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An ultrasoft, breathable, and multichannel ear-computer interface patch.

Ying Sun1, Weijia Liu2, Hao Zhang3

  • 1School of Interdisciplinary Science, Beijing Institute of Technology, Beijing 100081, China; State Key Laboratory of CNS/ATM, Beijing Institute of Technology, Beijing 100081, China.

Science Bulletin
|January 21, 2026
PubMed
Summary
This summary is machine-generated.

A new ear-computer interface (ECI) patch offers a comfortable, non-invasive way to control devices with brain activity. This wearable technology achieves high accuracy in fatigue monitoring and brain-computer interface tasks.

Keywords:
Brain-computer interfaceEar-computer interfaceElectroencephalographyTi(3)C(2)T(x) MXeneWearable electronics

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Wearable Technology

Background:

  • Brain-computer interfaces (BCIs) using electroencephalography (EEG) caps/bands or implants offer advanced human-computer control.
  • Existing non-invasive BCIs often require inconvenient gel application and tight scalp fixation.
  • Implantable BCIs, while effective, are invasive and unsuitable for healthy individuals.

Purpose of the Study:

  • To develop a novel, non-invasive wearable ear-computer interface (ECI) patch for brain activity-based control.
  • To evaluate the ECI patch's performance in monitoring user fatigue and executing BCI tasks.
  • To demonstrate the feasibility of the ECI patch for real-world applications like controlling unmanned vehicles.

Main Methods:

  • An 8-channel ECI patch was fabricated using MXene electrodes via a direct inject print method on a flexible medical film.
  • The ECI patch's ability to monitor fatigue was assessed through fatigue induction experiments.
  • Performance in a 4-target steady state visual evoked potential (SSVEP) BCI task was evaluated through offline and online experiments, including a complex route control task.

Main Results:

  • The ECI patch demonstrated superior adherence and comfort compared to traditional EEG caps.
  • Fatigue monitoring achieved an average classification accuracy of 90.5%.
  • Online SSVEP BCI tasks showed an average accuracy of 93.5%, comparable to commercial EEG caps. The ECI patch successfully controlled an unmanned vehicle in complex tasks.

Conclusions:

  • The developed ECI patch provides a comfortable and effective non-invasive alternative for brain-computer interfacing.
  • The ECI patch shows significant potential for applications requiring reliable brain activity monitoring and control.
  • This technology redefines the convenience and applicability of BCIs for both therapeutic and general use.