Multi-layer ear-scalp distillation framework for ear-EEG classification enhancement.

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  • Additional Information
    • Source:
      Publisher: Institute of Physics Pub Country of Publication: England NLM ID: 101217933 Publication Model: Electronic Cited Medium: Internet ISSN: 1741-2552 (Electronic) Linking ISSN: 17412552 NLM ISO Abbreviation: J Neural Eng Subsets: MEDLINE
    • Publication Information:
      Original Publication: Bristol, U.K. : Institute of Physics Pub., 2004-
    • Subject Terms:
      Electroencephalography*/methods ; Brain-Computer Interfaces* ; Scalp*/physiology ; Evoked Potentials, Visual*/physiology; Humans ; Male ; Ear/physiology ; Adult ; Female ; Young Adult ; Photic Stimulation/methods
    • Abstract:
      Background: Ear-electroencephalography (ear-EEG) holds significant promise as a practical tool in brain-computer interfaces (BCIs) due to its enhanced unobtrusiveness, comfort, and mobility compared to traditional steady-state visual evoked potential (SSVEP)-based BCI systems. However, achieving accurate SSVEP classification with ear-EEG remains a major challenge due to the significant attenuation and distortion of the signal amplitude.
      Objective: Our aim is to enhance the classification performance of SSVEP using ear-EEG and to increase its practical application value.
      Approach: To address this challenge, we focus on enhancing ear-EEG feature representations by training the model to learn features similar to those of scalp-EEG. We introduce a novel framework, termed multi-layer ear-scalp distillation (MESD), designed to optimize SSVEP target classification in ear-EEG data. This framework combines signals from the scalp to obtain multi-layer distilled knowledge through the cooperation of mid-layer feature distillation and output layer response distillation.Mainresults.We improve the classification of the initial 1 s data and achieved a maximum classification accuracy of 81.1%. We evaluate the proposed MESD framework through single-session, cross-session, and cross-subject transfer decoding, comparing it with baseline methods. The results demonstrate that the proposed framework achieves the best classification results in all experiments.
      Significance: Our study enhances the classification accuracy of SSVEP based on ear-EEG within a short time window. These results offer insights for the application of ear-EEG brain-computer interfaces in tasks such as auxiliary control and rehabilitation training in future endeavors.
      (Creative Commons Attribution license.)
    • Contributed Indexing:
      Keywords: brain computer interface; ear-electroencephalography; knowledge distillation; steady-state visual evoked potential
    • Publication Date:
      Date Created: 20241126 Date Completed: 20241206 Latest Revision: 20241206
    • Publication Date:
      20241209
    • Accession Number:
      10.1088/1741-2552/ad9778
    • Accession Number:
      39591752