Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine [PDF]
Scientific ReportsThe study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN,
Samaneh Alsadat Saeedinia +3 more
doaj +6 more sources
Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture [PDF]
BioengineeringArtificial intelligence (AI) is revolutionising neuroimaging by enabling automated analysis, predictive analytics, and the discovery of biomarkers for neurological disorders.
Omar Garcia-Palencia +5 more
doaj +4 more sources
A Retinotopic Spiking Neural Network System for Accurate Recognition of Moving Objects Using NeuCube and Dynamic Vision Sensors [PDF]
Frontiers in Computational Neuroscience, 2018 This paper introduces a new system for dynamic visual recognition that combines bio-inspired hardware with a brain-like spiking neural network. The system is designed to take data from a dynamic vision sensor (DVS) that simulates the functioning of the ...
Lukas Paulun +3 more
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Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI [PDF]
Bioengineering, 2023 Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity.
Nikola K. Kasabov +3 more
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Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements [PDF]
Scientific Reports, 2021 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)
Kaushalya Kumarasinghe +2 more
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FusionSense: Emotion Classification Using Feature Fusion of Multimodal Data and Deep Learning in a Brain-Inspired Spiking Neural Network [PDF]
Sensors, 2020 Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the ...
Clarence Tan +3 more
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Lightweight Building of an Electroencephalogram-Based Emotion Detection System [PDF]
Brain Sciences, 2020 Brain–computer interface (BCI) technology provides a direct interface between the brain and an external device. BCIs have facilitated the monitoring of conscious brain electrical activity via electroencephalogram (EEG) signals and the detection of human ...
Abeer Al-Nafjan +2 more
doaj +2 more sources
Prediction and detection of virtual reality induced cybersickness: a spiking neural network approach using spatiotemporal EEG brain data and heart rate variability. [PDF]
Brain Inform, 2023 Virtual Reality (VR) allows users to interact with 3D immersive environments and has the potential to be a key technology across many domain applications, including access to a future metaverse.
Yang AHX, Kasabov NK, Cakmak YO.
europepmc +3 more sources
Machine learning with a snapshot of data: Spiking neural network 'predicts' reinforcement histories of pigeons' choice behavior. [PDF]
J Exp Anal Behav, 2022 An accumulated body of choice research has demonstrated that choice behavior can be understood within the context of its history of reinforcement by measuring response patterns. Traditionally, work on predicting choice behaviors has been based on the relationship between the history of reinforcement—the reinforcer arrangement used in training ...
Plessas A +4 more
europepmc +2 more sources
Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification. [PDF]
Sensors (Basel), 2020 Existing methods in neuromorphic olfaction mainly focus on implementing the data transformation based on the neurobiological architecture of the olfactory pathway. While the transformation is pivotal for the sparse spike-based representation of odor data,
Vanarse A +4 more
europepmc +5 more sources