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Symbiote BCI

Romania

  • Brain-computer interfaces
  • EEG and neural monitoring
  • Wearables

Symbiote BCI develops Medusa, neuroadaptive software that adjusts generative AI responses to a user's cognitive load. It works with Claude Code and can be paired with an optional 8-channel EEG headset that estimates cognitive load from brain activity.

reccy insights

Recent highlights

The clearest available product detail is Medusa, software designed to adapt generative AI responses to a user's estimated cognitive load. It is compatible with Claude Code and can operate as a software-only product, estimating cognitive load from how a person works with AI agents. An optional 8-channel EEG headset offers another way to estimate cognitive load using brain activity. The stated use case is helping people work with AI agents during deep-work sessions. As a result, the product capabilities can be described, but there is not enough information to identify a specific recent milestone or quantify its reception. The available details indicate a focus on combining AI interaction with cognitive-state estimation rather than on a broad portfolio of disclosed products.

Written by reccy's AI from company sites, press and funding records · updated Oct 2026

What makes Symbiote BCI distinct

  1. 01Adapts AI responses to the user's estimated cognitive load.
  2. 02Offers a software-only option that estimates cognitive load from how users work with AI agents.
  3. 03Can be paired with an 8-channel EEG headset for brain-activity-based cognitive load estimation.
  4. 04Designed to support work with AI agents during deep-work sessions.

Technology & products

  • Medusa

    Neuroadaptive generative AI software that adjusts responses to a user's estimated cognitive load. It works with Claude Code and can use software-only estimation or an optional 8-channel EEG headset.

  • Medusa neuroadaptive generative AI software
  • Optional 8-channel EEG headset for cognitive load estimation
  • Claude Code compatibility
  • Software-based cognitive-load estimation from AI-agent interactions