with timing aligned to the live inference loop.
Clinical study operators
Manage calibration run consistency
Better audit readiness
Versioned configurations and run history provide operational traceability for calibration workflows.
Best for: Fits when research teams need repeatable, API-integrated BCI inference from acquisition to closed-loop output.
EEGLAB
specialistEEGLAB is a MATLAB toolbox for processing, visualizing, and analyzing EEG data.
ICA-based component exploration and rejection is integrated into dataset workflows and plotting tools.
Researchers and lab teams use EEGLAB for calibration-to-analysis workflows where consistent preprocessing and repeatable plots matter across sessions. The toolbox provides interactive steps for filtering, re-referencing, epoching, and artifact rejection, and it supports batch processing through scripts for repeat runs. Its data handling centers on EEGLAB dataset objects that keep channel, time, event, and ICA metadata together through typical analysis steps.
A key tradeoff is that device connectivity and real-time inference are not its native focus, since EEGLAB mainly processes recorded EEG rather than orchestrating live stimulation control loops. EEGLAB fits best when a workflow already produces standard EEG recordings in Matlab-readable formats and when the team needs flexible preprocessing plus visualization rather than a managed, production-grade pipeline.
- +Interactive preprocessing with scripted batch runs for repeatable pipelines
- +ICA workflow with tight integration into dataset handling and inspection
- +Broad plugin support for adding analysis steps and custom processing
- +High-quality visualization for components, epochs, and rejected data
- –Limited out-of-the-box device integration for live data capture
- –Matlab dependency increases friction for teams standardizing on other stacks
- –Closed-loop and stimulation control require external systems and custom glue
- –Large datasets can become slow in interactive review steps
Neuroscience labs analyzing EEG
Clean and decompose sessions consistently
Cleaner components and better epochs
BCI researchers building offline pipelines
Prepare features for decoding models
Reusable training-ready datasets
Show 1 more scenario
Multisite studies with standardized QC
Unify preprocessing and visualization review
Lower variance across cohorts
Run scripted preprocessing across sessions and use consistent plots for quality checks.
Best for: Fits when labs need repeatable EEG preprocessing, decomposition, and review in Matlab.
EEGLAB
enterpriseMATLAB toolbox for electrophysiological signal analysis including independent component analysis and artifact rejection.
EEGLAB’s MATLAB EEG data structure and event metadata flow through most preprocessing and analysis functions.
EEGLAB’s workflow centers on an EEG data structure in the MATLAB environment, so preprocessing stages like filtering, re-referencing, and artifact handling operate on a consistent in-memory representation. Tooling and extensions cover spatial filtering, feature extraction, and classifier training for offline evaluation, which fits calibration workflows and research iterations where methods change frequently. The automation surface is mainly script-driven through MATLAB functions and batch runs, so integration with acquisition systems usually happens via imported files or custom MATLAB code rather than a standardized device SDK.
A key tradeoff is that EEGLAB is not built around closed-loop execution from a live acquisition stream, so real-time inference and stimulation control require external wrappers. EEGLAB is a strong match for lab teams building and validating EEG analysis pipelines for motor imagery studies, event-related experiments, or speller-like stimulus protocols using offline or semi-offline data handling.
- +MATLAB scripting enables reproducible preprocessing and experiment reruns
- +Extensive add-on ecosystem for EEG processing and research pipelines
- +Event and trial handling supports structured calibration and segmentation
- +Rich visualization tools speed QA of filters and artifact removal
- –MATLAB-centric workflow slows production-grade integration into non-MATLAB stacks
- –Real-time closed-loop inference needs external integration work
- –Device integration is indirect for many acquisition systems
- –Complex pipelines can become fragile when custom scripts accumulate
Neurophysiology research teams
Build repeatable EEG preprocessing pipelines
Comparable datasets across sessions
BCI algorithm developers
Prototype decoders from recorded trials
Faster method iteration cycles
Show 2 more scenarios
Neural data analysts
QA artifacts and preprocessing effects
Lower preprocessing error rates
Use interactive views to inspect channel quality and verify the impact of cleaning steps.
BCI calibration study leads
Define trials and calibration epochs
Cleaner calibration inputs
Segment data around event markers to support calibration workflows for downstream decoding steps.
Best for: Fits when lab teams need scriptable EEG preprocessing and offline decoding experiments without a production-time runtime.
g.tec BCI Software
vertical specialistg.tec provides software for EEG acquisition, signal processing, and brain-computer interface applications.
