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In order to decrease communication bandwidth and transmission time in portable or low cost devices, data compression is required. In this paper we consider the ...
Energy Efficient Method for Motor Imagery Data Compression
www.researchgate.net › publication › 31...
Nov 6, 2024 · In this paper we consider the use of fast Discrete Cosine Transform (DCT) algorithms for lossy EEG data compression. Using this approach, the ...
Apr 23, 2024 · To overcome this issue, we propose an adaptive feature learning model that employs a Riemannian geometric approach to generate a feature matrix ...
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This study proposes an efficient MI-EEG classification framework utilizing a sparse representation of brain connectivity features and a dictionary learned from ...
3 days ago · In this study, we proposed a novel methodology that includes an innovative preprocessing step and a new model for MI EEG classification. In the ...
Mar 1, 2024 · This paper presents a non-iterative and fast algorithm for reconstructing EEG signals using compressed sensing and deep learning techniques.
An Energy Efficient Compressed Sensing Framework for the ... - NCBI
www.ncbi.nlm.nih.gov › PMC3926621
We propose the use of a compressed sensing (CS) framework to efficiently compress these signals at the sensor node. Our framework exploits both the temporal ...
Missing: Motor Imagery
This paper presents a comprehensive review of advancements in wireless EEG communication and analysis, with an emphasis on their role in next-generation green ...
An unsupervised dimensionality reduction method, i.e., the ELM-AE method, is proposed to compress redundant CSP features. Compared with the mutual ...
By embedding algorithms at the sensor node, the data is analyzed locally, preserving privacy, reducing the energy consumption for a longer battery lifetime, and ...