This article presents a small-area Bluetooth Low-Energy (BLE) transceiver (TRX) for short-range Internet-of-Things (IoT) applications in 65-nm CMOS. An integrated Radio-Frequency Input-Output (RFIO) circuitry embedded with transmitter/receiver (TX/RX) switch function and on-chip impedance matching is proposed. A hybrid-loop TRX structure based on a wide-bandwidth fractional-N digital phase-locked loop (DPLL) is implemented to achieve the maximum power reduction. A -94dBm receiver sensitivity is achieved with 2.3mW receiver power consumption, while a RF receiving bypass route integration enhances the input power tolerance. The BLE transceiver delivers -6dBm output power while consuming 2.6mW and delivers 0dBm output power while achieving 18.5% maximum TX efficiency. Thanks to the RFIO with harmonic suppression, -56dBc of 2nd-order harmonic distortion (HD2) and -48dBc of 3rd-order harmonic distortion (HD3) suppression are achieved with 0.85mm2on-chip area. This transceiver satisfied the BLE radio specification without the need for external filters and with low-power consumption, which enables minimum size and long life-time modules.

To understand how correlated the human brain is across multiple subjects or multiple trials when people make risky decisions, economical decision-making based on the Newsvendor Problems (NP) were investigated with 64-channel electro-encephalography (EEG) taken from 23 human subjects. Specifically, in this work, we analyzed the EEG signals recorded while subjects were playing NP decision-making tasks. Two groups of subjects were assigned to take NP with distinctive difficulty levels for examining the difference in neural responses to the task challenge. The Correlated Components Analysis (CorrCA) method was then utilized to identify the EEG channels that maximized the correlations among multiple subjects or trials. Both Inter-Subject Correlation (ISC) and Inter-Trial Correlation (ITC) results revealed an apparent modulation of the attentional state of subjects across three test phases (decision-making, feedback, and recovery) and two distinct task challenges. The neural correlation across multiple subjects or multiple trials increased significantly during the decision-making phase for more challenging tasks, in which subjects needed to be more attentive. Such observations confirm the modulation of the engagement state on the correlations of the neural activity across multiple subjects or repeated trials. Moreover, the alpha-band power results for different decision-making phases affirmed that the difficulty of different test phases modulates subjects' engagement.

Transcranial infrared laser stimulation (TILS) is a promising noninvasive intervention for neurological diseases. Though some experimental work has been done to understand the mechanism of TILS, the reported statistical analysis of data is quite simple and could not provide a comprehensive picture on the effect of TILS. This study learns the effect of TILS on hemodynamics of the human brain from experimental data using longitudinal data analysis methods. Specifically, repeated measures analysis of variance (ANOVA) is first applied to confirm the significance of the TILS effect and its characteristics. Based on that, two parametric mixed-effect models and non-parametric functional mixed-effect model are proposed to model the population-level performance and individual variation of this effect. Interpretations on the fitted models are provided, and comparison of the three proposed models in terms of fitting and prediction performance is made to select the best model. According to the selected model, TILS increases the concentration of oxygenated hemoglobin in the brain and this effect sustains even after the treatment stops. Also, there is considerable variation among individual responses to TILS.

Electroencephalography (EEG) source analysis is one of the most important noninvasive human brain imaging tools that provides millisecond temporal accuracy. However, discovering essential activated brain sources associated with different brain status is still a challenging problem. In this study, we propose for the first time that the ill-posed EEG inverse problem can be formulated and solved as a sparse over-complete dictionary learning problem. In particular, a novel supervised sparse dictionary learning framework was developed for EEG source reconstruction. A revised version of discriminative K-SVD (DK-SVD) algorithm is exploited to solve the formulated supervised dictionary learning problem. As the proposed learning framework incorporated the EEG label information of different brain status, it is capable of learning a sparse representation that reveal the most discriminative brain activity sources among different brain states. Compared to the state-of-the-art EEG source analysis methods, proposed sparse dictionary learning framework achieved significant superior performance in both computing speed and accuracy for the challenging EEG source reconstruction problem through extensive numerical experiments. More importantly, the experimental results also validated that the proposed sparse learning framework is effective to discover the discriminative task-related brain activation sources, which shows the potential to advance the high resolution EEG source analysis for real-time non-invasive brain imaging research.

