EEG-fTCD hybrid brain-computer interface using template matching and wavelet decomposition

Smart Healthcare System Based on Cloud-Internet of Things and Deep Learning

Introduction

Health monitoring and remote diagnosis can be realized through Smart Healthcare. In view of the existing problems such as simple measurement parameters of wearable devices, huge computing pressure of cloud servers, and lack of individualization of diagnosis, a novel Cloud-Internet of Things (C-IOT) framework for medical monitoring is put forward.

Methods

Smart phones are adopted as gateway devices to achieve data standardization and preprocess to generate health gray-scale map uploaded to the cloud server. The cloud server realizes the business logic processing and uses the deep learning model to carry out the gray-scale map calculation of health parameters. A deep learning model based on the convolution neural network (CNN) is constructed, in which six volunteers are selected to participate in the experiment, and their health data are marked by private doctors to generate initial data set.

Results

Experimental results show the feasibility of the proposed framework. The test data set is used to test the CNN model after training; the forecast accuracy is over 77.6%.

Conclusion

The CNN model performs well in the recognition of health status. Collectively, this Smart Healthcare System is expected to assist doctors by improving the diagnosis of health status in clinical practice.

Nanomaterials-patterned flexible electrodes for wearable health monitoring: a review

ABSTRACT

Electrodes fabricated on a flexible substrate are a revolutionary development in wearable health monitoring due to their lightweight, breathability, comfort, and flexibility to conform to the curvilinear body shape. Different metallic thin-film and plastic-based substrates lack comfort for long-term monitoring applications. However, the insulating nature of different polymer, fiber, and textile substrates requires the deposition of conductive materials to render interactive functionality to substrates. Besides, the high porosity and flexibility of fiber and textile substrates pose a great challenge for the homogenous deposition of active materials. Printing is an excellent process to produce a flexible conductive textile electrode for wearable health monitoring applications due to its low cost and scalability. This article critically reviews the current state of the art of different textile architectures as a substrate for the deposition of conductive nanomaterials. Furthermore, recent progress in various printing processes of nanomaterials, challenges of printing nanomaterials on textiles, and their health monitoring applications are described systematically.

A probabilistic approach for calibration time reduction in hybrid EEG-fTCD brain-computer interfaces

Background

Generally, brain–computer interfaces (BCIs) require calibration before usage to ensure efficient performance. Therefore, each BCI user has to attend a certain number of calibration sessions to be able to use the system. However, such calibration requirements may be difficult to fulfill especially for patients with disabilities. In this paper, we introduce a probabilistic transfer learning approach to reduce the calibration requirements of our EEG–fTCD hybrid BCI designed using motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. The proposed approach identifies the top similar datasets from previous BCI users to a small training dataset collected from a current BCI user and uses these datasets to augment the training data of the current BCI user. To achieve such an aim, EEG and fTCD feature vectors of each trial were projected into scalar scores using support vector machines. EEG and fTCD class conditional distributions were learnt separately using the scores of each class. Bhattacharyya distance was used to identify similarities between class conditional distributions obtained using training trials of the current BCI user and those obtained using trials of previous users.

Results

Experimental results showed that the performance obtained using the proposed transfer learning approach outperforms the performance obtained without transfer learning for both MI and flickering MR/WG paradigms. In particular, it was found that the calibration requirements can be reduced by at least 60.43% for the MI paradigm, while at most a reduction of 17.31% can be achieved for the MR/WG paradigm.

Conclusions

Data collected using the MI paradigm show better generalization across subjects.