Investigating the Effect of Flickering Frequency Pair and Mother Wavelet Selection in Steady-State Visually-Evoked Potentials on Two-Command Brain-Computer Interfaces

IntroductionSteady-state visually evoked potentials (SSVEPs) have become popular in brain-computer interface (BCI) applications in addition to many other applications on clinical neuroscience (neurodegenerative disorders, schizophrenia, epilepsy, etc.), cognitive (visual attention, working memory, brain rhythms, etc.), and use of engineering researches. Among available methods to measure brain activities, SSVEPs have advantages like higher information transfer rate, simplicity in structure, and short training time. SSVEP-based BCIs use flickering stimuli at different frequencies to discriminate distinct commands in real life. Some features are extracted from the SSVEP signals before these commands are classified. The wavelet transform (WT) has attracted researchers among feature extraction methods since it utilizes the non-stationary signals well. In the WT, a sample function (named mother wavelet) represents the SSVEP signal in both time and frequency domains. Unfortunately, there is no universal mother wavelet function that fits all the signals. Therefore, choosing an appropriate mother wavelet function may be a challenge in WT-related studies. Although there are such studies in three- and seven-command SSVEP-based studies, there is no study for two-command systems in our knowledge.Materials and MethodsIn this study, two user commands flickered at the combinations of seven different frequencies were tested to determine which frequency pairs give the highest performance. For this purpose, three well-known wavelet features (energy, entropy, and variance) were calculated for each of derived EEG frequency bands from the discrete WT coefficients of SSVEP signals. The WT was repeated for six different mother wavelet functions (Haar, Db4, Sym4, Coif1, Bior3.5, and Rbior2.8). Then, four feature sets (every three features, and all together) were applied to seven commonly-used machine learning algorithms (Decision Tree, Discriminant Analysis, Logistic Regression, Naive Bayes, Support Vector Machines, Nearest Neighbors, and Ensemble Classifiers).Results and DiscussionWe achieved 100% accuracies among these 3,528 runs (7 classifiers x 4 feature sets x 6 mother wavelets x 21 flickering frequency pairs) using the mother wavelet function of Haar and the Ensemble Learner classifier. The highest classifier performances are 100% when two commands have the flickering frequency pairs of (6.0 and 10 Hz), (6.5 and 8.2 Hz), or (6.5 and 10.0 Hz).ConclusionWe obtained three main outcomes from this study. First, the most representative mother wavelet function was Haar, while the worst one was Symlet 4. Second, the Ensemble Learner classifier gave the maximum classifier performance in a two-command SSVEP-based BCI system. Besides, two user commands from SSVEP should be one of the frequency pairs of (6.0 and 10.0 Hz), (6.5 and 8.2 Hz), and (6.5 and 10.0 Hz) to achieve the maximum accuracy.     

Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate

Photoplethysmographic signals obtained from a webcam are analyzed through a continuous wavelet transform to assess the instantaneous heart rate. The measurements are performed on human faces. Robust image and signal processing are introduced to overcome drawbacks induced by light and motion artifacts. In addition, the respiration signal is recovered using the heart rate series by respiratory sinus arrhythmia, the natural variation in heart rate driven by the respiration. The presented algorithms are implemented on a mid-range computer and the overall method works in real-time. The performance of the proposed heart and breathing rates assessment method was evaluated using approved contact probes on a set of 12 healthy subjects. Results show high degrees of correlation between physiological measurements even in the presence of motion. This paper provides a motion-tolerant method that remotely measures the instantaneous heart and breathing rates. These parameters are particularly used in telemedicine and affective computing, where the heart rate variability analysis can provide an index of the autonomic nervous system.     

A novel peak detection approach with chemical noise removal using short‐time FFT for prOTOF MS data

Peak detection is a pivotal first step in biomarker discovery from MS data and can significantly influence the results of downstream data analysis steps. We developed a novel automatic peak detection method for prOTOF MS data, which does not require a priori knowledge of protein masses. Random noise is removed by an undecimated wavelet transform and chemical noise is attenuated by an adaptive short‐time discrete Fourier transform. Isotopic peaks corresponding to a single protein are combined by extracting an envelope over them. Depending on the S/N, the desired peaks in each individual spectrum are detected and those with the highest intensity among their peak clusters are recorded. The common peaks among all the spectra are identified by choosing an appropriate cut‐off threshold in the complete linkage hierarchical clustering. To remove the 1 Da shifting of the peaks, the peak corresponding to the same protein is determined as the detected peak with the largest number among its neighborhood. We validated this method using a data set of serial peptide and protein calibration standards. Compared with MoverZ program, our new method detects more peaks and significantly enhances S/N of the peak after the chemical noise removal. We then successfully applied this method to a data set from prOTOF MS spectra of albumin and albumin‐bound proteins from serum samples of 59 patients with carotid artery disease compared to vascular disease‐free patients to detect peaks with S/N≥2. Our method is easily implemented and is highly effective to define peaks that will be used for disease classification or to highlight potential biomarkers.     

