EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints.

One of the most important needs in neuroimaging is brain dynamic source imaging with high spatial and temporal resolution. EEG source imaging estimates the underlying sources from EEG recordings, which provides enhanced spatial resolution with intrinsically high temporal resolution. To ensure identifiability in the underdetermined source reconstruction problem, constraints on EEG sources are essential. This paper introduces a novel method for estimating source activities based on spatio-temporal constraints and a dynamic source imaging algorithm. The method enhances time resolution by incorporating temporal evolution of neural activity into a regularization function. Additionally, two spatial regularization constraints based on L 1 and L 2 norms are applied in the transformed domain to address both focal and spread neural activities, achieved through spatial gradient and Laplacian transform. Performance evaluation, conducted quantitatively using synthetic datasets, discusses the influence of parameters such as source extent, number of sources, correlation level, and SNR level on temporal and spatial metrics. Results demonstrate that the proposed method provides superior spatial and temporal reconstructions compared to state-of-the-art inverse solutions including STRAPS, sLORETA, SBL, dSPM, and MxNE. This improvement is attributed to the simultaneous integration of transformed spatial and temporal constraints. When applied to a real auditory ERP dataset, our algorithm accurately reconstructs brain source time series and locations, effectively identifying the origins of auditory evoked potentials. In conclusion, our proposed method with spatio-temporal constraints outperforms the state-of-the-art algorithms in estimating source distribution and time courses.

VLCIoT: design and implementation of a visible light communication system for indoor Internet of Things applications.

In this paper, a visible light communication (VLC) system for indoor Internet of Things (IoT) applications, called VLCIoT, is proposed. The proposed system is based on type I of the IEEE 802.15.7 standard physical (PHY) layer. The PHY I is provided for low data rate applications from 10 to 100 kb/s, which looks suitable for the typical IoT applications. The on-off keying suggested modulation scheme by the PHY I that is simple and requires low-cost hardware for implementation is considered. The implemented VLCIoT system is robust against indoor ambient light interference. Using the frequency division multiple access, several VLC networks can operate at different frequencies in the vicinity of each other without interference. The data rate of VLCIoT is up to 115.2 kb/s, and the bit error ratio of the system is very low. This system is designed for indoor, which for this purpose operates well up to 7 m distances. In this paper, a figure of merit (FoM) is proposed, in which the most important parameters for IoT applications are considered. A comprehensive comparison of VLCIoT to other suitable VLC systems for IoT applications is performed. The results show that the VLCIoT achieves the best FoM and is suitable for indoor IoT applications.

Epileptic source connectivity analysis based on estimating of dynamic time series of regions of interest.

We propose a new source connectivity method by focusing on estimating time courses of the regions of interest (ROIs). To this aim, it is necessary to consider the strong inherent non-stationary behavior of neural activity. We develop an iterative dynamic approach to extract a single time course for each ROI encoding the temporal non-stationary features. The proposed approach explicitly includes dynamic constraints by taking into account the evolution of the sources activities for further dynamic connectivity analysis. We simulated an epileptic network with a non-stationary structure; accordingly, EEG source reconstruction using LORETA is performed. Using the reconstructed sources, the spatially compact ROIs are selected. Then, a single time course encoding the temporal non-stationarity is extracted for each ROI. An adaptive directed transfer function (ADTF) is applied to measure the information flow of underlying brain networks. Obtained results demonstrate that the contributed approach is more efficient to estimate the ROI time series and ROI to ROI information flow in comparison with existing methods. Our work is validated in three drug-resistance epilepsy patients. The proposed ROI time series estimation directly affects the quality of connectivity analysis, leading to the best possible seizure onset zone (SOZ) localization verified by electrocorticography and post-operational results.