Investigation.of human visual cortex responses to flickering light using functional near infrared spectroscopy and constrained ICA

The human visual sensitivity to the flickering light has been under investigation for decades.The finding of research in this area can contribute to the understanding of human visual system mechanism and visual disorders,and establishing diagnosis and treatment of diseases.The aim of this study is to investigate the ffects of the flickering light to the visual cortex by monitoring the hemodynamic responses of the brain with the functional near infrared spectrosoopy(ENIRS)method.Since the acquired fNIRS signals are afected by physiological factors and measurement artifacts,constrained independent component analysis(eICA)was applied to extract the actual fNIRS responses from the obtained data.The experimental results revealed significant changes(p<0.0001)of the hemodynamic responses of the visual cortex.from the baseline when the flickering stimulation was activated.With the uses of cICA,the contrast to noise ratio(CNR),reflecting the contrast of hemodynamic concentration between rest and task,became larger.This indicated the improvement of the NIRS signals when the noise was eliminated.In subsequent studies,statistical analysis was used to infer the correlation between the NIRS signals and the visual stimulus.We found that there was a slight decrease of the oxygenated hemoglobin con-centration(about 5.69%)over four frequencies when the modulation increased.However,the variations of oxy and deoxy-hemoglobin were not statistically significant.

A Machine Learning Approach for Artifact Removal from Brain Signal

Electroencephalography(EEG),helps to analyze the neuronal activity of a human brain in the form of electrical signals with high temporal resolution in the millisecond range.To extract clean clinical information from EEG signals,it is essential to remove unwanted artifacts that are due to different causes including at the time of acquisition.In this piece of work,the authors considered the EEG signal contaminated with Electrocardiogram(ECG)artifacts that occurs mostly in cardiac patients.The clean EEG is taken from the openly available Mendeley database whereas the ECG signal is collected from the Physionet database to create artifacts in the EEG signal and verify the proposed algorithm.Being the artifactual signal is non-linear and non-stationary the Random Vector Functional Link Network(RVFLN)model is used in this case.The Machine Learning approach has taken a leading role in every field of current research and RVFLN is one of them.For the proof of adaptive nature,the model is designed with EEG as a reference and artifactual EEG as input.The peaks of ECG signals are evaluated for artifact estimation as the amplitude is higher than the EEG signal.To vary the weight and reduce the error,an exponentially weighted Recursive Least Square(RLS)algorithm is used to design the adaptive filter with the novel RVFLN model.The random vectors are considered in this model with a radial basis function to satisfy the required signal experimentation.It is found that the result is excellent in terms of Mean Square Error(MSE),Normalized Mean Square Error(NMSE),Relative Error(RE),Gain in Signal to Artifact Ratio(GSAR),Signal Noise Ratio(SNR),Information Quantity(IQ),and Improvement in Normalized Power Spectrum(INPS).Also,the proposed method is compared with the earlier methods to show its efficacy.

Improved CICA Algorithm Used for Single Channel Compound Fault Diagnosis of Rolling Bearings

A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to realize single channel compound fault diagnosis of bearings and improve the diagnosis accuracy, an improved CICA algorithm named constrained independent component analysis based on the energy method （E-CICA） is proposed. With the approach, the single channel vibration signal is firstly decomposed into several wavelet coefficients by discrete wavelet transform（DWT） method for the purpose of obtaining multichannel signals. Then the envelope signals of the reconstructed wavelet coefficients are selected as the input of E-CICA algorithm, which fulfills the requirements that the number of sensors is greater than or equal to that of the source signals and makes it more suitable to be processed by CICA strategy. The frequency energy ratio（ER） of each wavelet reconstructed signal to the total energy of the given synchronous signal is calculated, and then the synchronous signal with maximum ER value is set as the reference signal accordingly. By this way, the reference signal contains a priori knowledge of fault source signal and the influence on fault signal extraction accuracy which is caused by the initial phase angle and the duty ratio of the reference signal in the traditional CICA algorithm is avoided. Experimental results show that E-CICA algorithm can effectively separate out the outer-race defect and the rollers defect from the single channel compound fault and fulfill the needs of compound fault diagnosis of rolling bearings, and the running time is 0.12% of that of the traditional CICA algorithm and the extraction accuracy is 1.4 times of that of CICA as well. The proposed research provides a new method to separate single channel compound fault signals.