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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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Analytic beamformer transformation for transfer learning in motion-onset visual evoked potential decoding.

Arno Libert1, Arne Van Den Kerchove1, Benjamin Wittevrongel1

  • 1Computational Neuroscience Research Group, Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium.

Journal of Neural Engineering
|April 2, 2022
PubMed
Summary
This summary is machine-generated.

The analytic beamformer transformation (ABT) improves population-based training for electroencephalography-based event-related potentials (ERPs) used in brain-computer interfaces (BCIs). ABT enables accurate individual classification with fewer data epochs.

Keywords:
BCIanalytic beamformer transformationpopulation trained decodingtransfer learning

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Individualized decoders for electroencephalography-based event-related potentials (ERPs) maximize performance.
  • Population-based training for individual ERP usage presents significant challenges.

Purpose of the Study:

  • Propose the analytic beamformer transformation (ABT) for extracting phase and magnitude information from spatiotemporal ERPs.
  • Evaluate ABT's effectiveness in improving population-trained classifiers for individual use.

Main Methods:

  • Tested ABT on 52 motion-onset visual evoked potential (mVEP) datasets from 26 healthy subjects.
  • Compared classification accuracy of SVM, stBF, and SWLDA using individual vs. population training.
  • Utilized phase and combined phase/magnitude information extracted by ABT.

Main Results:

  • ABT significantly improved the accuracy of population-trained classifiers for individual users (p<0.001).
  • ABT requires approximately 450 epochs (2 minutes of stimulation) for effective functioning.
  • ABT facilitates population-based training of mVEP classifiers with limited epochs.

Conclusions:

  • ABT enables effective population-based training for mVEP classifiers using minimal data.
  • ABT's structural invariance across subjects supports transfer learning for plug-and-play BCI applications.
  • The proposed method is expected to benefit other ERPs and synchronous stimulation paradigms for brain-computer interfaces.