Investigating the lossless compression of EEG data
LE3 .A278 2018
Bachelor of Science
An electroencephalograph, known as an EEG, is of great interest to the medical research community. The waveform of EEG data has a high level of similarity to that of audio signals. In this thesis, theories and principles of a very popular audio compression tool|the Free Lossless Audio Codec|will be discussed. FLAC applies the traditional audio compression structure, which is used by a variety of lossless audio compression techniques. Spectral analysis will be used to extract the frequency components from signals and these will be used to construct an alternative predictor. The created FFT-FLAC encoder achieved a compression ratio of 0.494 for a long-term EEG dataset. Another new predictor using the Wavelet Neural Network will be tested on EEG signals to show the potential of machine learning techniques for this fleld. Because EEGs are virtually always multi-channel data, the use of intra-channel redundancy to improve compression ratios is investigated.
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