Rethinking BCI Paradigm and Machine Learning Algorithm as a Symbiosis: Zero Calibration, Guaranteed Convergence and High Decoding Performance

Hübner, David; Kindermans, Pieter-Jan; Verhoeven, Thibault; Müller, Klaus‐Robert; Tangermann, Michael

doi:10.1007/978-3-030-05668-1_6

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2022

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“…In 2018, Qi et al [16] proposed a method to reduce calibration times by using the Riemannian distance measurement to select similar ERP samples. In 2019, Hübner et al [17] used learning from label proportions as a new classification approach and proved its value for the visual ERP BCI. In 2020, the investigation of Lee et al [18] showed that convolutional neural network (CNN) combined with large ERP samples could achieve calibration-free in a P300 speller BCI.…”

Section: Introductionmentioning

confidence: 99%

A Calibration-free Approach to Implementing P300-based Brain–computer Interface

et al. 2022

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Introduction: As a direct bridge between the brain and the outer world, brain-computer interface (BCI) is expected to replace, restore, enhance, supplement, or improve the natural output of brain. The prospect of BCI serving humans is very broad. However, the extensive applications of BCI have not been fully achieved. One of reasons is that the cost of calibration reduces the convenience and usability of BCI. Methods: In this study, we proposed a calibration-free approach, which is based on the ideas of reinforcement learning and transfer learning, for P300-based BCI. This approach, composed of two algorithms: P300 linear upper confidence bound (PLUCB) and transferred PLUCB (TPLUCB), is able to learn during the usage by exploration and exploitation and allows P300-based BCI

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Section: Introductionmentioning

confidence: 99%