End-to-end experiment execution that couples device-connected acquisition, processing configuration, and decoder run control in one workflow.
g.tec BCI Software is a workflow-focused toolchain for brain-computer interface experiments that centers on g.tec device connectivity and end-to-end signal-to-classification execution. It supports neural signal processing steps such as filtering and feature extraction alongside experiment control and recorded dataset handling.
- +Tight integration with g.tec signal acquisition and device drivers
- +Configurable processing chain supports repeatable preprocessing and decoding runs
- +Experiment control and data recording are built into the same workflow
- +Reusable session configuration reduces variance across calibration attempts
- –G.tec-centric integration can limit out-of-ecosystem device fit
- –Advanced pipeline tuning needs engineering discipline to stay reproducible
- –Automation depth for custom external decoders is narrower than general middleware
- –Feature extraction and model iteration workflows can feel heavier than lightweight lab tools
Best for: Fits when teams standardize acquisition and decoding on g.tec hardware and need consistent calibration-to-inference workflows.
OpenBCI GUI
specialistOpenBCI GUI provides visual tools for recording and analyzing biosignal data from OpenBCI hardware.
Channel and signal status views built for operator-led acquisition sessions with trigger-aware recording control.
OpenBCI GUI supports real-time EEG monitoring during acquisition with live channel visualization and session-level controls.
- +Live channel-level visualization helps operators spot dropouts and noisy leads quickly
- +Built for session control during acquisition with operator-friendly recording workflows
- +Direct compatibility with OpenBCI device streaming reduces glue code for demos
- +Event and trigger handling supports synchronized experimental logging
- –Limited automation and API surface compared with decoder pipelines built around code
- –Governance controls like RBAC and audit logging are not a focus for shared labs
- –Workflow depends on OpenBCI-specific device ecosystem compatibility
- –Advanced artifact rejection and filtering are limited to GUI-supported controls
Best for: Fits when labs need operator-driven EEG acquisition control and real-time monitoring tied to OpenBCI hardware.
BrainFlow
API-firstBrainFlow offers a hardware-independent API for acquiring and processing biosignal data.
A unified Python API for streaming and offline neural data handling across multiple supported biosignal board integrations.
BrainFlow targets brain-computer interface teams that need end-to-end code to move from neural signal acquisition to analysis and inference with minimal glue code. The project emphasizes a Python-first workflow that standardizes streaming, board setup, and data handling across supported EEG and biosignal devices.
- +Python-first APIs cover streaming, buffering, and offline analysis workflows
- +Device abstraction reduces custom code for common EEG acquisition paths
- +Built-in filtering and preprocessing utilities support repeatable pipelines
- +Batch and real-time execution modes fit closed-loop development loops
- –Production-grade governance controls like RBAC and audit logs are not the focus
- –Decoder development still requires engineering work beyond basic preprocessing
- –Hardware coverage depends on the set of supported boards and drivers
- –Complex multi-device setups can require careful configuration discipline
Best for: Fits when engineering teams need a Python API for neural signal processing and inference pipelines with consistent device data access.
OpenViBE
specialistOpenViBE provides a graphical environment for designing and running real-time neuroscience applications.
OpenViBE’s visual workflow editor maps processing nodes into runnable real-time graphs for calibration and online sessions.
OpenViBE is an open-source brain-computer interface software suite that centers on a visual pipeline for neural signal processing and real-time inference. It provides a workflow editor for assembling acquisition, filtering, feature extraction, and classifier steps with tight timing for closed-loop experiments.
- +Graph-based workflow editor covers acquisition to online classification end-to-end
- +Extensible module system supports custom signal processing and decoding components
- +Real-time execution model targets closed-loop timing for online experiments
- +Built-in calibration and online session patterns reduce experiment rework
- –Workflow setup requires disciplined configuration of sampling rates and channel mappings
- –Device integration quality varies across acquisition stacks and may need custom components
Best for: Fits when research and engineering teams need reproducible BCI workflows with real-time execution and extensibility.
BrainStorm
enterpriseOpen-source MATLAB and Python toolbox for MEG and EEG source imaging and connectivity analysis.
Real-time pipeline orchestration that couples streaming buffers, filtering stages, and classifier outputs for closed-loop feedback trials.
BrainStorm from the USC neuroimaging group targets end-to-end BCI research workflows by pairing neural signal processing with experiment execution logic and neural decoding components. It emphasizes configurable pipelines for online inference testing, including real-time buffering, filtering stages, and classifier outputs suitable for closed-loop neurofeedback experiments.
