The mu rhythm is an electroencephalogram (EEG) signal located at the central region of the brain that is frequently used for studies concerning motor activity. Quite often, the EEG data are contaminated with artifacts and the application of blind source separation (BSS) alone is insufficient to extract the mu rhythm component. We present a new two-stage approach to extract the mu rhythm component. The first stage uses second-order blind identification (SOBI) with stationary wavelet transform (SWT) to automatically remove the artifacts. In the second stage, SOBI is applied again to find the mu rhythm component. Our method is first compared with independent component analysis with discrete wavelet transform (ICA-DWT) as well as SOBI-DWT, ICA-SWT, and regression method for artifact removal using simulated EEG data. The results showed that the regression method is more effective in removing electrooculogram (EOG) artifacts, while SOBI-SWT is more effective in removing electromyogram (EMG) artifacts as compared to the other artifact removal methods. Then, all the methods are compared with the direct application of SOBI in extracting mu rhythm components on simulated and actual EEG data from ten subjects. The results showed that the proposed method of SOBI-SWT artifact removal enhances the extraction of the mu rhythm component.
In this letter, neural networks (NNs) classify alcoholics and nonalcoholics using features extracted from visual evoked potential (VEP). A genetic algorithm (GA) is used to select the minimum number of channels that maximize classification performance. GA population fitness is evaluated using fuzzy ARTMAP (FA) NN, instead of the widely used multilayer perceptron (MLP). MLP, despite its effective classification, requires long training time (on the order of 10(3) times compared to FA). This causes it to be unsuitable to be used with GA, especially for on-line training. It is shown empirically that the optimal channel configuration selected by the proposed method is unbiased, i.e., it is optimal not only for FA but also for MLP classification. Therefore, it is proposed that for future experiments, these optimal channels could be considered for applications that involve classification of alcoholics.
Genetic algorithm is used to perform variable selection to determine the ranges of wavelengths in NIR spectral data suitable to be used as predictors in multivariate calibration model via partial least squares. The NIR spectral data consists of three components of active substances, namely human serum albumin (HSA), γ-globulin and glucose. The wavelength selection is able to improve the calibration model by selecting the wavelengths that contains information or correlated with the concentration of substances, while others non-chosen wavelengths, which contribute no information or contain noises, are excluded from the calibration model.
This paper shows how dynamic heart rate measurements that are typically obtained from sensors mounted near to the heart can also be obtained from video sequences. In this study, two experiments are carried out where a video camera captures the facial images of the seven subjects. The first experiment involves the measurement of subjects' increasing heart rates (79 to 150 beats per minute (BPM)) while cycling whereas the second involves falling heart beats (153 to 88 BPM). In this study, independent component analysis (ICA) is combined with mutual information to ensure accuracy is not compromised in the use of short video duration. While both experiments are going on measures of heartbeat using the Polar heart rate monitor is also taken to compare with the findings of the proposed method. Overall experimental results show the proposed method can be used to measure dynamic heart rates where the root mean square error (RMSE) and the correlation coefficient are 1.88 BPM and 0.99 respectively.
In this paper, facial images from various video sequences are used to obtain a heart rate reading. In this study, a video camera is used to capture the facial images of eight subjects whose heart rates vary dynamically, between 81 and 153 BPM. Principal component analysis (PCA) is used to recover the blood volume pulses (BVP) which can be used for the heart rate estimation. An important consideration for accuracy of the dynamic heart rate estimation is to determine the shortest video duration that realizes it. This video duration is chosen when the six principal components (PC) are least correlated amongst them. When this is achieved, the first PC is used to obtain the heart rate. The results obtained from the proposed method are compared to the readings obtained from the Polar heart rate monitor. Experimental results show the proposed method is able to estimate the dynamic heart rate readings using less computational requirements when compared to the existing method. The mean absolute error and the standard deviation of the absolute errors between experimental readings and actual readings are 2.18 BPM and 1.71 BPM respectively.