Every company evolves as the problem it is trying to solve becomes clearer. For us, that moment arrived as we went deeper into building intelligence around human biosignals. What began with one idea grew into something broader and more foundational, and it needed an identity that reflected that shift. With the same DNA and a clearer direction, Nexstem is now Anthriq.

Today, we’re excited to unveil Project Kinesis — Study of the language of human movement. A research initiative to understand how muscle signals can help us build more natural, intuitive, and responsive ways to interact with technology. Earlier this month, we conducted a pilot study using surface electromyography (sEMG), marking the first step in our long-term mission to build the world’s most comprehensive dataset of human movements. Our goal is to move beyond simple motion tracking and detect human intent at its source. Kinesis: Gesture is a comprehensive library of hand gestures—including forearm flexion, wrist movements, and finger-level gestures.

Welcome back to our BCI crash course! We've journeyed from the fundamental concepts of BCIs to the intricacies of brain signals, mastered the art of signal processing, and learned how to train intelligent algorithms to decode those signals. Now, we're ready to tackle a fascinating and powerful BCI paradigm: motor imagery. Motor imagery BCIs allow users to control devices simply by imagining movements. This technology holds immense potential for applications like controlling neuroprosthetics for individuals with paralysis, assisting in stroke rehabilitation, and even creating immersive gaming experiences. In this post, we'll guide you through the step-by-step process of building a basic motor imagery BCI using Python, MNE-Python, and scikit-learn. Get ready to harness the power of your thoughts to interact with technology!

Welcome back to our BCI crash course! We've explored the foundations of BCIs, delved into the intricacies of brain signals, mastered the art of signal processing, and learned how to train intelligent algorithms to decode those signals. Now, we are ready to put all this knowledge into action by building a real-world BCI application: a P300 speller. P300 spellers are a groundbreaking technology that allows individuals with severe motor impairments to communicate by simply focusing their attention on letters on a screen. By harnessing the power of the P300 event-related potential, a brain response elicited by rare or surprising stimuli, these spellers open up a world of communication possibilities for those who might otherwise struggle to express themselves. In this blog, we will guide you through the step-by-step process of building a P300 speller using Python, MNE-Python, and scikit-learn. Get ready for a hands-on adventure in BCI development as we translate brainwaves into letters and words!

Welcome back to our BCI crash course! We've covered the fundamentals of BCIs, explored the brain's electrical activity, and equipped ourselves with the essential Python libraries for BCI development. Now, it's time to roll up our sleeves and dive into the practical world of signal processing. In this blog, we will transform raw EEG data into a format primed for BCI applications using MNE-Python. We will implement basic filters, create epochs around events, explore time-frequency representations, and learn techniques for removing artifacts. To make this a hands-on experience, we will work with the MNE sample dataset, a combined EEG and MEG recording from an auditory and visual experiment.

Welcome back to our BCI crash course! We've journeyed through the fundamental concepts of BCIs, delved into the intricacies of the brain, and explored the art of processing raw EEG signals. Now, it's time to empower ourselves with the tools to build our own BCI applications. Python, a versatile and powerful programming language, has become a popular choice for BCI development due to its rich ecosystem of scientific libraries, ease of use, and strong community support. In this post, we'll set up our Python environment and introduce the essential libraries that will serve as our BCI toolkit.

Welcome back to our BCI crash course! In the previous blog, we explored the basic concepts of BCIs and delved into the fundamentals of Neuroscience. Now, it's time to get our hands dirty with the practical aspects of EEG signal acquisition and processing. This blog will guide you through the journey of transforming raw EEG data into a format suitable for meaningful analysis and BCI applications. We will cover signal preprocessing techniques, and feature extraction methods, providing you with the essential tools for decoding the brain's electrical secrets.

Welcome back to our BCI crash course! In the previous blog, we explored the basics of BCIs and different approaches to decoding brain signals. Today, we will explore the fascinating world of neuroscience to understand the foundation upon which these incredible technologies are built. This blog will focus on the electrical activity of the brain, particularly relevant for EEG-based BCIs. By understanding how neurons communicate and generate the rhythmic oscillations that EEG measures, we can gain valuable insights into the development and application of BCI systems.