Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces

Frontiers in Human Neuroscience, Journal Year: 2018, Volume and Issue: 12

Published: June 28, 2018

In this study, a brain-computer interface (BCI) framework for hybrid functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) locked-in syndrome (LIS) patients is investigated. Brain tasks, channel selection methods, feature extraction classification algorithms available in the literature are reviewed. First, we categorize various types of with cognitive motor impairments to assess suitability BCI each them. The prefrontal cortex identified as suitable brain region imaging. Second, activity that contributes generation hemodynamic signals Mental arithmetic word formation tasks found be use LIS patients. Third, since specific targeted needed BCI, methods determining interest combination bundled-optode configuration threshold-integrated vector phase analysis turns out promising solution. Fourth, usable fNIRS features EEG For signal peak mean highest band powers promising. classification, linear discriminant has been most widely used. However, further research on classifier multiple commands desirable. Overall, proper identification will improve accuracy. conclusion, five future issues identified, new scheme, including therapy using fNIRS-EEG provided.

Language: Английский

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Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states

Physics Reports, Journal Year: 2021, Volume and Issue: 918, P. 1 - 133

Published: March 24, 2021

Language: Английский

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Proprioceptive Sonomyographic Control: A novel method for intuitive and proportional control of multiple degrees-of-freedom for individuals with upper extremity limb loss

Scientific Reports, Journal Year: 2019, Volume and Issue: 9(1)

Published: July 1, 2019

Technological advances in multi-articulated prosthetic hands have outpaced the methods available to amputees intuitively control these devices. Amputees often cite difficulty of use as a key contributing factor for abandoning their prosthesis, creating pressing need improved technology. A major challenge traditional myoelectric strategies using surface electromyography electrodes has been achieving intuitive and robust proportional multiple degrees freedom. In this paper, we describe new method, proprioceptive sonomyographic that overcomes several limitations control. sonomyography, muscle mechanical deformation is sensed ultrasound, compared electrical activation, therefore resulting signals can directly position end effector. Compared which controls velocity end-effector device, more congruent with residual proprioception limb. We tested our approach 5 upper-extremity able-bodied subjects virtual target achievement holding task. participants demonstrated ability achieve positional freedom an hour training. Our results demonstrate potential dexterous multiarticulated prostheses.

Language: Английский

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Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain–Computer Interface

Frontiers in Neuroscience, Journal Year: 2020, Volume and Issue: 14

Published: June 23, 2020

Cognitive workload is one of the widely invoked human factor in areas Human Machine Interaction (HMI) and Neuroergonomics. The precise assessment cognitive mental (MWL) vital requires accurate neuroimaging to monitor evaluate states brain. In this study, we have decoded four classes using long-short term memory (LSTM) with 89.31% average accuracy for brain-Computer Interface (BCI). brain activity signals are acquired functional Near-Infrared Spectroscopy (fNIRS) from prefrontal cortex (PFC) region We performed a supervised MWL experimentation varying levels on 15 participants (both male female) 10 trials each per participant. Real-time four-level assessed fNIRS system initial classification three strong machine learning (ML) techniques, Support Vector (SVM), k-Nearest Neighbor (k-NN) Artificial Neural Network (ANN) obtained accuracies 54.33%, 54.31%, 69.36% respectively. novel Deep (DL) frameworks proposed which utilizes Convolutional (CNN) Long Short-Term Memory 87.45% respectively, solve high-dimensional problem. Statistical analysis, t- test one-way F-test (ANOVA) also through deep algorithms. Results show that DL (LSTM CNN) algorithms significantly improve performance as compared ML (SVM, ANN, k-NN)

Language: Английский

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A Survey of Human Gait-Based Artificial Intelligence Applications

Frontiers in Robotics and AI, Journal Year: 2022, Volume and Issue: 8

Published: Jan. 3, 2022

We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. identified six key applications using data: 1) Gait analysis where analyzing techniques certain biomechanical factors are improved by utilizing artificial intelligence algorithms, 2) Health Wellness, with in monitoring for abnormal detection, recognition activities, fall detection sports performance, 3) Human Pose Tracking one-person or multi-person tracking localization systems such as OpenPose, Simultaneous Localization Mapping (SLAM), etc., 4) Gait-based biometrics person identification, authentication, re-identification well gender age 5) “Smart gait” ranging smart socks, shoes, other wearables homes retail stores incorporate continuous control 6) Animation reconstructs motion data, simulation Our goal is provide a single broad-based survey the technology identify future areas potential study growth. discuss have been used tasks they perform, problems attempt solve, trade-offs navigate.

