%A M.K. Alam
%A A.A. Aziz
%A S.A. Latif
%A A. Awang
%I Institute of Electrical and Electronics Engineers Inc.
%T EEG Data Compression using Truncated Singular Value Decomposition for Remote Driver Status Monitoring
%P 323-327
%X Advancements in wireless body sensor technology have enabled continuous recording of Electroencephalogram (EEG) data for remote monitoring. However, a significant amount of data introduced due to the continuous data recording over time has become a challenge for energy constraint sensor nodes to transfer the data to the remote stations. Therefore, many researchers explore data compression techniques to solve the large-scale data issue by compressing before the raw data are transmitted to the sink. This paper proposes a Truncated Singular Value Decomposition (TSVD) technique to compress raw EEG data by eliminating the high volume of redundant data. At the pre-processing stage, collected EEG data are reshaped to a 2-D matrix then the matrix is transformed into the subspace or vector-space using TSVD for to compress the matrix based on the correlation of the data. Afterwards, the proposed technique reconstructs the compressed data at the remote station for further analysis. Various performance metrics are utilized to evaluate the proposed technique. Simulation results show that the proposed technique suppresses a big amount of redundant data with acceptable distortion of the original data. © 2019 IEEE.
%K Data communication systems; Electroencephalography; Functional analysis; Monitoring; Remote control; Sensor nodes; Singular value decomposition; Vector spaces, Data compression techniques; Driver safety; Eeg datum; Electroencephalogram (EEG) datum; Performance metrics; Remote monitoring; Truncated singular value decomposition; TSVD, Data compression
%R 10.1109/SCORED.2019.8896252
%D 2019
%J 2019 IEEE Student Conference on Research and Development, SCOReD 2019
%L scholars11232
%O cited By 5; Conference of 17th IEEE Student Conference on Research and Development, SCOReD 2019 ; Conference Date: 15 October 2019 Through 17 October 2019; Conference Code:154444