Lossless EEG compression algorithm based on semi-supervised learning for VLSI implementation
YH Chen, YT Liu, TK Chi, CA Chen… - 2020 IEEE Asia …, 2020 - ieeexplore.ieee.org
YH Chen, YT Liu, TK Chi, CA Chen, YS Chiou, TL Lin, SL Chen
2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), 2020•ieeexplore.ieee.orgIn this paper, a hardware-oriented lossless EEG compression algorithm including a two-
stage prediction, voting prediction and tri-entropy coding is proposed. In two stages
prediction, 27 conditions and 6 functions are used to decide how to predict the current data
from previous data. Then, voting prediction finds optimal function according to 27 conditions
for best function to produce best Error (the difference of predicted data and current data).
Moreover, a tri-entropy coding technique is developed based on normal distribution. The two …
stage prediction, voting prediction and tri-entropy coding is proposed. In two stages
prediction, 27 conditions and 6 functions are used to decide how to predict the current data
from previous data. Then, voting prediction finds optimal function according to 27 conditions
for best function to produce best Error (the difference of predicted data and current data).
Moreover, a tri-entropy coding technique is developed based on normal distribution. The two …
In this paper, a hardware-oriented lossless EEG compression algorithm including a two-stage prediction, voting prediction and tri-entropy coding is proposed. In two stages prediction, 27 conditions and 6 functions are used to decide how to predict the current data from previous data. Then, voting prediction finds optimal function according to 27 conditions for best function to produce best Error (the difference of predicted data and current data). Moreover, a tri-entropy coding technique is developed based on normal distribution. The two-stage Huffman coding and Golomb-Rice coding was used to generate the binary code of Error value. In CHB-MIT Scalp EEG Database, the novel EEG compression algorithm achieves average compression rate to 2.37. The proposed hardware-oriented algorithm is suitable for VLSI implementation due to its low complexity.
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