H2020Индивидуална стипендия2015–2017

ZERO-TRAIN-BCI · Combining constrained based learning and transfer learning to facilitate Zero-training Brain-Computer Interfacing

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2015-04-01 → 2017-03-31
Финансиране от ЕС: 159 461 €
Участници: 1
Схема: MSCA-IF-EF-ST

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Накратко на български

Интерфейсите „мозък-компютър“ се разработват така, че да позволят управление на устройства чрез мозъчни сигнали без предварителна настройка за всеки потребител. Това помага на парализирани пациенти да възстановят комуникацията или моторния контрол, като се елиминира времето за калибриране на системата.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Combining constrained based learning and transfer learning to facilitate Zero-training Brain-Computer Interfacing

The ZERO-TRAIN-BCI project is devoted to developing a new generation of Brain-Computer Interfaces. Brain-Computer Interfaces (BCI) are designed to allow the user to control a computer directly through his brain signals. Important use-cases for BCI are the restoration of communication for paralysed patients, restoration of motor control and gaming. The first BCI prototypes required the user to generate brain signals that the computer could recognise easily. Here, the computer was pre-programmed and the user had to learn how to control his brain signals to obtain control over the device. The introduction of machine learning shifted part of the learning process to the computer. By recording brain signals and the user’s intention, the computer can learn how to produce the desired output. Recording this labelled data requires a calibration session, which typically takes around 15-30 minutes. During the recording of this calibration data, the user cannot be productive with the BCI. Since patients often have a limited attention span, this time must be limited as much as possible. Furthermore, the fact that the underlying statistics of the brain signals change over time leads to the need of frequent re-calibration. The BCI community has invested much effort on reducing the need for calibration data. In this project, we build upon these and we develop a new generation of BCI decoders that do not require explicit labelled data for all subjects. Instead we use unsupervised learning, weakly supervised learning and transfer learning to build a true zero training brain-computer interface. Such an interface can be used by a novel user without prior calibration.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Brain-Computer Interfaces (BCI) enable the user to control a computer or external device directly through his or her brain signals. This interface can be used for restoring communication for completely paralysed patients, to restore motor function through prostheses but also for non-medical applications such as gaming.The initial BCI prototypes relied on voluntary modulation of the brain signals to control the computer. Nowadays, it is the computer that is taught via machine learning algorithms how to interpret the brain signals and this reduced the training times to 15-30 minutes for a calibration session. During such a calibration session, the user is instructed to perform specific mental tasks, such that the recorded brain signals can be labelled with the user’s intention. This labelled data-set is then used to train the machine learning algorithm. Unfortunately, due to non-stationarity in the observed brain signals, re-calibration is often required to ensure the accuracy of the interface. Obviously, frequent (re-)calibration is not desired. Especially for patients with a limited attention span, it must be reduced to a minimum. The BCI community has invested much effort in reducing the need for calibration data. However, despite this effort, true zero-training BCIs that do not require calibration are rather rare. For the Event Related Potential (ERP) based BCI, we were able to develop such a true zero-training BCI based on the concepts of constraint based learning and transfer learning. That decoder was designed specifically for the ERP based BCI and cannot be ported directly to other paradigms. Hence, the goal in this project is to expand on this idea and to develop a true-zero training Motor Imagery (MI) based BCI by investigating novel machine learning methods based on constraint based learning and transfer learning.

Оригинален текст от CORDIS (на английски).

Участници

TECHNISCHE UNIVERSITAT BERLIN · BerlinКоординаторГермания