HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification
G Dai, J Zhou, J Huang, N Wang - Journal of neural engineering, 2020 - iopscience.iop.org
Objective. Electroencephalography (EEG) motor imagery classification has been widely
used in healthcare applications such as mobile assistive robots and post-stroke …
used in healthcare applications such as mobile assistive robots and post-stroke …
Plasticity and adaptation in neuromorphic biohybrid systems
Neuromorphic systems take inspiration from the principles of biological information
processing to form hardware platforms that enable the large-scale implementation of neural …
processing to form hardware platforms that enable the large-scale implementation of neural …
An on-chip processor for chronic neurological disorders assistance using negative affectivity classification
Chronic neurological disorders (CND's) are lifelong diseases and cannot be eradicated, but
their severe effects can be alleviated by early preemptive measures. CND's, such as …
their severe effects can be alleviated by early preemptive measures. CND's, such as …
Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware
Objective. The objective of this work is to use the capability of spiking neural networks to
capture the spatio-temporal information encoded in time-series signals and decode them …
capture the spatio-temporal information encoded in time-series signals and decode them …
NF-EEG: A generalized CNN model for multi class EEG motor imagery classification without signal preprocessing for brain computer interfaces
E Arı, E Taçgın - Biomedical Signal Processing and Control, 2024 - Elsevier
Abstract Objective Brain Computer Interface (BCI) systems have been developed to identify
and classify brain signals and integrate them into a control system. Even though many …
and classify brain signals and integrate them into a control system. Even though many …
Instrumentation, Measurement, and Signal Processing in Electroencephalography-Based Brain–Computer Interfaces: Situations and Prospects
Z Xue, Y Zhang, H Li, H Chen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Proper signal measurement and processing are crucial in electroencephalography (EEG)-
based brain-computer interfaces (BCIs), as they form the basis of brain insight and precise …
based brain-computer interfaces (BCIs), as they form the basis of brain insight and precise …
Demonstrating the viability of mapping deep learning based EEG decoders to spiking networks on low-powered neuromorphic chips
M Pals, RJP Belizón, N Berberich… - 2021 43rd Annual …, 2021 - ieeexplore.ieee.org
Accurate and low-power decoding of brain signals such as electroencephalography (EEG)
is key to constructing brain-computer interface (BCI) based wearable devices. While deep …
is key to constructing brain-computer interface (BCI) based wearable devices. While deep …
Effect of a click-like feedback on motor imagery in EEG-BCI and eye-tracking hybrid control for telepresence
A Petrushin, J Tessadori, G Barresi… - 2018 IEEE/ASME …, 2018 - ieeexplore.ieee.org
Motor Imagery (MI) is one of the most promising paradigms of electroencephalographic
(EEG) brain-computer interfaces (BCIs) for people with severe motor impairments. Since MI …
(EEG) brain-computer interfaces (BCIs) for people with severe motor impairments. Since MI …
Detection of brain abnormalities from spontaneous electroencephalography using spiking neural network
R Sahu, SR Dash - … Concepts, Applications, and Future Directions, Volume …, 2023 - Springer
A huge amount of people are suffering from brain abnormalities. Different machine learning
approaches and deep learning approaches are implemented on the …
approaches and deep learning approaches are implemented on the …
Catalogic systematic literature review of hardware-accelerated neurodiagnostic systems
Computer-aided diagnosis (CAD) plays a key role in automating and enhancing the
diagnosis of complex neurological disorders. Computers are not just used to automate the …
diagnosis of complex neurological disorders. Computers are not just used to automate the …