Language: Английский

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Machine learning based liver disease diagnosis: A systematic review

Neurocomputing, Journal Year: 2021, Volume and Issue: 468, P. 492 - 509

Published: Sept. 6, 2021

Language: Английский

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Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Frontiers in Human Neuroscience, Journal Year: 2021, Volume and Issue: 14

Published: Jan. 25, 2021

Human gait is a complex activity that requires high coordination between the central nervous system, limb, and musculoskeletal system. More research needed to understand latter coordination's complexity in designing better more effective rehabilitation strategies for disorders. Electroencephalogram (EEG) functional near-infrared spectroscopy (fNIRS) are among most used technologies monitoring brain activities due portability, non-invasiveness, relatively low cost compared others. Fusing EEG fNIRS well-known established methodology proven enhance brain–computer interface (BCI) performance terms of classification accuracy, number control commands, response time. Although there has been significant exploring hybrid BCI (hBCI) involving both different types tasks human activities, remains still underinvestigated. In this article, we aim shed light on recent development analysis using EEG-fNIRS-based The current review followed guidelines preferred reporting items systematic reviews meta-Analyses (PRISMA) during data collection selection phase. review, put particular focus commonly signal processing machine learning algorithms, as well survey potential applications analysis. We distill some critical findings follows. First, hardware specifications experimental paradigms should be carefully considered because their direct impact quality assessment. Second, since modalities, fNIRS, sensitive motion artifacts, instrumental, physiological noises, quest robust sophisticated algorithms. Third, temporal spatial features, obtained by virtue fusing associated with cortical activation, can help identify correlation activation gait. conclusion, hBCI (EEG + fNIRS) system not yet much explored lower limb its higher limb. Existing systems tend only one modality. foresee vast adopting Imminent technical breakthroughs expected assistive devices Monitor neuro-plasticity neuro-rehabilitation. However, although those perform controlled environment when it comes them certified medical device real-life clinical applications, long way go.

Language: Английский

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Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks

Sensors, Journal Year: 2022, Volume and Issue: 22(5), P. 1932 - 1932

Published: March 1, 2022

This research presents a brain-computer interface (BCI) framework for brain signal classification using deep learning (DL) and machine (ML) approaches on functional near-infrared spectroscopy (fNIRS) signals. fNIRS signals of motor execution walking rest tasks are acquired from the primary cortex in brain's left hemisphere nine subjects. DL algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM) used to achieve average accuracies 88.50%, 84.24%, 85.13%, respectively. For comparison purposes, three conventional ML support vector (SVM), k-nearest neighbor (k-NN), linear discriminant analysis (LDA) also classification, resulting 73.91%, 74.24%, 65.85%, study successfully demonstrates that enhanced performance fNIRS-BCI can be achieved terms accuracy compared approaches. Furthermore, control commands generated by these classifiers initiate stop gait cycle lower limb exoskeleton rehabilitation.

Language: Английский

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Driving drowsiness detection using spectral signatures of EEG-based neurophysiology

Frontiers in Physiology, Journal Year: 2023, Volume and Issue: 14

Published: March 30, 2023

Introduction: Drowsy driving is a significant factor causing dire road crashes and casualties around the world. Detecting it earlier more effectively can significantly reduce lethal aftereffects increase safety. As physiological conditions originate from human brain, so neurophysiological signatures in drowsy alert states may be investigated for this purpose. In preface, A passive brain-computer interface (pBCI) scheme using multichannel electroencephalography (EEG) brain signals developed spatially localized accurate detection of drowsiness during tasks. Methods: This pBCI modality acquired electrophysiological patterns 12 healthy subjects prefrontal (PFC), frontal (FC), occipital cortices (OC) brain. Neurological are recorded six EEG channels spread over right left hemispheres PFC, FC, OC sleep-deprived simulated post-hoc analysis, spectral δ, θ, α, β rhythms extracted terms band powers their ratios with temporal correlation complete span experiment. Minimum redundancy maximum relevance, Chi-square, ReliefF feature selection methods used aggregated Z-score based approach global ranking. The attributes classified decision trees, discriminant logistic regression, naïve Bayes, support vector machines, k-nearest neighbors, ensemble classifiers. binary classification results reported confusion matrix-based performance assessment metrics. Results: inter-classifier comparison, optimized model achieved best 85.6% accuracy precision, 89.7% recall, 87.6% F1-score, 80% specificity, 70.3% Matthews coefficient, 70.2% Cohen's kappa score, 91% area under receiver operating characteristic curve 76-ms execution time. inter-channel were obtained at F8 electrode position FC significance all was validated p-value less than 0.05 statistical hypothesis testing methods. Conclusions: proposed has better accomplishment multiple objectives. predictor importance reduced extraction cost computational complexity minimized use conventional machine learning classifiers resulting low-cost hardware software requirements. channel most promising region only single (F8) which reduces physical intrusiveness normal operation. good potential practical applications requiring earlier, accurate, disruptive information biosignals.

Language: Английский

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Interdisciplinary views of fNIRS: Current advancements, equity challenges, and an agenda for future needs of a diverse fNIRS research community

Frontiers in Integrative Neuroscience, Journal Year: 2023, Volume and Issue: 17

Published: Feb. 27, 2023

Functional Near-Infrared Spectroscopy (fNIRS) is an innovative and promising neuroimaging modality for studying brain activity in real-world environments. While fNIRS has seen rapid advancements hardware, software, research applications since its emergence nearly 30 years ago, limitations still exist regarding all three areas, where existing practices contribute to greater bias within the neuroscience community. We spotlight through lens of different end-application users, including unique perspective a manufacturer, report challenges using this technology across several disciplines populations. Through review domains utilized, we identify address presence bias, specifically due restraints current technology, limited diversity among sample populations, societal prejudice that infiltrates today's research. Finally, provide resources minimizing application agenda future use equitable, diverse, inclusive.

Language: Английский

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