ArticleScientific reports2021
Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 83 citations in OpenAlex.
- Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets Through Dynamic Dense Scattering Media.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- eXCube2: Explainable Brain-Inspired Spiking Neural Network Framework for Emotion Recognition from Audio, Visual and Multimodal Audio-Visual Data.Biomimetics (Basel, Switzerland) · 2026Article
- Distributed implantable microchips as wireless networks for neural sensing and stimulation.Frontiers in neuroscience · 2026Review
- JiSuJi, a virtual muscle for small animal simulations, accurately predicts force from naturalistic spike trains.bioRxiv : the preprint server for biology · 2025Article
- Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture.Bioengineering (Basel, Switzerland) · 2025Review
- In vivo electrophysiology recordings and computational modeling can predict octopus arm movement.Bioelectronic medicine · 2025Article
- Single unit electrophysiology recordings and computational modeling can predict octopus arm movement.bioRxiv : the preprint server for biology · 2024Article
- Spiking neural networks for physiological and speech signals: a review.Biomedical engineering letters · 2024Review
- Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine.Scientific reports · 2024Article
- Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram.Scientific reports · 2024Article
- An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors.Nature electronics · 2024Article
- Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI.Bioengineering (Basel, Switzerland) · 2023Article
- From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?Brain sciences · 2023Review
- ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network.Frontiers in neuroscience · 2023Article
- Current Applications of Machine Learning in Spine: From Clinical View.Global spine journal · 2022Review
- Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compared to the abilities of the animal brain, many Artificial Intelligence systems have limitations which emphasise the need for a Brain-Inspired Artificial Intelligence paradigm. This paper proposes a novel Brain-Inspired Spiking Neural Network (BI-SNN) model for incremental learning of spike sequences. BI-SNN maps spiking activity from input channels into a high dimensional source-space which enhances the evolution of polychronising spiking neural populations. We applied the BI-SNN to predict muscle activity and kinematics from electroencephalography signals during upper limb functional movements. The BI-SNN extends our previously proposed eSPANNet computational model by integrating it with the 'NeuCube' brain-inspired SNN architecture. We show that BI-SNN can successfully predict continuous muscle activity and kinematics of upper-limb. The experimental results confirmed that the BI-SNN resulted in strongly correlated population activity and demonstrated the feasibility for real-time prediction. In contrast to the majority of Brain-Computer Interfaces (BCIs) that constitute a 'black box', BI-SNN provide quantitative and visual feedback about the related brain activity. This study is one of the first attempts to examine the feasibility of finding neural correlates of muscle activity and kinematics from electroencephalography using a brain-inspired computational paradigm. The findings suggest that BI-SNN is a better neural decoder for non-invasive BCI.
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