A noninvasive neuromotor interface for human-computer interaction (HCI) has been developed using surface electromyography (sEMG) to enable intuitive and seamless computer input. The system, called sEMG-RD, is a dry-electrode, multi-channel recording platform that can be easily worn and removed, and is capable of capturing sEMG signals from the wrist. This allows for the transformation of neuromotor commands into computer input without the need for an intermediary device. The system was trained on data from thousands of participants, enabling the development of generic sEMG neural network decoding models that work across many people without the need for per-person calibration. Test users not included in the training set demonstrated closed-loop performance of 0.5 target acquisitions per second in a continuous navigation task, 0.9 gesture detections per second in a discrete gesture task, and 17.0 adjusted words per minute in handwriting. The system's performance can be further improved by personalizing sEMG decoding models to the individual, which could lead to a future where humans and machines co-adapt to provide seamless translation of human intent. This is the first high-bandwidth neuromotor interface that directly leverages biosignals with performant out-of-the-box generalization across people. The system was tested on a variety of tasks, including continuous navigation, discrete gesture recognition, and handwriting transcription, and demonstrated reliable performance across a large and diverse population. The results show that the system can be used for a wide range of computer interactions, including 1D continuous navigation, gesture detection, and handwriting transcription. The system's performance was evaluated in both offline and online settings, with the online evaluation showing that the system can be used in closed-loop scenarios to control a 1D cursor, navigate a discrete grid, and write prompted text. The system's performance was also evaluated in terms of its ability to generalize across participants, with results showing that the system can be used by a wide range of users without the need for individual calibration. The system's performance was also evaluated in terms of its ability to handle different types of gestures and handwriting, with results showing that the system can be used for a wide range of tasks. The system's performance was also evaluated in terms of its ability to handle different types of participants, with results showing that the system can be used by a wide range of users without the need for individual calibration. The system's performance was also evaluated in terms of its ability to handle different types of tasks, with results showing that the system can be used for a wide range of tasks. The system's performance was also evaluated in terms of its ability to handle different types of data, with results showing that the system can be used for a wide range of tasks. The system's performance was also evaluated in terms of its ability to handle different types of data, with results showing that the system can be used for a wide range of tasks.A noninvasive neuromotor interface for human-computer interaction (HCI) has been developed using surface electromyography (sEMG) to enable intuitive and seamless computer input. The system, called sEMG-RD, is a dry-electrode, multi-channel recording platform that can be easily worn and removed, and is capable of capturing sEMG signals from the wrist. This allows for the transformation of neuromotor commands into computer input without the need for an intermediary device. The system was trained on data from thousands of participants, enabling the development of generic sEMG neural network decoding models that work across many people without the need for per-person calibration. Test users not included in the training set demonstrated closed-loop performance of 0.5 target acquisitions per second in a continuous navigation task, 0.9 gesture detections per second in a discrete gesture task, and 17.0 adjusted words per minute in handwriting. The system's performance can be further improved by personalizing sEMG decoding models to the individual, which could lead to a future where humans and machines co-adapt to provide seamless translation of human intent. This is the first high-bandwidth neuromotor interface that directly leverages biosignals with performant out-of-the-box generalization across people. The system was tested on a variety of tasks, including continuous navigation, discrete gesture recognition, and handwriting transcription, and demonstrated reliable performance across a large and diverse population. The results show that the system can be used for a wide range of computer interactions, including 1D continuous navigation, gesture detection, and handwriting transcription. The system's performance was evaluated in both offline and online settings, with the online evaluation showing that the system can be used in closed-loop scenarios to control a 1D cursor, navigate a discrete grid, and write prompted text. The system's performance was also evaluated in terms of its ability to generalize across participants, with results showing that the system can be used by a wide range of users without the need for individual calibration. The system's performance was also evaluated in terms of its ability to handle different types of gestures and handwriting, with results showing that the system can be used for a wide range of tasks. The system's performance was also evaluated in terms of its ability to handle different types of participants, with results showing that the system can be used by a wide range of users without the need for individual calibration. The system's performance was also evaluated in terms of its ability to handle different types of tasks, with results showing that the system can be used for a wide range of tasks. The system's performance was also evaluated in terms of its ability to handle different types of data, with results showing that the system can be used for a wide range of tasks. The system's performance was also evaluated in terms of its ability to handle different types of data, with results showing that the system can be used for a wide range of tasks.