- +Configurable online decoding pipelines with explicit real-time execution steps
- +Event-synchronized processing supports time-locked BCI tasks and feedback
- +Structured experiment logic reduces custom glue code for runs and calibration
- +Works well for EEG-oriented research that needs iterative pipeline tuning
- –Device integration depends on lab-specific drivers and data format alignment
- –Automation and API extensibility are limited for enterprise-style governance
- –Advanced workflows require careful parameter tuning across pipeline stages
- –Multi-site deployment and RBAC controls are not a primary focus
Best for: Fits when research teams need configurable online inference workflows for EEG-linked BCI experiments.
LSL
API-firstOpen-source framework for synchronizing multi-modal data streams including EEG, markers, and auxiliary sensors in real time.
LSL stream discovery and timestamped data flow let independent processes join an experiment session with consistent event and channel timing.
LSL performs standardized signal streaming for brain-computer interface experiments by turning device outputs into time-aligned streams. It provides a real-time pipeline built around stream discovery, timestamp handling, and pluggable consumers for signal processing, markers, and inference.
- +Time-stamped stream model keeps signals and events aligned across devices
- +Stream discovery reduces wiring effort when adding new acquisition or processing nodes
- +Code-level APIs support custom processing and inference consumers
- +Centralizes event markers alongside continuous data for synchronized logs
- –Requires build and configuration discipline to keep stream definitions consistent
- –High-volume real-time workloads need careful performance tuning
- –Does not include a full end-to-end decoder and stimulation control workflow
- –Operational monitoring is limited compared with enterprise data pipelines
Best for: Fits when teams need synchronized neural data streaming across heterogeneous EEG or BCI tools without building custom transport layers.
PsychoPy
vertical specialistOpen-source Python library for presenting stimuli and collecting behavioral data in neuroscience experiments.
Tight coupling of PsychoPy’s visual stimulus timing with EEG-connected real-time decision loops in Python.
PsychoPy is an open-source brain-computer interface software stack built around a Python scripting workflow. It pairs stimulus presentation and experimental timing with EEG analysis and real-time control via a modular codebase.
- +Python-first experiment scripting keeps stimulus timing and logic in one codebase
- +Built-in EEG preprocessing steps cover filtering, resampling, and common artifact handling
- +Real-time loops support closed-loop feedback tied to streaming or periodically updated data
- +Extensible processing and model code supports custom pipelines without rigid UI constraints
- –Studio-style administration and governance controls are limited for multi-lab deployments
- –Advanced setups require careful coding discipline for signal timing and data alignment
- –Production-grade monitoring and audit logging are not native features
- –Device and data-format integration can rely on community modules and custom adapters
Best for: Fits when labs need Python-coded experimental timing plus EEG pipelines in a single workflow.
Conclusion
After evaluating 10 bci software, Timeflux stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Bci Software
BCI software in this guide covers end-to-end workflow tooling for neural signal processing, real-time inference, and closed-loop outputs, with emphasis on integration depth between acquisition, decoding, and external consumers. The coverage includes Timeflux, EEGLAB, g.tec BCI Software, OpenViBE, BrainFlow, and other production-adjacent options that differ in orchestration style.
The reviewed tools include graph-driven pipelines in Timeflux, MATLAB-centric preprocessing and ICA workflows in EEGLAB, g.tec’s device-coupled experiment execution, and OpenViBE’s visual workflow editor that runs online classification graphs. The list also includes OpenBCI GUI for operator-led session control, plus BrainFlow, BrainStorm, LSL, and PsychoPy for teams that prioritize Python APIs, synchronized streaming, or Python-coded stimulus timing.
BCI Software for neural decoding workflows, from acquisition pipelines to closed-loop execution
BCI software is the toolchain that links neural signal acquisition to preprocessing, decoding, and real-time control loops for brain-computer interface experiments and deployments. It can run offline analysis for repeatable experiment reruns or execute online graphs for live inference and time-locked feedback.
Timeflux represents an orchestration approach that keeps identical preprocessing and decoding logic across calibration and live runs through configuration-driven processing graphs. OpenViBE represents a workflow editor approach that maps processing nodes into runnable real-time graphs for calibration and online sessions, with an extensible module system for custom components.
BCI Software evaluation: orchestration, integration, and runtime control
BCI workflows fail in predictable ways when preprocessing and decoding logic diverges between calibration and live inference runs. The highest-control tools keep processing steps consistent, then expose live execution inputs for downstream consumers.
