Abstract
Neural decoding is crucial to translate the neural activity for Brain-Computer Interfaces (BCIs) and provides information on how external variables (e.g., movement) are represented and encoded in the neural system. Convolutional neural networks (CNNs) are emerging as neural decoders for their high predictive power and are largely applied with electroencephalographic signals; these algorithms, by automatically learning the more relevant class-discriminative features, improve decoding performance over classic decoders based on handcrafted features. However, applications of CNNs for single-neuron decoding are still scarce and require further validation. In this study, a CNN architecture was designed via Bayesian optimization and was applied to decode different grip types from the activity of single neurons of the posterior parietal cortex of macaque (area V6A). The Bayesian-optimized CNN significantly outperformed a naïve Bayes classifier, commonly used for neural decoding, and proved to be robust to a reduction of the number of cells and of training trials. Adopting a sliding window decoding approach with a high time resolution (5 ms), the CNN was able to capture grip-discriminant features early after cuing the animal, i.e., when the animal was only attending the object to grasp, further supporting that grip-related neural signatures are strongly encoded in V6A already during movement preparation. The proposed approach may have practical implications in invasive BCIs to realize accurate and robust decoders, and may be used together with explanation techniques to design a general tool for neural decoding and analysis, boosting our comprehension of neural encoding.
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References
Wolpaw, J., Wolpaw, E.W.: Brain-Computer Interfaces: Principles and Practice. Oxford University Press, USA (2012)
Filippini, M., Borra, D., Ursino, M., Magosso, E., Fattori, P.: Decoding sensorimotor information from superior parietal lobule of macaque via Convolutional Neural Networks. Neural Netw. 151, 276–294 (2022). https://doi.org/10.1016/j.neunet.2022.03.044
Filippini, M., Breveglieri, R., Akhras, M.A., Bosco, A., Chinellato, E., Fattori, P.: Decoding information for grasping from the macaque dorsomedial visual stream. J. Neurosci. 37, 4311–4322 (2017). https://doi.org/10.1523/JNEUROSCI.3077-16.2017
Filippini, M., Breveglieri, R., Hadjidimitrakis, K., Bosco, A., Fattori, P.: Prediction of reach goals in depth and direction from the parietal cortex. Cell Rep. 23, 725–732 (2018). https://doi.org/10.1016/j.celrep.2018.03.090
Solon, A.J., Lawhern, V.J., Touryan, J., McDaniel, J.R., Ries, A.J., Gordon, S.M.: Decoding P300 variability using convolutional neural networks. Front. Hum. Neurosci. 13, 201 (2019). https://doi.org/10.3389/fnhum.2019.00201
Borra, D., Fantozzi, S., Magosso, E.: Interpretable and lightweight convolutional neural network for EEG decoding: application to movement execution and imagination. Neural Netw. 129, 55–74 (2020). https://doi.org/10.1016/j.neunet.2020.05.032
Borra, D., Fantozzi, S., Magosso, E.: A lightweight multi-scale convolutional neural network for p300 decoding: analysis of training strategies and uncovering of network decision. Front. Hum. Neurosci. 15, 655840 (2021). https://doi.org/10.3389/fnhum.2021.655840
Borra, D., Magosso, E.: Deep learning-based EEG analysis: investigating P3 ERP components. J. Integr. Neurosci. 20, 791–811 (2021). https://doi.org/10.31083/j.jin2004083
Livezey, J.A., Glaser, J.I.: Deep learning approaches for neural decoding across architectures and recording modalities. Brief. Bioinform. 22, 1577–1591 (2021). https://doi.org/10.1093/bib/bbaa355
Craik, A., He, Y., Contreras-Vidal, J.L.: Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16, 031001 (2019). https://doi.org/10.1088/1741-2552/ab0ab5
Suhaimi, N.S., Mountstephens, J., Teo, J.: EEG-based emotion recognition: a state-of-the-art review of current trends and opportunities. Comput. Intell. Neurosci. 2020, 1–19 (2020). https://doi.org/10.1155/2020/8875426
Simões, M., et al.: BCIAUT-P300: a multi-session and multi-subject benchmark dataset on autism for p300-based brain-computer-interfaces. Front. Neurosci. 14, 568104 (2020). https://doi.org/10.3389/fnins.2020.568104
Borra, D., Magosso, E., Castelo-Branco, M., Simoes, M.: A Bayesian-optimized design for an interpretable convolutional neural network to decode and analyze the P300 response in autism. J. Neural Eng. 19 (2022). https://doi.org/10.1088/1741-2552/ac7908
Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38, 5391–5420 (2017)
Mulliken, G.H., Musallam, S., Andersen, R.A.: Decoding trajectories from posterior parietal cortex ensembles. J. Neurosci. 28, 12913–12926 (2008). https://doi.org/10.1523/JNEUROSCI.1463-08.2008
Aflalo, T., et al.: Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science 348, 906–910 (2015). https://doi.org/10.1126/science.aaa5417
Chinellato, E., Grzyb, B.J., Marzocchi, N., Bosco, A., Fattori, P., del Pobil, A.P.: The Dorso-medial visual stream: from neural activation to sensorimotor interaction. Neurocomputing 74, 1203–1212 (2011). https://doi.org/10.1016/j.neucom.2010.07.029
Fattori, P., Breveglieri, R., Bosco, A., Gamberini, M., Galletti, C.: Vision for prehension in the medial parietal cortex. Cereb. Cortex. bhv302 (2015). https://doi.org/10.1093/cercor/bhv302
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Bach, F. and Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. pp. 448–456. PMLR, Lille (2015)
Clevert, D.-A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). arXiv preprint (2015)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Frazier, P.I.: A tutorial on Bayesian optimization (2018). http://arxiv.org/abs/1807.02811
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 [cs] (2017)
Smith, S., Nichols, T.: Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44, 83–98 (2009). https://doi.org/10.1016/j.neuroimage.2008.03.061
Nowak, M., Zich, C., Stagg, C.J.: Motor cortical gamma oscillations: what have we learnt and where are we headed? Curr. Behav. Neurosci. Rep. 5(2), 136–142 (2018). https://doi.org/10.1007/s40473-018-0151-z
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv:1312.6034 [cs] (2014)
Funding
This study was supported by PRIN 2017 – Prot. 2017KZNZLN and MAIA project. MAIA project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 951910. This article reflects only the author’s view, and the Agency is not responsible for any use that may be made of the information it contains.
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Borra, D., Filippini, M., Ursino, M., Fattori, P., Magosso, E. (2023). A Bayesian-Optimized Convolutional Neural Network to Decode Reach-to-Grasp from Macaque Dorsomedial Visual Stream. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13811. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_36
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