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Rapid adaptation of brain–computer interfaces to new neuronal ensembles or participants via generative modelling


For brain–computer interfaces (BCIs), obtaining sufficient training data for algorithms that map neural signals onto actions can be difficult, expensive or even impossible. Here we report the development and use of a generative model—a model that synthesizes a virtually unlimited number of new data distributions from a learned data distribution—that learns mappings between hand kinematics and the associated neural spike trains. The generative spike-train synthesizer is trained on data from one recording session with a monkey performing a reaching task and can be rapidly adapted to new sessions or monkeys by using limited additional neural data. We show that the model can be adapted to synthesize new spike trains, accelerating the training and improving the generalization of BCI decoders. The approach is fully data-driven, and hence, applicable to applications of BCIs beyond motor control.

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Fig. 1: Experimental paradigm and training the baseline BCI LSTM decoder.
Fig. 2: General framework.
Fig. 3: Normalized position activity map, constructed as the histogram of neural activity as a function of position.
Fig. 4: Synthesized spike trains generated from specific kinematics.
Fig. 5: Correlation between synthesized and real spike trains.
Fig. 6: Cross-session decoding.
Fig. 7: Cross-subject decoding.

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Data availability

The raw and analysed datasets generated during the study are available on Github at

Code availability

The codes used in this study are available on Github at


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This work was supported by the National Science Foundation (grant no. CCF-1317433), C-BRIC (one of six centers in JUMP, a Semiconductor Research Corporation (SRC) program sponsored by DARPA), the Intel Corporation and the National Institutes of Health (grant nos. NIH NINDS T32 HD07418, F31 NS092356, NS053603 and NS074044). We affirm that the views expressed herein are solely our own and do not represent the views of the US government or any agency thereof.

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Authors and Affiliations



M.G.P. and L.E.M. conducted the experiments. S.W., A.Y., T.F. and L.I. analysed the results. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Shixian Wen or Laurent Itti.

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The authors declare no competing interests.

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Peer review information Nature Biomedical Engineering thanks Jonathan Viventi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 General Framework.

Step 1: training a neural spike synthesizer on the neural data from session one of Monkey one to learn a direct mapping from kinematics to spike trains and to capture the embedded neural attributes. Step 2: freezing the generator that captures the embedded neural attributes and fine-tuning the readout modules for different sessions or subjects to allow variations in neural attributes, using the neural data from session two of Monkey one or the neural data from session one of Monkey two. Then, synthesizing a large amount of spike trains that are suitable for another session or subject. Step 3: training a BCI decoder for another session or subject using the same small amount of real neural data used for fine-tuning (in step 2) and a large amount of synthesized spike trains (in step 2). Step 4: testing the same BCI decoder on an independent test set from another session or subject.

Extended Data Fig. 2 Cross-session decoding.

The GAN-augmentation, mutation-augmentation, stretch-augmentation, real-concatenation and real-only methods are shown in red, purple, orange, blue and green curves with an error bar in 5-fold cross-validation. The horizontal axis is the number of minutes of neural data from the session two of Monkey C used. The vertical axis is correlation coefficient between the decoded kinematics and real kinematics on an independent test set from the session two of Monkey C (mean +/ - S.D., n = 5 folds). Synthesized spike trains that capture the neural attributes accelerate the training of a BCI decoder for the cross-session decoding.

Extended Data Fig. 3 Cross-subject decoding.

The GAN-augmentation, mutation-augmentation, stretch-augmentation, real-concatenation and real-only methods are shown in red, purple, orange, blue and green curves with an error bar in 5-fold cross-validation. The horizontal axis is the number of minutes of neural data from Monkey M used. The vertical axis is the correlation coefficient between the decoded kinematics and real kinematics on an independent test set from the Monkey M (mean + / - S.D., n = 5 folds). When the neural data from another subject is limited, synthesized spike trains that capture the neural attributes improve the cross-subject decoding performance on acceleration. Even with ample neural data for both subjects, the neural attributes learned from one subject can transfer some useful knowledge that improves the best achievable decoding performance on the acceleration of another subject.

Extended Data Fig. 4

Detailed structure of the CC-LSTM-GAN.

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Wen, S., Yin, A., Furlanello, T. et al. Rapid adaptation of brain–computer interfaces to new neuronal ensembles or participants via generative modelling. Nat. Biomed. Eng 7, 546–558 (2023).

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