Towards an actor-based model of the neurofeedback/BCI closed-loop

International audienceNeurofeedback training describes a closed-loop paradigm in which a Brain-Computer Interface is typically used to provide a subject with an evaluation of his/her own mental states. As a learning process, it aims at enabling the subject to apprehend his or her own latentcognitive states in order to modulate it. Its use for therapeutic purposes has gained a lot of traction in the public sphere in the last decade, but conflicting evidence concerning its efficacy has led to increasing efforts by the scientific community to provide better explanations for the cognitive mechanisms at work. We intend to contribute to this effort by proposing a mathematical formalization of the mechanisms at play in this (arguably) quite complex dynamical system.Due to the subjective nature of the task, a representation of the subject and experimenter separate beliefs and hypothesis is an important first step to propose a meaningful approximation. We provide a first model of the training loop based on those considerations, introducing two pipelines. The direct pipeline (subject-> feedback) makes use of a coupling between cognitive and physiological states to infer latent cognitive states from measurement. The return pipeline (feedback-> subject) describes how perception of the indicator impacts subject behaviour. To describe the behaviour of an agent facing an uncertain environment, we make use of the Active Inference framework (1), a bayesian approach to belief updating that provides a biologically plausible model of perception, action and learning. The ensuing model is then leveraged to simulate computationally the behaviour and evolvingbeliefs of a neurofeedback training subject in tasks of varying nature and difficulty. We finally analyze the effects of several sources of error such as measurement noise or uncertainty surrounding the choice of the biomarker to conclude on their influence on training efficacy

Modeling subject perception and behaviour during neurofeedback training

International audienceNeurofeedback training (NFT) describes a closed-loop paradigm in which a subject is provided with a real time evaluation of his/her brain activity. As a learning process, it is designed to help the subject learn to apprehend his/her own cognitive states and better modulate them through mental actions. Its use for therapeutic purposes has gained a lot of traction in the public sphere in the last decade, but conflicting evidence concerning its efficacy has led to a two-pronged effort from the scientific community. First, a call for experimental protocols and reports standardization [1], aiming to reduce the variability of the results and provide a reliable set of data to describe empirical findings. Second, an effort towards a formal description of the neurofeedback loop and the main hypotheses that guide the design of our experiments, in order to explain or even predict the effects of such training [2,3]

Modeling subject perception and behaviour during neurofeedback training

International audienceNeurofeedback training (NFT) describes a closed-loop paradigm in which a subject is provided with a real time evaluation of his/her brain activity. As a learning process, it is designed to help the subject learn to apprehend his/her own cognitive states and better modulate them through mental actions. Its use for therapeutic purposes has gained a lot of traction in the public sphere in the last decade, but conflicting evidence concerning its efficacy has led to a two-pronged effort from the scientific community. First, a call for experimental protocols and reports standardization [1], aiming to reduce the variability of the results and provide a reliable set of data to describe empirical findings. Second, an effort towards a formal description of the neurofeedback loop and the main hypotheses that guide the design of our experiments, in order to explain or even predict the effects of such training [2,3]

Towards an actor-based model of the neurofeedback/BCI closed-loop

International audienceNeurofeedback training describes a closed-loop paradigm in which a Brain-Computer Interface is typically used to provide a subject with an evaluation of his/her own mental states. As a learning process, it aims at enabling the subject to apprehend his or her own latentcognitive states in order to modulate it. Its use for therapeutic purposes has gained a lot of traction in the public sphere in the last decade, but conflicting evidence concerning its efficacy has led to increasing efforts by the scientific community to provide better explanations for the cognitive mechanisms at work. We intend to contribute to this effort by proposing a mathematical formalization of the mechanisms at play in this (arguably) quite complex dynamical system.Due to the subjective nature of the task, a representation of the subject and experimenter separate beliefs and hypothesis is an important first step to propose a meaningful approximation. We provide a first model of the training loop based on those considerations, introducing two pipelines. The direct pipeline (subject-> feedback) makes use of a coupling between cognitive and physiological states to infer latent cognitive states from measurement. The return pipeline (feedback-> subject) describes how perception of the indicator impacts subject behaviour. To describe the behaviour of an agent facing an uncertain environment, we make use of the Active Inference framework (1), a bayesian approach to belief updating that provides a biologically plausible model of perception, action and learning. The ensuing model is then leveraged to simulate computationally the behaviour and evolvingbeliefs of a neurofeedback training subject in tasks of varying nature and difficulty. We finally analyze the effects of several sources of error such as measurement noise or uncertainty surrounding the choice of the biomarker to conclude on their influence on training efficacy