scholarly journals Feasibility of a Real-Time Clinical Augmented Reality and Artificial Intelligence Framework for Pain Detection and Localization From the Brain (Preprint)

2019 ◽  
Author(s):  
Xiao-Su Hu ◽  
Thiago D. Nascimento ◽  
Mary C Bender ◽  
Theodore Hall ◽  
Sean Petty ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Augmented Reality ◽  
Real Time ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Positive Likelihood Ratio ◽  
Cortical Activity ◽  
Functional Near Infrared Spectroscopy ◽  
Pain Detection ◽  
The Brain

BACKGROUND For many years, clinicians have been seeking for objective pain assessment solutions via neuroimaging techniques, focusing on the brain to detect human pain. Unfortunately, most of those techniques are not applicable in the clinical environment or lack accuracy. OBJECTIVE This study aimed to test the feasibility of a mobile neuroimaging-based clinical augmented reality (AR) and artificial intelligence (AI) framework, CLARAi, for objective pain detection and also localization direct from the patient’s brain in real time. METHODS Clinical dental pain was triggered in 21 patients by hypersensitive tooth stimulation with 20 consecutive descending cold stimulations (32°C-0°C). We used a portable optical neuroimaging technology, functional near-infrared spectroscopy, to gauge their cortical activity during evoked acute clinical pain. The data were decoded using a neural network (NN)–based AI algorithm to classify hemodynamic response data into pain and no-pain brain states in real time. We tested the performance of several networks (NN with 7 layers, 6 layers, 5 layers, 3 layers, recurrent NN, and long short-term memory network) upon reorganized data features on pain diction and localization in a simulated real-time environment. In addition, we also tested the feasibility of transmitting the neuroimaging data to an AR device, HoloLens, in the same simulated environment, allowing visualization of the ongoing cortical activity on a 3-dimensional brain template virtually plotted on the patients’ head during clinical consult. RESULTS The artificial neutral network (3-layer NN) achieved an optimal classification accuracy at 80.37% (126,000/156,680) for pain and no pain discrimination, with positive likelihood ratio (PLR) at 2.35. We further explored a 3-class localization task of left/right side pain and no-pain states, and convolutional NN-6 (6-layer NN) achieved highest classification accuracy at 74.23% (1040/1401) with PLR at 2.02. CONCLUSIONS Additional studies are needed to optimize and validate our prototype CLARAi framework for other pains and neurologic disorders. However, we presented an innovative and feasible neuroimaging-based AR/AI concept that can potentially transform the human brain into an objective target to visualize and precisely measure and localize pain in real time where it is most needed: in the doctor’s office. INTERNATIONAL REGISTERED REPOR RR1-10.2196/13594

scholarly journals Feasibility of a Real-Time Clinical Augmented Reality and Artificial Intelligence Framework for Pain Detection and Localization From the Brain

10.2196/13594 ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. e13594 ◽  
Author(s):  
Xiao-Su Hu ◽  
Thiago D Nascimento ◽  
Mary C Bender ◽  
Theodore Hall ◽  
Sean Petty ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Augmented Reality ◽  
Real Time ◽  
Pain Detection ◽  
Detection And Localization ◽  
The Brain

scholarly journals FPGA Implementation of RLSE Algorithm for Multichannel Brain Imaging

2021 ◽  
Vol 40 (1) ◽  
pp. 241-250
Author(s):  
Muhammad Shahid Nazir ◽  
Haroon-Ur-Rasheed Khan ◽  
Abubaker Akram ◽  
Bhagesh Maheshwari ◽  
Muhammad Aqil
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Resource Sharing ◽  
Near Infrared ◽  
Brain Activity ◽  
Computational Cost ◽  
Recursive Least Squares ◽  
Multiple Channels ◽  
Functional Near Infrared Spectroscopy ◽  
The Brain

This paper describes the implementation of a computationally efficient embedded system on an Field Programmable Gate Array (FPGA) platform for real-time brain activity estimation with multiple channels. The brain signals from multiple channels are considered as output of independent linear systems with unknown parameters representing the brain activity in corresponding channels. Multiple adaptive Recursive Least-Squares Estimation (RLSE) cores are implemented in FPGA to independently estimate the brain activity in each channel concurrently. The proposed RLSE-FPGA system provides dedicated (no time or resource sharing) and parallel processing environment. The universal asynchronous receiver transmitter core is also developed to communicate the measured and estimated parameters supported by storage facility programmed as shared memory. The computational precision is guaranteed by deploying a 32-bit floating point core for all the variables. The validation carried out by real Functional Near-Infrared Spectroscopy dataset and comparative analysis with the previously reported result, demonstrates the effectiveness of the proposed system. The computational cost endorses the effectiveness of concurrent processing of multiple channelsꞌ data in a sample before the arrival of the next sample. The proposed methodology has potential in real-time medical, military and industrial applications.

