Improving zero-training brain-computer interfaces by mixing model estimators

Verhoeven, Thibault; Hübner, David; Tangermann, Michael; Müller, K-R; Dambre, Joni; Kindermans, P J

doi:10.1088/1741-2552/aa6639

Cited by 23 publications

(11 citation statements)

References 28 publications

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“…Following this hypothesis, a combined approach could lead to a faster rampup behaviour and a more robust classifier compared to the traditional EMalgorithm or the standalone LLP. This idea was picked up by Verhoeven and colleagues [50] who show that the performance of a combined approach can even transcend the performance of each individual classifier at almost any time. Second, LLP could be used in a transfer learning scenario where one starts with a general classifier obtained on several other subjects and utilizes LLP as an unsupervised adaptation method with guarantees.…”

Section: Possible Extensionsmentioning

confidence: 99%

Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees

Hübner¹,

Verhoeven²,

Schmid³

et al. 2017

PLoS ONE

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ObjectiveUsing traditional approaches, a brain-computer interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g., by subject-to-subject transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch and adapt over time. While such heuristics work well in practice, none of them can provide theoretical guarantees. Our objective is to modify an event-related potential (ERP) paradigm to work in unison with the machine learning decoder, and thus to achieve a reliable unsupervised calibrationless decoding with a guarantee to recover the true class means.MethodWe introduce learning from label proportions (LLP) to the BCI community as a new unsupervised, and easy-to-implement classification approach for ERP-based BCIs. The LLP estimates the mean target and non-target responses based on known proportions of these two classes in different groups of the data. We present a visual ERP speller to meet the requirements of LLP. For evaluation, we ran simulations on artificially created data sets and conducted an online BCI study with 13 subjects performing a copy-spelling task.ResultsTheoretical considerations show that LLP is guaranteed to minimize the loss function similar to a corresponding supervised classifier. LLP performed well in simulations and in the online application, where 84.5% of characters were spelled correctly on average without prior calibration.SignificanceThe continuously adapting LLP classifier is the first unsupervised decoder for ERP BCIs guaranteed to find the optimal decoder. This makes it an ideal solution to avoid tedious calibration sessions. Additionally, LLP works on complementary principles compared to existing unsupervised methods, opening the door for their further enhancement when combined with LLP.

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Section: Possible Extensionsmentioning

confidence: 99%

Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees

Hübner¹,

Verhoeven²,

Schmid³

et al. 2017

PLoS ONE

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“…To be a more practical application, the training mode should be minimized or removed. Numerous studies are underway in the field to construct this general classifier (Kindermans et al, 2014a , b ; Verhoeven et al, 2017 ; Eldeib et al, 2018 ; Lee et al, 2020 ). Usually, however, a general classifier requires a significant number of data samples, which can be achieved through transfer learning using data from one domain for another.…”

Section: Discussionmentioning

confidence: 99%

An Open Source-Based BCI Application for Virtual World Tour and Its Usability Evaluation

Woo

Lee

Kim

et al. 2021

Front. Hum. Neurosci.

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Brain–computer interfaces can provide a new communication channel and control functions to people with restricted movements. Recent studies have indicated the effectiveness of brain–computer interface (BCI) applications. Various types of applications have been introduced so far in this field, but the number of those available to the public is still insufficient. Thus, there is a need to expand the usability and accessibility of BCI applications. In this study, we introduce a BCI application for users to experience a virtual world tour. This software was built on three open-source environments and is publicly available through the GitHub repository. For a usability test, 10 healthy subjects participated in an electroencephalography (EEG) experiment and evaluated the system through a questionnaire. As a result, all the participants successfully played the BCI application with 96.6% accuracy with 20 blinks from two sessions and gave opinions on its usability (e.g., controllability, completeness, comfort, and enjoyment) through the questionnaire. We believe that this open-source BCI world tour system can be used in both research and entertainment settings and hopefully contribute to open science in the BCI field.

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“…BCI studies to date typically require extensive training over a period of several days or weeks, typically with a 20-to 30-min recalibration being necessary prior to each session [141]. However, recent studies suggest that initial training time can be reduced to less than a minute and that subsequent calibration time can be eliminated entirely [141][142][143]. Replicating such low initial training and subsequent calibration times in speech BCI would be highly desirable.…”

Section: Neural Decoding Performancementioning

confidence: 99%

The Potential for a Speech Brain–Computer Interface Using Chronic Electrocorticography

2019

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A brain-computer interface (BCI) is a technology that uses neural features to restore or augment the capabilities of its user. A BCI for speech would enable communication in real time via neural correlates of attempted or imagined speech. Such a technology would potentially restore communication and improve quality of life for locked-in patients and other patients with severe communication disorders. There have been many recent developments in neural decoders, neural feature extraction, and brain recording modalities facilitating BCI for the control of prosthetics and in automatic speech recognition (ASR). Indeed, ASR and related fields have developed significantly over the past years, and many lend many insights into the requirements, goals, and strategies for speech BCI. Neural speech decoding is a comparatively new field but has shown much promise with recent studies demonstrating semantic, auditory, and articulatory decoding using electrocorticography (ECoG) and other neural recording modalities. Because the neural representations for speech and language are widely distributed over cortical regions spanning the frontal, parietal, and temporal lobes, the mesoscopic scale of population activity captured by ECoG surface electrode arrays may have distinct advantages for speech BCI, in contrast to the advantages of microelectrode arrays for upper-limb BCI. Nevertheless, there remain many challenges for the translation of speech BCIs to clinical populations. This review discusses and outlines the current stateof-the-art for speech BCI and explores what a speech BCI using chronic ECoG might entail.

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Improving zero-training brain-computer interfaces by mixing model estimators

Abstract: Improving the accuracy and subsequent reliability of calibrationless BCIs makes these systems more appealing for frequent use.

Cited by 23 publications

References 28 publications

Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees

Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees

An Open Source-Based BCI Application for Virtual World Tour and Its Usability Evaluation

The Potential for a Speech Brain–Computer Interface Using Chronic Electrocorticography

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