Fig.3 EEG recoding and additional sensing system
After the basic preprocessing, IMU data was filtered with a low-passed
filter (cut-off frequency is 10Hz), and combing with the foot pressure
signals from the heels, the onset of each standing and sitting action
was determined. Then, all the data under the three conditions were
segmented into the epochs lasting about 7s, including 4.5s prior and
2.5s post the onset of the motion action, and baseline correction were
completed. Then the epochs which is greatly affected by artifacts were
removed through visual detection, and the ICA decomposition of all
signals was completed using runica. Artifact component which from eye
movement, eye blink, muscle artifact and other artifacts mainly caused
by the movement were removed with the help of SASICA toolbox. After the
artifact remove, if necessary, the bad electrode was interpolated and
the data was re-referenced. Finally, the 30 channels from the whole
brain were divided into eight regions, which is left frontal (LF: FP1,
F3, F3), right frontal (RF:FP2,F4,F8), left central
(LC:FC1,FC5,C3,CP1,CP5), right central (RC: FC2, FC6, C4, CP2, CP6),
left temporal (LT:T7), right temporal (RT: T8), left occipital
(LO:P3,P7,O1) and right occipital (RO: P4, P8, O2).
Complexity analysis is an important tool to reveal the characteristics
of a nonlinear system. In recently years, more and more researchers
began to evaluate the activity state of the brain through the nonlinear
dynamic analysis [11]. Among them, entropy is one of the most widely
used analysis methods. At present, various entropy analysis have been
used for the neural signal analysis [11,12]. In order to more
comprehensively discuss the representation of various entropy on EEGs
during motion, this study calculated various time-domain entropies, such
as Shannon Entropy (ShEn), Approximate Entropy (ApEn), Sample Entropy
(SaEn), Permutation Entropy (PeEn), Conditional Entropy (CoEn), and
Fuzzy Entropy (FuEn). Besides, Spectral Entropy (SpEn) and Wavelet
Entropy (WaEn) which representing time-frequency characteristics were
also discussed. In addition, we also discussed the Hurst index, Kurtosis
index and Hjorth parameters. These measures were calculated for the
averaged signals in each region. Finally, through the statistical
analysis, we selected the brain regions and the complexity measures
which shows significant differences among the three conditions to form
the feature vector and several machine learning classifiers were used to
achieve the recognition of the sitting and standing condition.
Results and Discussion: In order to conduct quantitative analysis
of the complexity measures in each brain region under the three
conditions, ten complexity measures were calculated for averaged EEGs of
each brain region respectively. We found that the values of various
parameters in the eight regions are very closed and these parameters in
LO and RO region are the largest, followed by the LC and RC region.
Statistical analysis found that PeEn, ShEn, SpEn and Kurtosis in RT
region were significantly different (t-test, p <0.05)
between standing and sitting, and the ShEn and Kutosis in LF region,
Kutosis in RF region, CoEn, ShEn and Kurtosis in RT region, and Kurtosis
in LC, RC, LO and RO region shows significant difference between
standing and quiet. While the CoEn, SaEn, ShEn and Kurtosis in LF
region, Kurtosis in RF region, ShEn in RT region, Kurtosis in LC region,
ShEn and Kurtosis in RC region and Kurtosis in LO and RO region shows
significant different between sitting and quiet.
Based on the above discussed complexities of the EEGs in each region,
combined with the statistical analysis results, the feature vector was
constructed with these parameters which has significant difference among
the three conditions. Three machine learning algorithms, including
support vector machine (SVM), logistic regression (LR) and linear
discriminant analysis (LDA), were used to test these features to
complete the recognition of two types of motion condition. As show in
Fig.4 is the averaged classification accuracy obtained after five-fold
cross validation. It can be seen that all the classification accuracies
are over 81% and the SVM has the best effect. The classification
accuracy of standing and sitting, standing and quiet, and sitting and
quiet are 83.3%, 87% and 82.5% respectively, which proves the
effectiveness of this method.