Real-time work also depends on how software moves data and timing signals across components. Tooling that clarifies stream alignment, device configuration, and online graph execution reduces the engineering time spent on adapters and “glue” code.
Processing-graph orchestration that reuses identical logic across runs
Timeflux uses configuration-driven processing graphs that keep the same preprocessing and decoding logic in calibration and live runs. This makes closed-loop output more reproducible than pipelines that rebuild steps separately for each phase.
Interactive preprocessing and ICA workflows tightly coupled to EEG datasets
EEGLAB integrates ICA-based component exploration and rejection directly into dataset workflows and plotting tools. This combination fits labs that need repeatable MATLAB preprocessing and decomposition inspection before any decoding step.
End-to-end experiment execution tied to a device-connected acquisition stack
g.tec BCI Software couples device-connected acquisition, processing configuration, and decoder run control in one workflow. This reduces calibration-to-inference mismatches when teams standardize on g.tec hardware.
Visual workflow editor for real-time classification graphs
OpenViBE uses a visual workflow editor that maps processing nodes into runnable real-time graphs for calibration and online sessions. This design supports extensibility through a module system when custom processing or decoding components are required.
Operator-led channel monitoring and trigger-aware recording control
OpenBCI GUI provides channel and signal status views built for operator-led acquisition sessions with trigger-aware recording control. This fits labs that prioritize human-in-the-loop session management over automated online deployment.
Python-first device abstraction for streaming and offline neural data handling
BrainFlow offers a unified Python API for streaming and offline neural data handling across multiple supported biosignal board integrations. This supports engineering teams that want consistent device data access in Python without building separate capture modules per board.
Time-stamped stream discovery for joining heterogeneous pipelines
LSL focuses on stream discovery and timestamped data flow so independent processes can join an experiment session with consistent event and channel timing. This helps teams connect EEG tools without writing custom transport layers for each added component.
How to choose BCI Software based on orchestration model and integration targets
Choosing BCI software requires matching the orchestration model to the execution shape of the experiment. Calibration and live inference can be consistent in graph-based systems like Timeflux or node-based visual editors like OpenViBE, while MATLAB-centric pipelines in EEGLAB prioritize interactive preprocessing and scripted reruns.
The decision also turns on integration surface. Device-coupled experiment control in g.tec BCI Software differs from operator-run workflows in OpenBCI GUI, while streaming-first integration in BrainFlow and LSL shifts the engineering work toward Python code or stream definitions rather than built-in governance or admin controls.
Pick the orchestration style that must stay consistent across calibration and live runs
If the workflow must reuse the same preprocessing and decoding logic in both calibration and live inference, select Timeflux and build the pipeline as a processing graph configuration. If the team needs a visual node graph that runs real-time classification graphs for online sessions, select OpenViBE.
Match the software’s runtime to the lab’s dominant workflow language
If MATLAB is the standard environment for preprocessing and decomposition inspection, EEGLAB fits because its MATLAB EEG data structure and event metadata flow through preprocessing and analysis functions. If Python is the standard for streaming and inference pipelines, BrainFlow fits because it provides a unified Python API for streaming and offline neural data handling.
Decide whether device coupling or interoperability is the priority
If acquisition and decoder control must be run together with g.tec hardware, choose g.tec BCI Software because it integrates device-connected acquisition, processing configuration, and decoder run control. If heterogeneous tools must be connected with consistent timing across processes, choose LSL because it provides time-stamped stream discovery and a stream model for synchronized joining.
Choose the control model for live sessions based on who operates the system
If an operator needs real-time channel monitoring and trigger-aware recording control, choose OpenBCI GUI because it is built for operator-led acquisition sessions with live channel-level visualization. If engineering teams need explicit real-time execution steps and event-synchronized processing for feedback trials, choose BrainStorm for its configurable online decoding pipeline and real-time orchestration.
Use graph-centric tools only when the workflow can be expressed as configurable steps
If the team can model preprocessing and decoding as graph configurations without losing reproducibility, Timeflux supports repeatable pipeline execution for closed-loop outputs. If the team expects heavy changes during runtime setup that are better handled by scripted batch reruns and interactive inspection, EEGLAB’s interactive ICA workflow often fits better.
Who should use these BCI Software tools
BCI software selection depends on whether the work needs research-grade preprocessing, production-adjacent orchestration, or device-centric experiment control. Timeflux and OpenViBE align with teams that run online graphs and need consistent calibration-to-inference behavior.
