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Brain Invaders Cortico 2018
- 1. Grenoble | images | parole | signal | automatique | laboratoire UMR 5216 Brain Invaders A Brain Computer Interface game Anton Andreev CNRS/Gipsa-lab CORTICO 19/04/2018
- 2. Team CORTICO 2018 Marco CONGEDO, Phd Anton ANDREEV CNRS CNRS Grégoire CATTAN Alexandre BARACHANT, PhD CNRS/IHMTEK Home-page: https://bitbucket.org/toncho11/openvibe-gipsa-extensions/overview Louis KORCZOWSKI
- 3. CORTICO 2018 Agenda 1- Brain Invaders 2- Architecture 3- Challenges and Algorithm 4- Research topics
- 4. 1. What is Brain Invaders? CORTICO 2018 You have a grid of 36 aliens. The red circled alien is your taget. You need to concentrate on the target in order to destroy it. Our system uses only the EEG signal that comes from the subject. It is also a good source of experimental data!
- 5. 1. Brain Invaders vs P300 Speller CORTICO 2018 P300 speller Brain Invaders ! Stimuli matrix The principe is basically the same. Both use P300 for detection of a letter or an alien.
- 6. 1. How it works? CORTICO 2018 A flashing target letter elicits a change in the cerebral response that appears as a change in the amplitude of the EEG signal over a time interval.
- 7. 2. Architecture CORTICO 2018 OpenVibe BI Launcher Brain Invaders Gipsa Extensions We use boxes provided by OpenVibe and our own developed by Gipsa-lab. We have OpenVibe scenarios for: Adaptive, Training, Online and Multiplayer Brain Invaders Acquisition server
- 8. 2. Components CORTICO 2018 Launcher: - Validates COM port, scenarios paths, sampling frequency … Gipsa-lab boxes for OpenVibe (OpenViBE-plugins-gipsa.dll): - Train MDM - Process MDM – (Online phase) - Adaptive MDM in Python - Riemann Potato - Signal merger with frequency resampling - Upsampler - Modified version of xDawn - Parallel Port to Stimulation - Stimulation Transformer All other boxes are already contributed to the OpenVibe project
- 9. 3. Challenges CORTICO 2018 The EEG signal is weak and pertrubated The neural activity of each subject is different How to perform real-time analysis of the data
- 10. 3. MDM Algorithm JTCVAM 2017 • Supervised learning • A version of k-means (a moving centroid algorithm) • Two centroids for every class: “contains P300" and “no P300" • It uses Riemannian geometry and it calculates the proximity to each centroid Also available for offline analysis: • Python machine learning library: https://github.com/pyRiemann/pyRiemann
- 11. CORTICO 2018 3. Objective: Plug and Play BCI Common parameters for the MDM algorithm A subject who has never played MDM parameters specific to the subject
- 12. 4. Research in Gipsa-lab CORTICO 2018 Better (faster and more accurate) detection of P300 An algorithm that works for everyone, not only for certain subjects Utilize the train phase of one subject for the online phase of another one Eliminate the training phase, so that BCI experience of the user is more enjoyable Multi-player BCI Combine BCI and Virtual Reality
Editor's Notes
- We have several applications that work together. BI Launcher is a C# application that configures and starts both OpenVibe and Brain Invaders
- Riemman Potato - unsupervised and automatic EEG artifact rejection Signal merger with frequency resampling - with this box you can acquire signal from two different sources (e.x.EEG/eye - tracker) and different frequency and merge them them with the frequency of one of the sources (first channel is used as reference for the frequency). Upsampler - increases the signal frequency by an integer factor by resampling the signal. This is exactly the opposite of the Decimation box in OpenVibe. We use it to synchronize the signal from two devices to a common frequency. For example if the first is 600Hz and the second 1000Hz then we upsample both to 3000Hz Parallel Port to Stimulation - converts a list of numeric values (usually parallel port values) to OpenVibe stimulations Stimulation Transformer - converts a list of stimulations to another list of stimulations
- First we start with a pretrained model based on 20 subjects. So this allows a new subject to start playing, but perhaps not with the best performance. In the next 40 seconds we use the data from the new subject to perform a new training. So after 1 minute the model is adapted to the new subject.
- We use Brain Invaders to do research. Using a BCI game we are able to acquire many EEG recordings of P300 ERPs. It also allows us to test different algorithms.
- Chaque sujet a un cerveau (et des ondes cérébrales) différent(es). Il faut s’adapter! Une phase de calibration peut être nécessaire avant d’être prêt à jouer.