Deep Dives
Long-form research and writing from Arctop on brain-computer interfaces, consumer EEG hardware, and neural signal modeling.
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Reinforcement Learning from Brain Feedback (RLbF) for Large Language Model Improvement
A framework for post-training LLMs with continuous, involuntary EEG-derived cognitive-state signals — and a proof-of-concept platform (Isaac) that instantiates it.
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Why Consumer EEG Embedded Hardware Is the Next Big Platform: My Predictions as a CTO
A look at the consumer EEG hardware market and why it's poised to become a major platform for developers.
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Memory Decoding at Home: Mapping Inter-Subject Neural Synchrony to Episodic Memory Performance
Research on mapping neural synchrony to memory performance using EEG data collected at home. Our novel FCCA algorithm outperforms traditional methods in decoding episodic memory from consumer-grade brain devices.
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Part II Five Levels of Explanation (Part II): How Brain-Computer Interfaces Work
An in-depth exploration of brain-computer interface technology in five levels of difficulty. This is levels 4 and 5.
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Computers Will Soon Read Your Mind
Technology will help patients suffering from ALS or strokes.
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Part I Five Levels of Explanation (Part I): How Brain-Computer Interfaces Work
An in-depth exploration of brain-computer interface technology in five levels of difficulty. This is levels 1 through 3.
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Brain-based Authentication: Towards A Scalable, Commercial Grade Solution Using Noninvasive Signals
Research on brain-based authentication using noninvasive signals showing how brain activity can serve as a unique biometric identifier for secure, passwordless authentication systems.
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Modeling The Effect of Background Sounds on Human Focus Using Brain Decoding Technology
Research on how different sound environments affect human focus, showing personalized soundscapes significantly improve concentration compared to music playlists and silence.