Causality analysis of simultaneous measurements of the brain's electrical activity and its hemodynamic activity provides the opportunity to study the neural underpinning of hemodynamic fluctuations. This multimodal analysis can also be used to extract valuable information regarding the location of the generators of various electrical events such as Alpha rhythms or epileptiform activity. To best of our knowledge, we are the first propose a method to assess causality from EEG to the hemodynamic activity measured using functional near-infrared spectroscopy (fNIRs). The main challenge in studying causality within this setting arises from the low sampling rate of the fNIRs and the mixed frequency nature of the data. Our method of analysis consists of two parts. Through a simple modification of Geweke's formulation of contamination, we first show that the low sampling frequency of the fNIRs does not cause contamination in estimating causality from EEG to fNIRs. We then apply a novel causality test to avoid the down-sampling of the EEG when measuring for causality. The method of analysis proposed here can be generalized to study causality in other biomedical signal analysis applications and mixed frequency settings.

Diffuse optical tomography (DOT) is a noninvasive technique which measures hemodynamic changes in the tissue with near infrared light, which has been increasingly used to study brain functions. Due to the nature of light propagation in the tissue, the reconstruction problem is severely ill-posed. For linearized DOT problems, sparsity regularization has achieved promising results over conventional Tikhonov regularization in recent experimental research. As extensions to standard sparsity, it is widely known that structured sparsity based methods are often superior in terms of reconstruction accuracy, when the data follows some structures. In this paper, we exploit the structured sparsity of diffuse optical images. Based on the functional specialization of the brain, it is observed that the in vivo absorption changes caused by a specific brain function would be clustered in certain region(s) and not randomly distributed. Thus, a new algorithm is proposed for this clustered sparsity reconstruction (CSR). Results of numerical simulations and phantom experiments have demonstrated the superiority of the proposed method over the state-of-the-art methods. An example from human in vivo measurements further confirmed the advantages of the proposed CSR method.

This paper presents our recent development of a classification algorithm for identification of breast cancer margins measured by hyperspectral imaging for the purpose of lowering the number of missed positive margins in breast cancer lumpectomy. After extracting Fourier coefficient selection features and reducing the dimensionality of hyperspectral image data via the Minimum Redundancy Maximum Relevance method, an SVM classifier involving a radial basis kernel function is deployed to separate cancerous tissues from normal tissues. By examining exvivo breast cancer hyperspectral images tagged by a pathologist, the developed classification approach is shown to achieve a sensitivity of about 98% and a specificity of about 99%.

Reflectance diffuse optical tomography (rDOT) of brain function is limited by its high sensitivity to the superficial tissues (i.e., the scalp and skull) and by its severe decrease in measurement sensitivity with increased depth. Significant interference in rDOT results from spontaneous fluctuations that are embedded in both the superficial tissues and brain, such as arterial pulsation and vasomotion. In this study, first we investigate coherence and phase shift of the spontaneous fluctuations in the resting state, within the superficial tissues and at various depths of the brain, respectively. We demonstrate that the spontaneous fluctuations originating from arterial pulsations ( ∼ 1 Hz) are spatially global and temporally coherent, while the fluctuations originating from vasomotion ( ∼ 0.1 Hz) tend to have less coherence with increased depth. Second, adaptive cancellation of spontaneous fluctuations with a frequency-specific strategy is utilized and validated in both resting and activation (evoked by a finger-tapping task) states. Third, improved depth localization of motor activation in reconstructed rDOT images is achieved by combining adaptive cancellation with a depth compensation algorithm that we recently reported.