# Electromyographic typing gesture classification dataset for neurotechnological human-machine interfaces

## TL;DR

This paper introduces a dataset of muscle signals recorded during typing to help develop better human-machine interfaces using neurological signals.

## Contribution

The paper presents a new sEMG dataset for typing gesture classification to advance human-machine interface research.

## Key findings

- Intra-session classification accuracy is high with simple models.
- Inter-session and inter-subject accuracy is significantly lower, highlighting generalization challenges.
- The dataset includes 16-channel sEMG and key logs from 19 participants across two sessions.

## Abstract

Neurotechnological interfaces have the potential to create new forms of human-machine interactions, by allowing devices to interact directly with neurological signals instead of via intermediates such as keystrokes. Surface electromyography (sEMG) has been used extensively in myoelectric control systems, which use bioelectric activity recorded from muscles during contractions to classify actions. This technology has been used primarily for rehabilitation applications. In order to support the development of myoelectric interfaces for a broader range of human-machine interactions, we present an sEMG dataset obtained during key presses in a typing task. This fine-grained classification dataset consists of 16-channel bilateral sEMG recordings and key logs, collected from 19 individuals in two sessions on different days. We report baseline results on intra-session, inter-session and inter-subject evaluations. Our baseline results show that within-session accuracy is relatively high, even with simple learning models. However, the results on between-session and between-participant are much lower, showing that generalizing between sessions and individuals is an open challenge.