Arctop

Transforming Brain Activity to Digital Commands

Brain Brain Machine Interface Core AI

AI-Optimized Hardware Deep Learning Machine Learning

Business Overview

Jun 2016

Date Founded

Active

Operating Status

Employees

Arctop’s vision is to build fielddefining neural interface systems that enable entirely new functions and play a central role in humanity’s interaction with the world. NeuosTM is Arctop’s landmark software platform that creates highresolution maps of people’s feelings, reactions, and intent. It does so by taking electrical measurements of brain activity and applying specialized artificial intelligence. Unlike conventional brainmachine interfaces, Neuos uses a matrix of proprietary algorithms equipped for specialized tasks. These algorithms collect insights and form a ‘superintelligent’ system that can decode human thoughts and feelings with razor sharp resolution. From upperorder acts of cognition like thinking yes to more instinctual feelings like surprise, Neuos captures the brain’s fingerprint with extraordinary sensitivity. This unprecedented look into human experience enables empathic computing: operating systems that understand people from people’s own point of view. Designed for realtime processing in a way that’s never been possible before, Neuos enables applications to connect with meaningful brain patterns as they happen. Running on AR/VR headsets, headphones, and hats embedded with brain sensors, Neuos turns computing into an empathic experience.

Business Stage

Alpha

Sectors

Brain Brain Machine Interface Core AI Technologies

AI Technology Stack

AI Team Size

Horizontal AI

AI Application

AI Description

We use AI to discover and interpret meaningful brain patterns with a higher resolution than a human expert can, and much, much faster.

AI Types

AI-Optimized Hardware Deep Learning Machine Learning Robotics

ML Types

Reinforcement Learning, Semi-Supervised Learning, Supervised Learning, Unsupervised Learning

AI Algorithms

Bayesian Networks, Clustering, Decision Trees, Fuzzy C-Means, Genetic Algorithms, Hebbian Learning, Hidden Markov Model, Learning Classifier System, Naive Bayes Classifiers, Neural Networks, Q-learning, Rule-Based, Semantic Feature Space, Similarity and Metric Learning, Sparse Dictionary Learning, Support Vector Machines (SVM)