Brain activities commonly recorded using the electroencephalogram (EEG) are contaminated with ocular artifacts. These activities can be suppressed using a robust independent component analysis (ICA) tool, but its efficiency relies on manual intervention to accurately identify the independent artifactual components. In this paper, we present a new unsupervised, robust, and computationally fast statistical algorithm that uses modified multiscale sample entropy (mMSE) and Kurtosis to automatically identify the independent eye blink artifactual components, and subsequently denoise these components using biorthogonal wavelet decomposition. A 95% two-sided confidence interval of the mean is used to determine the threshold for Kurtosis and mMSE to identify the blink related components in the ICA decomposed data. The algorithm preserves the persistent neural activity in the independent components and removes only the artifactual activity. Results have shown improved performance in the reconstructed EEG signals using the proposed unsupervised algorithm in terms of mutual information, correlation coefficient, and spectral coherence in comparison with conventional zeroing-ICA and wavelet enhanced ICA artifact removal techniques. The algorithm achieves an average sensitivity of 90% and an average specificity of 98%, with average execution time for the datasets ( N = 7) of 0.06 s ( SD = 0.021) compared to the conventional wICA requiring 0.1078 s ( SD = 0.004). The proposed algorithm neither requires manual identification for artifactual components nor additional electrooculographic channel. The algorithm was tested for 12 channels, but might be useful for dense EEG systems.

Circulating tumor cells (CTCs) have substantial clinical implications in cancer diagnosis and monitoring. Although significant progress has been made in developing technologies for CTC detection and counting, the ability to quantitatively detect multiple surface protein markers on individual tumor cells remains very limited. In this work, we report a multiplexed method that uses magnetic multicolor surface-enhanced Raman scattering (SERS) nanotags in conjunction with a chip-based immunomagnetic separation to quantitatively and simultaneously detect four surface protein markers on individual tumor cells in whole blood. Four-color SERS nanotags were prepared using magnetic-optical iron oxide−gold core−shell nanoparticles with different Raman reporters to recognize four different cancer markers with respective antibodies. A microfluidic device was fabricated to magnetically capture the nanoparticle-bound tumor cells and to perform online negative staining and single-cell optical detection. The level of each targeted protein was obtained by signal deconvolution of the mixed SERS signals from individual tumor cells using the classic least squares regression method. The method was tested with spiked tumor cells in human whole blood with three different breast cancer cell lines and compared with the results of purified cancer cells suspended in a phosphate buffer solution. The method, with either spiked cancer cells in blood or purified cancer cells, showed a strong correlation with purified cancer cells by enzyme-linked immunosorbent assay, suggesting the potential of our method for the reliable detection of multiple surface markers on CTCs. Combining immunomagnetic enrichment with high specificity, multiplexed targeting for the capture of CTC subpopulations, multicolor SERS detection with high sensitivity and specificity, microfluidics for handling rare cells and magnetic−plasmonic nanoparticles for dual enrichment and detection, our method provides an integrated, yet a simple and an efficient platform that has the potential to more sensitively detect and monitor cancer metastasis.

Mild cognitive impairment (MCI) is the preliminary stage of dementia, which may lead to Alzheimer's disease (AD) in the elderly people. Therefore, early detection of MCI has the potential to minimize the risk of AD by ensuring the proper mental health care before it is too late. In this paper, we demonstrate a single-channel EEG-based MCI detection method, which is costeffective and portable, and thus suitable for regular home-based patient monitoring. We collected the scalp EEG data from 23 subjects, while they were stimulated with five auditory speech signals. The cognitive state of the subjects was evaluated by the Montreal cognitive assessment test (MoCA). We extracted 590 features from the event-related potential (ERP) of the collected EEG signals, which included time and spectral domain characteristics of the response. The top 25 features, ranked by the random forest method, were used for classification models to identify subjects with MCI. Robustness of our model was tested using leave-one-out cross-validation while training the classifiers. Best results (leave-one-out cross-validation accuracy 87.9%, sensitivity 84.8%, specificity 95%, and F score 85%) were obtained using support vector machine (SVM) method with radial basis kernel (RBF) (sigma = 10/cost = 10 2). Similar performances were also observed with logistic regression (LR), further validating the results. Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI.

Electroencephalogram (EEG) is a technique for recording the asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. Artifacts, such as eye blink activities, can corrupt these neuronal signals. While ocular artifact (OA) removal is well investigated for multiple channel EEG systems, in alignment with the recent momentum toward minimalistic EEG systems for use in natural environments, we investigate unsupervised and effective removal of OA from single-channel streaming raw EEG data. In this paper, the unsupervised wavelet transform (WT) decomposition technique was systematically evaluated for the effectiveness of OA removal for a single-channel EEG system. A set of seven raw EEG data set was analyzed. Two commonly used WT methods, Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), were applied. Four WT basis functions, namely, haar, coif3, sym3, and bior4.4, were considered for OA removal with universal threshold and statistical threshold (ST). To quantify OA removal efficacy from single-channel EEG, five performance metrics were utilized: correlation coefficients, mutual information, signal-to-artifact ratio, normalized mean square error, and time-frequency analysis. The temporal and spectral analysis shows that the optimal combination could be DWT with ST with coif3 or bior4.4 to remove OA among 16 combinations. This paper demonstrates that the WT can be an effective tool for unsupervised OA removal from single-channel EEG data for real-time applications.

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