We develop a new source imaging technique, Deep Brain Neural Network (DeepBraiNNet), for robust sparse spatiotemporal EEG source estimation.
Abstract—EEG inverse problem is underdetermined, which poses a long standing challenge in Neuroimaging. The combination of source-imaging and analysis of ...
The combination of source-imaging and analysis of cortical directional networks enables us to noninvasively explore the underlying neural processes. However, ...
Here, we develop a new source imaging technique, Deep Brain Neural Network (DeepBraiNNet), for robust sparse spatiotemporal EEG source estimation. In ...
People also ask
What is a long short-term memory network LSTM?
What is the purpose of the input, output, and forget gates in long short-term memory (LSTM) networks?
Evaluation of Long-Short Term Memory Networks for M/EEG ...
www.biorxiv.org › content › 2022.04.13...
Jan 26, 2023 · The LSTM network shows higher accuracy on multiple metrics and for varying numbers of neural sources, compared to classical inverse solutions ...
Apr 17, 2022 · We present a long-short term memory (LSTM) network to solve the M/EEG inverse problem. It integrates several aspects essential for qualitative ...
EEG source localization using spatio-temporal neural network
www.semanticscholar.org › paper › EEG...
A novel source localization method is proposed to model the EEG inverse problem using spatio-temporal long-short term memory recurrent neural networks (LSTM)
Apr 13, 2022 · In this paper, we propose to use graph Fourier transform (GFT) based bidirectional long-short term memory (BiLSTM) neural network to solve the ESI problem.
Mar 9, 2021 · In our approach, source estimates are spatially filtered, or “corrected,” by a prediction that has been generated by a network of long short- ...
The LSTM network shows higher accuracy on multiple metrics and for varying numbers of neural sources, compared to classical inverse solutions but also compared ...