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<p>Surface electromyography (sEMG) records muscle electrical signals and reflects neuromuscular physiological behaviors. Recently, high-density sEMG (HD-sEMG), which allows non-invasive identification of motor unit action potential trains (MUAPTs) and direct access to underlining neural drive derived from the spinal cord, becomes a research hotspot. However, datasets comprising HD-sEMG signals remain limited, especially for multi-day conditions, leading to the lack of long-term investigation of motor neuron activities. This paper presents a 320-channel HD-sEMG dataset, CEMHSEY (<b>C</b>onsecutiv<b>E M</b>ulti-day <b>H</b>igh-density <b>S</b>urface <b>E</b>lectromyograph<b>Y</b>), recorded from forearm muscles and across 11 consecutive days. The dataset consists of two sub-datasets as: an isometric contraction dataset containing 13 subjects performing 7 grasps under 3 different contraction force levels (named GRASP) and a hand gesture dataset with 6 subjects performing 11 hand gestures (named GESTURE). The dataset was validated with the usability of force regression, hand gesture recognition, and motor unit decoding. In addition, the multi-day data provide support for developing robust human-machine interfaces as well as analyzing neuromuscular modulation.</p>

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A Consecutive Multi-Day High-Density Surface Electromyography Dataset Comprising 7 Grasps and 11 Gestures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月14日

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  • Shutian Yang,
  • Chen Chen,
  • Dongxuan Li,
  • Xiangyang Zhu

摘要

Surface electromyography (sEMG) records muscle electrical signals and reflects neuromuscular physiological behaviors. Recently, high-density sEMG (HD-sEMG), which allows non-invasive identification of motor unit action potential trains (MUAPTs) and direct access to underlining neural drive derived from the spinal cord, becomes a research hotspot. However, datasets comprising HD-sEMG signals remain limited, especially for multi-day conditions, leading to the lack of long-term investigation of motor neuron activities. This paper presents a 320-channel HD-sEMG dataset, CEMHSEY (ConsecutivE Multi-day High-density Surface ElectromyographY), recorded from forearm muscles and across 11 consecutive days. The dataset consists of two sub-datasets as: an isometric contraction dataset containing 13 subjects performing 7 grasps under 3 different contraction force levels (named GRASP) and a hand gesture dataset with 6 subjects performing 11 hand gestures (named GESTURE). The dataset was validated with the usability of force regression, hand gesture recognition, and motor unit decoding. In addition, the multi-day data provide support for developing robust human-machine interfaces as well as analyzing neuromuscular modulation.