The vision
BCINexus was conceived while building BCILattice. The goal was to create a platform where researchers could share reproducible BCI workflows, collaborate openly, and build on each other’s work.
Our mission
Making BCI research
BCINexus helps researchers turn raw biosignal recordings into clean datasets, trained models, reports, and reusable studies, without weeks of setup friction.
9
Core modules
14+
File formats
16
Signal measures
11+
Research modules
Principles
Built to remove research friction
Every major decision is about faster setup, clearer methods, and easier reproducibility.
01
Analyze more signal types
Work with HD-DOT fNIRS, EEG, EMG, and related biosignals using visible processing steps such as HbO/HbR separation, Beer-Lambert conversion, TDDR, and short-separation regression.
02
Trust the processing steps
Use established signal-processing methods with every step visible and configurable, so results are easier to explain, audit, and repeat.
03
Build without script sprawl
Design complete BCI workflows on a canvas, compile them, and reuse the same method instead of rebuilding the analysis in scattered scripts.
04
Reuse work that already runs
Browse, import, and publish reusable studies so researchers can build on proven methods instead of starting from zero.
05
Report results that hold up
Reported numbers carry their confidence interval, sample size and chance level; a check runs before every export; and a finished report can be frozen with the results it was written against, so a year later it still says what was sent.
In our words
Why we built BCINexus
The questions we are asked most, answered plainly — what we set out to fix, what we got wrong along the way, and what we are still trying to change.
01
The problem
There is a gap between doing BCI research and preserving it in a form that others can reliably reproduce, evaluate, and build on.
A research workflow ends up spread across software, scripts, configurations, datasets, results, and documentation. Once the experiment is finished, reconnecting all of those pieces is difficult — often even for the person who ran it.
02
The approach
We wanted a practical environment where BCI researchers could construct, run, inspect, and reproduce their workflows visually, while still working with real scientific and machine-learning components.
BCILattice became the foundation for turning a research workflow from a collection of disconnected code and settings into something structured and reusable.
03
What we learned
That building the analysis itself was only part of the problem.
Researchers also need to know what was done, which version was used, how the workflow evolved, how results were produced, and how another researcher can reproduce or extend the work. That realization led to the larger BCINexus platform.
04
The platform
Because the goal is not to create another isolated tool. It is to connect the research lifecycle: build, validate, document, publish, review, reproduce, reuse.
BCINexus exists around that lifecycle, while BCILattice provides the environment for building and running the underlying workflows.
05
Where this goes
We want BCI research to depend less on disconnected files, undocumented steps, and one-off environments.
The longer-term goal is a research ecosystem where a published study is not just a paper, but a structured, inspectable, reproducible, and reusable research object.
Story
Our journey
From first import to reusable research studies.
Research & planning
We explored the challenges of reproducibility in BCI research and designed the platform around studies, pipelines, organizations, publications, and long-term collaboration.
Platform development
Development began on the core platform, including researcher profiles, organizations, authentication, study management, and the foundation for publishing reproducible research.
BCILattice integration
BCINexus was connected with BCILattice, allowing researchers to move studies and workflows between the desktop application and the web platform.
Building the ecosystem
Support expanded for organizations, reusable paradigms, datasets, publications, hardware partnerships, and versioned research assets, laying the foundation for an open BCI ecosystem.
Private preview
The platform entered private testing with its core functionality in place, focusing on stability, usability, and feedback before public release.
Public launch
BCINexus launches its first stable release, beginning its mission to make BCI research more reproducible, collaborative, and accessible.
Who is behind it
The person building this
BCINexus started as one engineer’s attempt to make his own BCI research reproducible. It is still built to that standard.
Rehan Naeem is an AI engineer and technical founder working across machine learning, deep learning, computer vision, natural language processing, automation systems, and biosignal processing. His work focuses on building research software and real-world AI applications, with a particular focus on BCI technology.
BCILattice began as his own tooling problem: BCI workflows that were hard to rerun, hard to explain, and hard to hand to anyone else. He built the desktop application, the fNIRS, EEG, and EMG processing stack, the visual pipeline and workflow engines, the model training and evaluation modules, the API backend, and the platform you are reading this on.
He still writes the code. Becoming CEO changed what else the role includes, not who builds it.
Technical Founder & Lead Developer
Built the technical foundation of BCILattice and BCINexus, developing the core systems and shaping the platform through hands-on AI engineering and BCI research.
Founder & CEO
Leading BCINexus from a technical product into a research technology company — overseeing product strategy, business development, partnerships, and the growth of the ecosystem.
Works in
Machine LearningDeep LearningComputer VisionNatural Language ProcessingBiosignal ProcessingBrain-Computer InterfacesAutomation SystemsResearch Software
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