"Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a braincomputer interface: three-class classification of rest, right-, and left-hand motor execution," Neurophoton. 5(1), 011008 (2017), doi: 10.1117/1.NPh.5.1.011008. Abstract. The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

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Segmentation of ultrasound images is an essential task in both diagnosis and image-guided interventions given the ease-of-use and low cost of this imaging modality. As manual segmentation is tedious and time consuming, a growing body of research has focused on the development of automatic segmentation algorithms. Deep learning algorithms have shown remarkable achievements in this regard; however, they need large training datasets. Unfortunately, preparing large labeled datasets in ultrasound images is prohibitively difficult. Therefore, in this study, we propose the use of simulated ultrasound (US) images for training the U-Net deep learning segmentation architecture and test on tissue-mimicking phantom data collected by an ultrasound machine. We demonstrate that the trained architecture on the simulated data is transferrable to real data, and therefore, simulated data can be considered as an alternative training dataset when real datasets are not available. The second contribution of this paper is that we train our U-Net network on envelope and B-mode images of the simulated dataset, and test the trained network on real envelope and Bmode images of phantom, respectively. We show that test results are superior for the envelope data compared to B-mode image.

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Background Among the paraspinal muscles, the structure and function of the lumbar multifidus (LM) has become of great interest to researchers and clinicians involved in lower back pain and muscle rehabilitation. Ultrasound (US) imaging of the LM muscle is a useful clinical tool which can be used in the assessment of muscle morphology and function. US is widely used due to its portability, cost-effectiveness, and ease-of-use. In order to assess muscle function, quantitative information of the LM must be extracted from the US image by means of manual segmentation. However, manual segmentation requires a higher level of training and experience and is characterized by a level of difficulty and subjectivity associated with image interpretation. Thus, the development of automated segmentation methods is warranted and would strongly benefit clinicians and researchers. The aim of this study is to provide a database which will contribute to the development of automated segmentation algorithms of the LM. Construction and content This database provides the US ground truth of the left and right LM muscles at the L5 level (in prone and standing positions) of 109 young athletic adults involved in Concordia University’s varsity teams. The LUMINOUS database contains the US images with their corresponding manually segmented binary masks, serving as the ground truth. The purpose of the database is to enable development and validation of deep learning algorithms used for automatic segmentation tasks related to the assessment of the LM cross-sectional area (CSA) and echo intensity (EI). The LUMINOUS database is publicly available at http://data.sonography.ai. Conclusion The development of automated segmentation algorithms based on this database will promote the standardization of LM measurements and facilitate comparison among studies. Moreover, it can accelerate the clinical implementation of quantitative muscle assessment in clinical and research settings.

Functional connectivity derived from resting-state functional near infrared spectroscopy has gained attention of recent scholars because of its capability in providing valuable insight into intrinsic networks and various neurological disorders in a human brain. Several progressive methodologies in detecting resting-state functional connectivity patterns in functional near infrared spectroscopy, such as seed-based correlation analysis and independent component analysis as the most widely used methods, were adopted in previous studies. Although these two methods provide complementary information each other, the conventional seed-based method shows degraded performance compared to the independent component analysis-based scheme in terms of the sensitivity and specificity. In this study, artificial neural network and convolutional neural network were utilized in order to overcome the performance degradation of the conventional seed-based method. First of all, the results of artificial neural network-and convolutional neural network-based method illustrated the superior performance in terms of specificity and sensitivity compared to both conventional approaches. Second, artificial neural network, convolutional neural network, and independent component analysis methods showed more robustness compared to seed-based method. Moreover, resting-state functional connectivity patterns derived from artificial neural network-and convolutional neural network-based methods in sensorimotor and motor areas were consistent with the previous findings. The main contribution of the present work is to emphasize that artificial neural network as well as convolutional neural network can be exploited for a high-performance seed-based method to estimate the temporal relation among brain networks during resting state.

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