EEGLAB fits research teams anchored in MATLAB preprocessing, while BrainFlow and LSL fit engineering teams that integrate multiple components through Python APIs or timestamped streaming.
Research teams building repeatable calibration and live inference workflows
Timeflux fits because it keeps identical preprocessing and decoding logic across calibration and live runs through processing-graph orchestration. OpenViBE fits when the workflow must be built as a visual real-time graph and extended with custom modules.
Labs standardizing on MATLAB for EEG preprocessing and decomposition
EEGLAB fits because its ICA-based component exploration and rejection are integrated into dataset workflows and plotting tools. EEGLAB’s MATLAB-centric structure also supports scriptable batch runs for repeatable preprocessing.
Teams standardizing acquisition and decoding on g.tec hardware
g.tec BCI Software fits because it couples device-connected acquisition with processing configuration and decoder run control in one workflow. This reduces mismatches when experiments depend on g.tec signal acquisition and device drivers.
Engineering teams integrating neural data streams into Python pipelines
BrainFlow fits because it offers a unified Python API for streaming and offline neural data handling across supported biosignal boards. This approach reduces custom code for common EEG acquisition paths.
Organizations connecting multiple tools with consistent timing across processes
LSL fits because its stream discovery and timestamped data flow keep signals and events aligned when independent processes join an experiment session. This is especially useful when adding new acquisition or processing nodes without rewriting transport code.
Common BCI Software pitfalls when teams move from experiments to live runs
BCI systems often fail during deployment due to inconsistencies between calibration-time processing and online inference execution. The tools listed here avoid that in different ways, so choosing software that matches the team’s orchestration discipline reduces rework.
Teams also waste time when they assume enterprise-style administration is built into neuroscience tools, even when the software is optimized for signal processing and experimental execution.
Treating operator-led acquisition tools as a replacement for an automated online inference pipeline
OpenBCI GUI is built for operator-led session control and live channel monitoring, and it has limited automation and API surface compared with code-first decoder pipelines. Use OpenBCI GUI for acquisition sessions, then route captured signals into a pipeline tool designed for online inference control.
Assuming graph tools will stay reproducible without disciplined configuration
Timeflux provides configuration-driven processing graphs for reproducible pipelines, but effective use depends on modeling the workflow as a graph configuration. OpenViBE similarly requires disciplined sampling rate and channel mapping setup, or the online graph can run with misaligned assumptions.
Planning for governance controls like RBAC and audit logs as if they are standard in BCI toolchains
BrainFlow does not focus on production-grade governance controls like RBAC and audit logs, and OpenBCI GUI also does not focus on those shared-lab governance features. If governance is a requirement, plan for external controls around the runtime rather than expecting them to be native.
Building real-time closed-loop systems in a MATLAB-only stack
EEGLAB’s MATLAB-centric workflow can slow production-grade integration into non-MATLAB stacks. MATLAB is excellent for preprocessing and decomposition workflows, but real-time closed-loop integration can require additional engineering work outside the MATLAB environment.
How We Selected and Ranked These Tools
We evaluated Timeflux, EEGLAB, g.tec BCI Software, OpenViBE, BrainFlow, OpenBCI GUI, BrainStorm, LSL, and PsychoPy using features, ease, and value. Features accounted for 40% of the score because orchestration clarity and end-to-end execution support directly affect calibration-to-inference consistency.
Ease and value each accounted for 30% because operator control, integration effort, and workflow friction determine how quickly teams can run online graphs or streaming pipelines. Timeflux ranked first because its processing-graph orchestration keeps identical preprocessing and decoding logic across calibration and live runs while also supporting real-time streaming to external consumers.
Frequently Asked Questions About Bci Software
How does Timeflux keep preprocessing and decoding consistent between calibration and live runs?
Which tool is better for scripted EEG preprocessing in Matlab: EEGLAB or OpenBCI GUI?
Which option fits teams that need a Python API across multiple EEG or biosignal boards: BrainFlow or OpenViBE?
How do OpenViBE and PsychoPy differ in how experiment timing and neural processing are wired together?
When does g.tec BCI Software become a better choice than LSL for experiment execution and device connectivity?
What breaks when LSL stream discovery and timestamp alignment are not handled correctly in a multi-process setup?
How do BrainStorm and Timeflux handle real-time buffering and closed-loop inference stages?
How should teams plan data migration when moving from Matlab workflows to a streaming pipeline?
What governance controls matter most for audit-friendly BCI processing runs in Timeflux compared with interactive-only analysis tools?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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