scholarly journals Fugl-Meyer hand motor imagination recognition for brain–computer interfaces using only fNIRS

2021 ◽  
Author(s):  
Chenguang Li ◽  
Hongjun Yang ◽  
Long Cheng
Keyword(s):  
Classification Accuracy ◽  
Near Infrared ◽  
Support Vector ◽  
Classification Methods ◽  
Experimental Paradigm ◽  
Functional Near Infrared Spectroscopy ◽  
Machine Method ◽  
Physiological Signal ◽  
Computer Interfaces ◽  
Mode Decomposition

AbstractAs a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

scholarly journals Understanding Mental Health and Cognitive Restructuring With Ecological Neuroscience

2021 ◽  
Vol 12 ◽  
Author(s):  
James Crum
Keyword(s):  
Mental Health ◽  
Near Infrared ◽  
Ecological Validity ◽  
Experimental Designs ◽  
Functional Near Infrared Spectroscopy ◽  
Practical Applications ◽  
Recent Developments ◽  
The Brain ◽  
Neuroimaging Techniques ◽  
Selection Of

Neuroimaging and neuropsychological methods have contributed much toward an understanding of the information processing systems of the human brain in the last few decades, but to what extent do cognitive neuroscientific findings represent and generalize to the inter- and intra-brain dynamics engaged in adapting to naturalistic situations? If it is not marked, and experimental designs lack ecological validity, then this stands to potentially impact the practical applications of a paradigm. In no other domain is this more important to acknowledge than in human clinical neuroimaging research, wherein reduced ecological validity could mean a loss in clinical utility. One way to improve the generalizability and representativeness of findings is to adopt a more “real-world” approach to the development and selection of experimental designs and neuroimaging techniques to investigate the clinically-relevant phenomena of interest. For example, some relatively recent developments to neuroimaging techniques such as functional near-infrared spectroscopy (fNIRS) make it possible to create experimental designs using naturalistic tasks that would otherwise not be possible within the confines of a conventional laboratory. Mental health, cognitive interventions, and the present challenges to investigating the brain during treatment are discussed, as well as how the ecological use of fNIRS might be helpful in bridging the explanatory gaps to understanding the cultivation of mental health.

scholarly journals Functional Near Infrared Spectroscopy (fNIRS) based Odor Detection and Classification using Functional Data Analysis

2021 ◽  
Author(s):  
Faezeh Moradi ◽  
Shima T. Moein ◽  
Issa Zakeri ◽  
Kambiz Pourrezaei
Keyword(s):  
Infrared Spectroscopy ◽  
Data Analysis ◽  
Near Infrared Spectroscopy ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Near Infrared ◽  
Stimulus Condition ◽  
Functional Near Infrared Spectroscopy ◽  
Odor Detection ◽  
The Brain

AbstractAn objective approach for odor detection is to analyze the brain activity using imaging techniques during the odor stimulation. In this study, Functional Near Infrared Spectroscopy (fNIRS) is used to record hemodynamic response from the frontal region of the brain by using a 4-channel fNIRS system. The fNIRs data is collected during the odor detection task in which the subjects were asked to press a button when they detect the given odor. Functional Data Analysis (FDA) was applied on fNIRs data to convert discrete measured samples of data to continuous smooth curves. The FDA method enables us to use the bases coefficients of fNIRS smoothed curves for features that represent the shape of the raw fNIRS signal. With the learning algorithm that we proposed, these features were used to train the support vector machine classifier. We evaluated the odor detection problem, in two binary classification cases: odorant vs. non-odorant and odorant vs. fingertapping. The model achieved a classification accuracy of 94.12% and 97.06% over the stimulus condition in the two cases, respectively. Moreover to find the actual predictors we used the extracted defined features (slope, standard deviation, and delta) to train our classifier. We achieved an average accuracy of 91.18 % on classifying odorant vs. non-odorant and an accuracy of 94.12% for odorant vs. fingertapping on the stimulus condition. The results determined that fNIRs signals of odorant and non-odorant are distinguishable without being affected by the motor activity during the experiment.These findings suggest that fNIRs measurement on the forehead could be potentially used for objective and comparably inexpensive assessment of odor detection in cases that the subjective report is unreliable.

scholarly journals Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)

Biosensors ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 389
Author(s):  
Kogulan Paulmurugan ◽  
Vimalan Vijayaragavan ◽  
Sayantan Ghosh ◽  
Parasuraman Padmanabhan ◽  
Balázs Gulyás
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Brain Function ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Non Invasive ◽  
Neuron Function ◽  
Brain Computer Interfacing ◽  
The Brain ◽  
Computer Interfacing

Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system originally developed for continuous and non-invasive monitoring of brain function by measuring blood oxygen concentration. Recent advancements in brain–computer interfacing allow us to control the neuron function of the brain by combining it with fNIRS to regulate cognitive function. In this review manuscript, we provide information regarding current advancement in fNIRS and how it provides advantages in developing brain–computer interfacing to enable neuron function. We also briefly discuss about how we can use this technology for further applications.

scholarly journals Evaluation of the Short-Term Music Therapy on Brain Functions of Preterm Infants Using Functional Near-Infrared Spectroscopy

2021 ◽  
Vol 12 ◽  
Author(s):  
Haoran Ren ◽  
Liangyan Zou ◽  
Laishuan Wang ◽  
Chunmei Lu ◽  
Yafei Yuan ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Functional Connectivity ◽  
Music Therapy ◽  
Preterm Infants ◽  
Near Infrared ◽  
Music Intervention ◽  
Short Term ◽  
Functional Near Infrared Spectroscopy ◽  
Brain Functions ◽  
The Brain

Music contains substantial contents that humans can perceive and thus has the capability to evoke positive emotions. Even though neonatal intensive care units (NICUs) can provide preterm infants a developmental environment, they still cannot fully simulate the environment in the womb. The reduced maternal care would increase stress levels in premature infants. Fortunately, music intervention has been proved that it can improve the NICU environment, such as stabilize the heart rate and the respiratory rate, reduce the incidence of apnea, and improve feeding. However, the effects of music therapy on the brain development of preterm infants need to be further investigated. In this paper, we evaluated the influence of short-term music therapy on the brain functions of preterm infants measured by functional near-infrared spectroscopy (fNIRS). We began by investigating how premature babies perceive structural information of music by calculating the correlations between music features and fNIRS signals. Then, the influences of short-term music therapy on brain functions were evaluated by comparing the resting-state functional connectivity before and after the short-term music therapy. The results show that distinct brain regions are responsible for processing corresponding musical features, indicating that preterm infants have the capability to process the complex musical content. However, the results of network analysis show that short-term music intervention is insufficient to cause the changes in cerebral functional connectivity. Therefore, long-term music therapy may be required to achieve the deserved effects on brain functional connectivity.

A Review on Brain Imaging Techniques for BCI Applications

2016 ◽  
pp. 39-70
Author(s):  
Saugat Bhattacharyya ◽  
Anwesha Khasnobish ◽  
Poulami Ghosh ◽  
Ankita Mazumder ◽  
D. N. Tibarewala
Keyword(s):  
Magnetic Resonance Imaging ◽  
Positron Emission Tomography ◽  
Magnetic Resonance ◽  
Brain Imaging ◽  
Near Infrared ◽  
Emission Tomography ◽  
Resonance Imaging ◽  
Functional Near Infrared Spectroscopy ◽  
Positron Emission ◽  
The Brain

Evolution has endowed human race with the most adroit brain, and to harness its potential to the fullest the concept of brain computer interface (BCI) has emerged. One of the most crucial components of BCI is the technique of brain imaging. The first approach in the field of brain imaging was to measure the electrical and magnetic activity of the brain, the techniques being known as Electroencephalography and Magnetoencephalography. Striving for furtherance, researchers came up with another alternative known as Magnetic Resonance Imaging. But it being confined to only structural imaging, the functional aspects of brain were mapped using functional magnetic resonance imaging. A similar but comparatively newer neuroimaging modality is Functional Near Infrared Spectroscopy. Transcranial Magnetic Stimulation neuro-physiological technique is based on the principle of electromagnetic induction. Based on nuclear medicine the brain imaging technologies that are widely explored in the world of BCI are Positron Emission Tomography and Single Positron Emission Tomography.

scholarly journals Update of fNIRS as an Input to Brain–Computer Interfaces: A Review of Research from the Tufts Human–Computer Interaction Laboratory

Photonics ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 90 ◽  
Author(s):  
Bosworth ◽  
Russell ◽  
Jacob
Keyword(s):  
Human Computer Interaction ◽  
Real Time ◽  
Near Infrared ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
The Past ◽  
Computer Interfaces ◽  
Brain States ◽  
Computer Interaction

Over the past decade, the Human–Computer Interaction (HCI) Lab at Tufts University has been developing real-time, implicit Brain–Computer Interfaces (BCIs) using functional near-infrared spectroscopy (fNIRS). This paper reviews the work of the lab; we explore how we have used fNIRS to develop BCIs that are based on a variety of human states, including cognitive workload, multitasking, musical learning applications, and preference detection. Our work indicates that fNIRS is a robust tool for the classification of brain-states in real-time, which can provide programmers with useful information to develop interfaces that are more intuitive and beneficial for the user than are currently possible given today’s human-input (e.g., mouse and keyboard).

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