Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals
using Novel Wavelet Packet Decomposition in Combination with Canonical
Correlation Analysis
The electroencephalogram (EEG) and functional near-infrared spectroscopy
(fNIRS) signals, highly non-stationary in nature, greatly suffers from motion
artifacts while recorded using wearable sensors. This paper proposes two robust
methods: i) Wavelet packet decomposition (WPD), and ii) WPD in combination with
canonical correlation analysis (WPD-CCA), for motion artifact correction from
single-channel EEG and fNIRS signals. The efficacy of these proposed techniques
is tested using a benchmark dataset and the performance of the proposed methods
is measured using two well-established performance matrices: i) Difference in
the signal to noise ratio ({\Delta}SNR) and ii) Percentage reduction in motion
artifacts ({\eta}). The proposed WPD-based single-stage motion artifacts
correction technique produces the highest average {\Delta}SNR (29.44 dB) when
db2 wavelet packet is incorporated whereas the greatest average {\eta} (53.48%)
is obtained using db1 wavelet packet for all the available 23 EEG recordings.
Our proposed two-stage motion artifacts correction technique i.e. the WPD-CCA
method utilizing db1 wavelet packet has shown the best denoising performance
producing an average {\Delta}SNR and {\eta} values of 30.76 dB and 59.51%,
respectively for all the EEG recordings. On the other hand, the two-stage
motion artifacts removal technique i.e. WPD-CCA has produced the best average
{\Delta}SNR (16.55 dB, utilizing db1 wavelet packet) and largest average {\eta}
(41.40%, using fk8 wavelet packet). The highest average {\Delta}SNR and {\eta}
using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and
26.40%, respectively for all the fNIRS signals using fk4 wavelet packet. In
both EEG and fNIRS modalities, the percentage reduction in motion artifacts
increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques
are employed.Comment: 25 pages, 10 figures and 2 table