When technology meets the mind, the boundaries between science and what once seemed like science fiction begin to blur. But to realize these possibilities, data must be managed efficiently and accurately, ensuring that insights are not only validated but accelerated.

REMspace is a neurotech startup at the cross-section between neuroscience and artificial intelligence that has claimed to have achieved pod-like communication between two individuals during lucid dreaming.

This achievement is spreading across the tech media since it may just turn around the way we understand both AI and the human brain. The results go beyond the novelty of communicating from dreams to hinting at significant applications for neurotechnology in daily life, with enhanced insights as a result of machine learning in Neuroscience.

What is Neurotechnology?

People also ask whether neuroscience and neurotechnology are the same. The answer is no. Neuroscience and neurotechnology are definitely linked, but they aren’t exactly the same. 

Neuroscience is about studying how the brain and nervous system work, while neurotechnology focuses on building tools that can interact with the brain—like devices that let people communicate with computers just by thinking.

What can neurotechnology help with? REMspace is part of a new wave of neuro biotechnology companies pioneering human-machine interactions, including: 

• Neuralink, an American company founded by Elon Musk, has developed implantable brain-computer interfaces (BCIs) that allow direct communication between the brain and computers as of 2024.
• Precision Neuroscience, Neuralink’s rival which just got 93M in funding in November 2024, is also aiming to help those with neurological issues using BCIs.
• Neurable which is working on brain-computer interfaces for emotion interpretation 
• Kernel which is developing non-invasive neural measurement technology, and 
• Blackrock Neurotech which is pioneering brain-computer interfaces with the goal of real-time neural interaction. 

But in addition to all the excitement on the grounds of innovation from REMspace and other players, there must come tempered, healthy scientific skepticism and consideration of how an AI-powered data stack could optimize processes for even quicker progress in innovation.

The Optimal Data Stack for Neurotech

The central concept of a data stack applies to areas such as neuroscience, where such complex data—any form of neural signals—operate on highly sophisticated multilayered solutions. Most simply put, a data stack can be defined as the technological infrastructure required for collecting, processing, and analyzing data before presenting it in an insightful format. REMspace's experiments using human test subjects in lucid dreaming would function with this sort of stack, embedding AI algorithms into real-time signal processing, with feedback loops inside the dream environments.

Drawing similarities from Shakudo's success with CentralReach, a company focused on AI solutions in autism care, it becomes clear how a robust data stack can support innovation across diverse fields. As a case study, Shakudo's platform allowed CentralReach to scale AI solutions rapidly, and the same principle applies to the innovative work in the neuroscience tech space. 

Open-Source Data Stack for REMspace's Lucid Dream Research

The tools from CentralReach's data stack with Shakudo line up with the specific needs of neurotech players like Neuralink and REMspace because it can offer complex, real-time processing, which is needed for brain-computer interactions. Here is how each part might contribute:

Dify & Ollama (Software that works with Large Language Models)

Software like Dify & Ollama that work with LLMs (large language models) can help decode complex neural data into clear outputs. 

As REMspace’s dream language ‘Remmyo’ evolves, LLM software could help make sense of diverse language signals, turning them into structured commands essential for communicating in real time within dreams. 
Why does this matter? Brain signals can be ambiguous, so using NLP algorithms makes it easier to go from messy neural inputs to direct, actionable outputs.

Appsmith (Low-Code Development)

Neuroscience technology companies needs to quickly test new user interfaces for dream-based interaction. Appsmith’s platform can help them rapidly prototype, showing neural commands or EMG feedback to improve the user experience.
With Appsmith, teams could easily make dashboards or control panels that researchers can tweak without deep coding, keeping up with the pace of development.

n8n (Workflow Automation)

n8n would help sync data input, processing, and feedback during experiments, automating responses like triggering visual feedback when EMG signals pass a threshold.
Since companies in this new wave run experiments that need quick adjustments and feedback, n8n would make data flow easier, reducing delays and keeping things running smoothly.

Qdrant (Vector Database)

Qdrant would store and organize neural signals, helping the system detect patterns, track commands from brain signals like within dreams, and analyze communication attempts over multiple sessions.
Neural data (especially EMG sensor data) is high-dimensional, and Qdrant can handle this, enabling rapid retrieval and comparison of historical data against live inputs.

Supabase (Data Management)

Supabase would handle structured data like participant details, experiment results, and logs, providing a secure backend for sensitive research data.

As these companies expands their experiments, Supabase can keep participant data organized while following compliance and ethical standards, crucial in biotechnology.

Windmill (Pipeline Orchestration)

Windmill could handle the complex steps involved in real-time data work—preparing signals, running AI models, and giving participants instant feedback.

To keep real-time communication in dreams working, a company like REMspace needs synced data streams, which Windmill’s workflow orchestration makes possible, keeping everything transparent and efficient.

The Need for Verification and Reproducibility

The concept of people talking to each other in lucid dreams is certainly intriguing and there are promising results in reputable research journals. However, we need consistent, large-scale, replicable results to prove that the data is real and reliable. This is a hopeful early step — but to give this science any real gravitas, we need larger and better controlled trials. 

Ethical Concerns

As with any tech that interfaces directly with the human brain, it raises very serious ethical issues, especially with respect to privacy and informed consent. Risks and benefits should be as transparent as possible to participants in human trials.

The Approach for CentralReach at Shakudo

The CentralReach + Shakudo partnership is a prime example of the power of an integrated data ecosystem. Using AI-driven processes, they helped streamline complex workflows, reduce deployment times, and facilitate better data insights—and this is exactly how we want to approach REMspace by investing in rapid prototyping, adaptable AI applications, and live feedback.

Common Characteristics

• Adaptive AI: Both CentralReach + REMspace utilize adaptive AI. At CentralReach, the AI learns from clinical data inputs and user needs, while REMspace’s AI interprets neural signals.
  

• Immediate Data Processing: There should be quick data processing in both the systems so responses can be given without any delay.
  

• Why It Matters to Them: Iterative Development: CentralReach is built in pieces, allowing an ability to pivot quickly, and REMspace also plans to do the same, iteratively testing and retesting their tools based on what they develop—hopefully, with no big delays.

Looking Ahead: AI in Neuroscience Technology

The complexity of sourcing and managing neuroscience data—clinical, genetic, neuroimaging, and real-world data—presents a significant challenge for neurotech companies like REMspace, which require a flexible infrastructure and a focused approach. 

One of the biggest neurotechnological players, Neuralink, recently spoke about where real-time integration plays into increasing its powers radically. They have reached a milestone with one patient who was able to control a computer mouse by thinking about it because of their brain-chip implant. Advanced data systems are opening up new possibilities in the interaction between brains and computers.

Just as Neuralink’s work depends on real-time processing of brain signals, REMspace’s technology relies on a data stack capable of handling multi-modal, high-frequency data inputs. REMspace aims to create an integrated system for managing complex neural data—similar to established models like BRAIN Commons—so researchers can more easily exchange data, accelerating insights and enabling innovation at the scale required to make an impact.

REMspace is also carrying along, into the bargain, a few data-driven strategies aimed at exactly the question of how to make communication possible inside lucid dreams.
What does that all mean to executives? It means a capable data basis is the sole giving full force to current projects while scaling future innovations. With neurotech continuing to evolve, being able to process data in a scalable and real-time manner and seamlessly integrate across multiple sources, companies will start differentiating. 

Companies like REMspace and Neuralink are driving this wave in neurotechnology improvements, setting the scene for game-changing human-machine interaction.

Interested in elevating your data stack with a one-stop solution for open-source data software? Reach out to one of our AI and data experts to tackle your company’s unique data challenges.