Intention estimation in brain-machine interfaces

Journal of Neural Engineering (Impact Factor: 3.3). 02/2014; 11(1):016004. DOI: 10.1088/1741-2560/11/1/016004
Source: PubMed


The objective of this work was to quantitatively investigate the mechanisms underlying the performance gains of the recently reported 'recalibrated feedback intention-trained Kalman Filter' (ReFIT-KF).
This was accomplished by designing variants of the ReFIT-KF algorithm and evaluating training and online data to understand the neural basis of this improvement. We focused on assessing the contribution of two training set innovations of the ReFIT-KF algorithm: intention estimation and the two-stage training paradigm.
Within the two-stage training paradigm, we found that intention estimation independently increased target acquisition rates by 37% and 59%, respectively, across two monkeys implanted with multiunit intracortical arrays. Intention estimation improved performance by enhancing the tuning properties and the mutual information between the kinematic and neural training data. Furthermore, intention estimation led to fewer shifts in channel tuning between the training set and online control, suggesting that less adaptation was required during online control. Retraining the decoder with online BMI training data also reduced shifts in tuning, suggesting a benefit of training a decoder in the same behavioral context; however, retraining also led to slower online decode velocities. Finally, we demonstrated that one- and two-stage training paradigms performed comparably when intention estimation is applied.
These findings highlight the utility of intention estimation in reducing the need for adaptive strategies and improving the online performance of BMIs, helping to guide future BMI design decisions.

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    • "This learning trend results in larger performance gains within daily sessions (Ganguly and Carmena, 2010; Krakauer and Mazzoni, 2011). A more recent study reported that a two-stage decoder calibration process – in which the first stage initializes the decoder while the other recalibrates it based on closed loop brain control data – results in a reduction in the observed changes in neuronal tuning between training and test data (Fan et al., 2014). Since the decoder defines a causal link between the neural activity and the state of the end effector, a few studies have examined the extent to which subjects could learn to adapt to perturbations to a given decoding rule (Jarosiewicz et al., 2008; Chase et al., 2012; Sadtler et al., 2014; Ganguly and Carmena, 2009; Golub et al., 2012). "
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    • "The Kalman filter’s Gaussian noise model clearly does not match the data (spike counts), yet due to its accuracy and execution speed the method has remained popular since its first use by Wu et al. (2003) (Aggarwal et al., 2013; Chen et al., 2013; Dangi et al., 2013a,c; Homer et al., 2013; Ifft et al., 2013; Jarosiewicz et al., 2013; Kao et al., 2013; Merel et al., 2013; Wong et al., 2013; Zhang and Chase, 2013; Fan et al., 2014; Golub et al., 2014; Gowda et al., 2014; Homer et al., 2014). While point process filters (for a review, see Koyama et al., 2010) offer a more realistic noise model, their use in decoding is still relatively rare (Shanechi et al., 2013; Velliste et al., 2014; Xu et al., 2014), due in part to their heavier computational burden. "
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