Paper:
A multi-agent control framework for co-adaptation in brain-computer interfaces
In a closed-loop brain-computer interface (BCI), adaptive decoders are used to learn parameters suited to decoding the user's neural response. Feedback to the user provides information which permits the neural tuning to also adapt. We present an approach to model this process of co-adaptation between the encoding model of the neural signal and the decoding algorithm as a multi-agent formulation of the linear quadratic Gaussian (LQG) control problem. In simulation we characterize how decoding performance improves as the neural encoding and adaptive decoder optimize, qualitatively resembling experimentally demonstrated closed-loop improvement. We then propose a novel, modified decoder update rule which is aware of the fact that the encoder is also changing and show it can improve simulated co-adaptation dynamics. Our modeling approach offers promise for gaining insights into co-adaptation as well as improving user learning of BCI control in practical settings.

Review:
In brain-computer interface (BCI), co-adaptation is an 
      important issue, because not only the decoders, but also the brain 
      activities can also adapt through feedback. To investigate this problem, 
      the paper introduced a split linear quadratic Gaussian (LQG) system. Based 
      on the theoretical framework used in the multi-agent control theory, the 
      authors proposed interleaved algorithm combining linear-quadratic 
      estimation (LQE) for decoder updates and linear-quadratic regulator (LQR) 
      for encoder updates with recursive least squares (RLS) to estimate the 
      system parameters of the other agents (Procedure 1). They modified further 
      the co-adaptive decoder by incorporating r-step ahead estimators of the 
      encoder parameters (Procedure 2). For proof of concepts, a numerical 
      experiment with both procedures was conducted and the results looks 
      promising. Quality: Although I did not check all the 
      equations, the paper is technically sound except for the second line of 
      Equation (13). It should be a scaler, but the matrix multiplications do 
      not become scalars, probably. The authors employed a simple LQG system to 
      investigate the co-adaptation problem. The simulation results nicely 
      demonstrated that the procedures work as intended in a rather ideal 
      situation. However, it is not clear from the manuscript how useful their 
      algorithms are in actual BCI applications. Clarity: The paper 
      is well organized and clear enough. Originality: I was not 
      aware of a theoretical framework for investigating co-adaptation problem 
      in BCI community (I have not read important references). The authors have 
      in mind 3D tracking applications of BCI with invasive devices, where the 
      LQG system is more appropriate. As future extensions, incorporating some 
      nonlinearity is necessary to deal with other type of BCI applications. 
      Significance: Although there is no validation of the proposed 
      procedures with practical BCI applications, I think this work could 
      inspire many BCI researchers who are looking for efficient feedback 
      schemes to enhance BCI performances and to increase the number of BCI 
      users. Based on the above criteria, I would think that this 
      paper is a fair contribution marginally above the threshold. One reason is 
      that such a theoretical framework could inspire other researchers in the 
      field.