Application of wavelet analysis to ecological data

Ecological data are difficult to analyze due to complexity residing in the ecological systems with the variables varying in non-linear fashion. Efficient methods are required to properly extract information out of the complex data. Wavelets have good time–frequency (time-scale) localization, can represent data parsimoniously, and can be implemented with very fast algorithms. Brief backgrounds and computational aspects of wavelets were outlined for implementation to ecological data analysis. Wavelets are well suited for building mathematical models of ecological data and the statistical analysis of combined effects of complex factors in ecological network. Wavelet based analysis and synthesis may lead researchers in ecological studies to new insights and novel theories for understanding complex ecological and environmental phenomena.     

Energy analysis reveals the negative effect of delays in passive movement mirror therapy

Wavelet transform energy analyses of the mean and standard error of the electromyogram (EMG) and electroencephalogram (EEG) of eight subjects were investigated in passive movement mirror therapies with no delay (in-phase) and with delay (out-of-phase) situations in two frequency bands of 7.81–15.62 and 15.62–31.25?Hz. It was found that the energy levels of EEG at electrode C4 in the in-phase situation were lower than those in out-of-phase situations, while the energy levels of flexor and extensor forearm muscle groups were larger. With two exceptions, this pattern could be seen in all other subjects. The difference between the in-phase (D0) and out-of-phase situations (D025 and D05) for the frequency range of 15.62–31.25?Hz was found to be significant at a significance level of 0.05 (paired t-test analysis). The respective elevation and decline of EEG and EGM with regard to the increase of the delay may indicate the necessity for synchronization of passive movement and mirror therapy.     

Social synchronization of circadian rhythmicity in female mice depends on the number of cohabiting animals

Communal animals often engage in group activities that require temporal synchrony among its members, including synchrony on the circadian timescale. The principles and conditions that foster such collective synchronization are not understood, but existing literature hints that the number of interacting individuals may be a critical factor. We tested this by recording individual circadian body temperature rhythms of female house mice housed singly, in twos (pairs), or in groups of five (quintets) in constant darkness; determining the daily phases of the circadian peak for each animal; and then calculating the cycle-to-cycle phase relationship between cohabiting animals over time. Significant temporal coherence was observed in quintets: the proportion of quintets (4/7), but not pairs (2/8), that became synchronized was greater than could be achieved by the complete simulated reassortment of all individuals. We speculate that the social coupling of individual circadian clocks of group members may be adaptive under certain conditions, and we propose that optimal group sizes in nature may depend not only on species-specific energetics, spatial behaviour and natural history but also on the mathematics of synchronizing assemblies of weakly coupled animal oscillators.     

一种新型心电监护治疗系统的研究与实现

介绍了一种新型的心电监护治疗系统,可有效的用于心血管疾病的预防、诊断、监护和治疗。采用小波变换的方法检测和处理心电信号。本文在介绍系统的硬件组成、软件设计的基础上,提出一种基于32位WINDOWS环境、采用多线程技术实现串行通信的新方法,有效地解决了传统多机监护系统串行通信中的迟滞性和不可靠性。     

Race‐specific differences in the phase coherence between blood flow and oxygenation: A simultaneous NIRS,white light spectroscopy and LDF study

Race‐specific differences in the level of glycated hemoglobin are well known. However, these differences were detected by invasive measurement of mean oxygenation, and their understanding remains far from complete. Given that oxygen is delivered to the cells by hemoglobin through the cardiovascular system, a possible approach is to investigate the phase coherence between blood flow and oxygen transportation. Here we introduce a noninvasive optical method based on simultaneous recordings using NIRS, white light spectroscopy and LDF, combined with wavelet‐based phase coherence analysis. Signals were recorded simultaneously for individuals in two groups of healthy subjects, 16 from Sub‐Saharan Africa (BA group) and 16 Europeans (CA group). It was found that the power of myogenic oscillations in oxygenated and de‐oxygenated hemoglobin is higher in the BA group, but that the phase coherence between blood flow and oxygen saturation, or blood flow and hemoglobin concentrations is higher in the CA group     

Quantitative measurement of blood glucose influenced by multiple factors via photoacoustic technique combined with optimized wavelet neural networks

In this work, the photoacoustic (PA) quantitative measurement of blood glucose concentration (BGC) influenced by multiple factors was firstly investigated. A set of PA detection system of blood glucose considering the comprehensive influence of five factors was established. The PA signals and peak-to-peak values (PPVs) of 625 rabbit whole blood were obtained under 625 influence combinations. Due to the accurate measurement of BGC limited by the overlap PA signals, wavelet neural network (WNN) was utilized to train the PPVs of blood glucose for 500 rabbit blood. The mean square error (MSE) of BGC for 125 testing blood was approximately 6.5782 mmol/L. To decrease the MSE, the parameters of WNN were optimized by particle swarm optimization (PSO), that is, PSO-WNN algorithm was employed. Under the optimal parameters, MSE of BGC was decreased to approximately 0.48005 mmol/L. To further improve the prediction accuracy of BGC, an improved nonlinear dynamic inertia weight (NDIW) strategy of PSO was proposed, and compared with other two kinds of dynamic inertia weight strategies. Under the optimal parameters, the MSE of BGC was decreased to approximately 0.2635 mmol/L. The comparison of nine algorithms demonstrate that the PA technique combined with PSO-WNN and the improved NDIW strategy is significant in the quantitative measurement of blood glucose influenced by multiple factors.