International audienceAdaptive Brain-Computer interfaces (BCIs) have shown to improve performance, however a general and flexible framework to implement adaptive features is still lacking. We appeal to a generic Bayesian approach, called Active Inference (AI), to infer user's intentions or states and act in a way that optimizes performance. In realistic P300-speller simulations, AI outperforms traditional algorithms with an increase in bit rate between 18% and 59%, while offering a possibility of unifying various adaptive implementations within one generic framework

Frey, Jérémy

Joffily, Mateus

Lotte, Fabien

Maby, Emmanuel

Mattout, Jeremie

Mladenović, Jelena

arXiv

Mladenović , Jelena

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Active Inference for Adaptive BCI: application to the P300 Speller

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HAL Id: hal-01796754https://hal.inria.fr/hal-01796754Submitted on 21 May 2018HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Active Inference for Adaptive BCI: application to theP300 SpellerJelena Mladenović, Jérémy Frey, Emmanuel Maby, Mateus Joffily, FabienLotte, Jeremie MattoutTo cite this version:Jelena Mladenović, Jérémy Frey, Emmanuel Maby, Mateus Joffily, Fabien Lotte, et al.. Active In-ference for Adaptive BCI: application to the P300 Speller. International BCI meeting, May 2018,Asilomar, United States. 2018. ￿hal-01796754￿Active Inference for Adaptive BCI: application to the P300SpellerJelena Mladenović1,2, Jérémy Frey3, Emmanuel Maby2, Mateus Joffily4, Fabien Lotte1,Jérémie Mattout21Inria, Bordeaux, France; 2CRNL Inserm, CNRS, Lyon, France; 3Ullo, La Rochelle, France;4GATE, CNRS, Lyon, FranceCorrespondence: Jelena Mladenović, Inria, Bordeaux, France. E-mail: jelena.mladenovic@inria.frAbstract.Adaptive Brain-Computer interfaces (BCIs) have shown to improve performance, however a general and flexibleframework to implement adaptive features is still lacking. We appeal to a generic Bayesian approach, called ActiveInference (AI), to infer user’s intentions or states and act in a way that optimizes performance. In realistic P300-spellersimulations, AI outperforms traditional algorithms with an increase in bit rate between 18% and 59%, while offeringa possibility of unifying various adaptive implementations within one generic framework.Keywords: Active Inference, BCI, P300 Speller, EEG1 IntroductionAdaptive BCIs have shown to improve performance [Mladenović et al., 2017], however thorough adaptation is farfrom being reached, and a general and flexible framework to implement adaptive features is still lacking. We appeal toa generic Bayesian approach, called Active Inference (AI), which tightly couples perception and action [Friston et al.,2006]. Endowing the machine with AI, enables: (1) to infer user’s intentions or states by accumulating observations(e.g. electrophysiological data) in a flexible manner, as well as (2) to act adaptively in a way that optimizes perfor-mance. We illustrate AI applied to BCI using realistic P300-speller simulations. We demonstrate it can implementnew features such as optimizing the sequence of flashed letters and yield significant bit rate increases.2 Material, Methods and ResultsActive Inference rests on an explicit probabilistic model of user and task. Key variables include observed data, userhidden states, and machine’s action, as follows.The observed data, here electroencephalagraphy (EEG) responses to target, non-target (P300 or not) and feedbackstimuli (Error Potentials – ErrPs or not), allow the machine to infer user’s hidden states, here the intention to spella letter or pause as well as the recognition of a target/non-target or a correct/incorrect feedback. Depending on thehidden states inferred, the computer has possible actions, here to flash in order to accumulate confidence about thetarget letter, to stop flashing and to display the chosen letter, or to switch off the screen if it infers an idle state of theuser, i.e. no P300 response has been observed for some time.Each hidden state is mapped onto observations through the data likelihood matrix which can be learned fromcalibration data. Given the machine’s actions, the transitions between hidden states are modeled by a probability(Markov) martix. We also predefine the preference over all possible outcomes. Typically, the preferred outcome is tobe in the state of observing a correctly spelled letter. Finally, a parameter γ sets the exploration-exploitation tradeofffor action selection.We compared AI to two classical approaches:1. P300-spelling with a fixed flash number (12) of repetitions and pseudo-random flashing;2. P300-spelling with pseudo-random flashing but optimal stopping [Mattout et al., 2015].To do so, we used data from 18 subjects from a previous P300-speller experiment [Perrin et al., 2011]. For eachalgorithm and subject, we simulated the spelling of 12000 letters.Furthermore, to demonstrate AI’s flexibility, we implemented a "LookAway" case, in which the machine wouldinfer the user to be in idle state and would switch the screen off. We also simulated an ErrP classification enabling the7th International BCI Meeting, May 21-15, 2018 1automated detection of a wrongly spelled letter. In case of such detection, AI picks the next most probable letter tospell or choose to continue flashing to strengthen its confidence.AI showed significantly higher bit rate (54.12bit/min) than the second best strategy (optimal stopping, 45.70bit/min),see Figure 1. Its performance increased even further when a perfect ErrP classifier is used (73bit/min). Finally, whenidle user states are simulated, it accurately switches off the speller ≈89% of the time, after ≈24 flashes.Figure 1: Comparison in bit rate (bit/min) between various flashing methods in a P300 BCI application. Data collected from thesimulated spelling of 12000 letters with 18 subjects who were recorded in a previous experiment [Perrin et al., 2011]. All methodssignificantly differ from one another (ANOVA, Tukey post-hoc, p < 0.01).3 DiscussionOur results demonstrate a great potential for implementing adaptive BCI beyond existing approaches, showing anincrease of 18% and 59% (using ErrP classifier) in bit rate.4 SignificanceAI outperforms other algorithms while offering a possibility of unifying various adaptive implementations withinone generic framework. Thanks to such genericity, with only a few tuning of its parameters, AI can incorporate manyfeatures, such as automated correction or accounting for an idle user state. It can adjust to signal variability by inferringabout the user, but it can also take into account the influence of its actions onto the user. This approach lays groundfor future co-adaptive systems.ReferencesFriston, K., Kilner, J., and Harrison, L. (2006). A free energy principle for the brain. Journal of Physiology-Paris,100(1-3):70–87.Mattout, J., Perrin, M., Bertrand, O., and Maby, E. (2015). Improving bci performance through co-adaptation: Appli-cations to the p300-speller. Annals of physical and rehabilitation medicine, 58(1):23–28.Mladenović, J., Mattout, J., and Lotte, F. (2017). A generic framework for adaptive EEG-based BCI training andoperation. In Nam, C. S., Nijholt, A., and Lotte, F., editors, Brain-Computer Interfaces Handbook: Technologi-cal and Theoretical Advances, volume 1 of Brain-Computer Interfaces Handbook: Technological and TheoreticalAdvances. CRC Press: Taylor & Francis Group.Perrin, M., Maby, E., Bouet, R., Bertrand, O., and Mattout, J. (2011). Detecting and interpreting responses to feedbackin bci. pages 116–119.7th International BCI Meeting, May 21-15, 2018 2

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