High-Precision and High-Resolution Synchrosqueezing Transform via Time-Frequency Instantaneous Phases

Synchrosqueezing transform (SST) can effectively improve time-frequency precision and resolution by squeezing time-frequency spectra via instantaneous frequencies, and it has been applied in many diverse disciplines; however, the precision of estimated instantaneous frequencies during SST is usually affected by the time-sample interval of the inputted signal; this usually leads to low-precision or inaccurate SST results and limits its further application. To obtain high-precision and high-resolution SST results with high efficiency, we propose a high-precision and high-resolution SST via time-frequency instantaneous phases (HSST); in HSST, time-frequency instantaneous phases with period-jumps removal are used for high-precision instantaneous frequencies estimation and SST. Two synthetic signal examples show that HSST can minimize the impact of the time-sample interval to achieve high-precision and high-resolution SST results with high efficiency. A real 3D seismic data application demonstrates that HSST has fantastic performance in time-frequency precision and resolution enhancement, and it can be widely used in digital signals processing and interpretation fields.

Developing Behavioral Markers of Social Function in Aging: A Methodology Using Pitch Mimicry

The Covid pandemic brought to the forefront the crucial role of social interactions for society at large and in gerontological practice. Social interactions play a paramount role in preserving cognitive reserve in older adults. They rely on neurobehavioral processes that are complex (engage large parts of the brain and demand integrity of multiple perceptuomotor, attentional, cognitive and memory functions). Pitch mimicry is a well-known and spontaneously arising social phenomenon that requires the integrity of numerous processes of the brain, and we hypothesize that it constitutes a potentially sensitive behavioral marker of neurodegeneration in Alzheimer’s Disease and Related Dementias (ADRD). We developed and validated a series of algorithms to parse verbal exchanges between people and quantify the level of mimicry that each exhibit with their partners. Those algorithms are based on silence thresholding, carefully parametrized CEPSTRAL algorithms for automatic pitch estimation and Synchrosqueezing Transform for validation. We introduce a theoretical model to compare our estimates of pitch mimicry with model’s expectations based on the null hypothesis that its neurobehavioral pathways retain their integrity. Our method will allow researchers to study the evolution of pitch mimicry in aging individuals and its sensitivity to diverse social contexts, including those preserving lasting social engagement. Our method will also allow us to test the hypothesis that Pitch Mimicry is a sensitive behavioral marker of dementia, a condition characterized by a breakdown in social relatedness.

Sinusoidal Noise Removal in PD Measurement Based on Synchrosqueezing Transform and Singular Spectrum Analysis

In electrical engineering, partial discharge (PD) measurement has been widely used for inspecting and judging insulation conditions of high voltage (HV) apparatus. However, on-site PD measurement easily becomes contaminated by noises. Particularly, sinusoidal noise makes it difficult to recognize real PD signal, thus leading to the misjudgment of insulation conditions. Therefore, sinusoidal noise removal is necessary. In this paper, instantaneous frequency (IF) is introduced, and the synchrosqueezing transform (SST) as well as singular spectrum analysis (SSA) is proposed for sinusoidal noise removal. A continuous analytic wavelet transform is firstly applied to the noisy PD signal and then the time frequency representation (TFR) is reassigned by SST. Narrow-band sinusoidal noise has fixed IF, while PD signal has much larger frequency range and time-varying IF. Due to the difference, the reassigned TFR enables the sinusoidal noise to be distinguished from PD signal. After synthesizing the signal with the recognized IF, SSA is further applied to signal refinement. At last, a numerical simulation is carried out to verify the effectiveness of the proposed method, and its robustness to white noise is also validated. After the implementation of the proposed method, wavelet thresholding can be further applied for white noise reduction.

Detection of Movement Intention in EEG-Based Brain-Computer Interfaces Using Fourier-Based Synchrosqueezing Transform

Patients with motor impairments need caregivers’ help to initiate the operation of brain-computer interfaces (BCI). This study aims to identify and characterize movement intention using multichannel electroencephalography (EEG) signals as a means to initiate BCI systems without extra accessories/methodologies. We propose to discriminate the resting and motor imagery (MI) states with high accuracy using Fourier-based synchrosqueezing transform (FSST) as a feature extractor. FSST has been investigated and compared with other popular approaches in 28 healthy subjects for a total of 6657 trials. The accuracy and f-measure values were obtained as 99.8% and 0.99, respectively, when FSST was used as the feature extractor and singular value decomposition (SVD) as the feature selection method and support vector machines as the classifier. Moreover, this study investigated the use of data that contain certain amount of noise without any preprocessing in addition to the clean counterparts. Furthermore, the statistical analysis of EEG channels with the best discrimination (of resting and MI states) characteristics demonstrated that F4-Fz-C3-Cz-C4-Pz channels and several statistical features had statistical significance levels, [Formula: see text], less than 0.05. This study showed that the preparation of the movement can be detected in real-time employing FSST-SVD combination and several channels with minimal pre-processing effort.