scholarly journals Real-Time Tracking of Selective Auditory Attention from M/EEG: A Bayesian Filtering Approach

2017 ◽  
Author(s):  
Sina Miran ◽  
Sahar Akram ◽  
Alireza Sheikhattar ◽  
Jonathan Z. Simon ◽  
Tao Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Adaptive Filtering ◽  
Significant Degree ◽  
Bayesian Filtering ◽  
Attentional State ◽  
Non Invasive ◽  
Entire Duration ◽  
Eeg Data ◽  
Multiple Trials ◽  
Target Speaker

AbstractHumans are able to identify and track a target speaker amid a cacophony of acoustic interference, which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). These procedures operate in an offline fashion, i.e., require the entire duration of the experiment and multiple trials to provide robust results. Therefore, they cannot be used in emerging applications such as smart hearing aid devices, where a single trial must be used in real-time to decode the attentional state. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the attentional state. Our proposed framework consists of three main modules: 1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, 2) Extracting reliable markers of the attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and 3) Devising a near real-time state-space estimator that translates the noisy and variable attention markers to robust and reliable estimates of the attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, ℓ1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurate as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings.

scholarly journals Parameter estimation and identifiability in a neural population model for electro-cortical activity

2018 ◽  
Author(s):  
Agus Hartoyo ◽  
Peter J. Cadusch ◽  
David T. J. Liley ◽  
Damien G. Hicks
Keyword(s):  
Real Time ◽  
Population Model ◽  
Alpha Rhythm ◽  
Population Models ◽  
Neural Population ◽  
Eeg Signals ◽  
Macroscopic System ◽  
Non Invasive ◽  
Eeg Data ◽  
Input Parameters

AbstractElectroencephalography (EEG) provides a non-invasive measure of brain electrical activity. Neural population models, where large numbers of interacting neurons are considered collectively as a macroscopic system, have long been used to understand features in EEG signals. By tuning dozens of input parameters describing the excitatory and inhibitory neuron populations, these models can reproduce prominent features of the EEG such as the alpha-rhythm. However, the inverse problem, of directly estimating the parameters from fits to EEG data, remains unsolved. Solving this multi-parameter non-linear fitting problem will potentially provide a real-time method for characterizing average neuronal properties in human subjects. Here we perform unbiased fits of a 22-parameter neural population model to EEG data from 82 individuals, using both particle swarm optimization and Markov chain Monte Carlo sampling. We estimate how much is learned about individual parameters by computing Kullback-Leibler divergences between posterior and prior distributions for each parameter. Results indicate that only a single parameter, that determining the dynamics of inhibition, is directly identifiable, while other parameters have large, though correlated, uncertainties. We show that the eigenvalues of the Fisher information matrix are roughly uniformly spaced over a log scale, indicating that the model is sloppy, like many of the regulatory network models in systems biology. These eigenvalues indicate that the system can be modeled with a low effective dimensionality, with inhibition being prominent in driving system behavior.Author summaryElectroencephalography (EEG), where electrodes are used to measure electric potential on the outside of the scalp, provides a simple, non-invasive way to study brain activity. Physiological interpretation of features in EEG signals has often involved use of collective models of neural populations. These neural population models have dozens of input parameters to describe the properties of inhibitory and excitatory neurons. Being able to estimate these parameters by direct fits to EEG data holds the promise of providing a real-time non-invasive method of inferring neuronal properties in different individuals. However, it has long been impossible to fit these nonlinear, multi-parameter models effectively. Here we describe fits of a 22-parameter neural population model to EEG spectra from 82 different subjects, all exhibiting alpha-oscillations. We show how only one parameter, that describing inhibitory dynamics, is constrained by the data, although all parameters are correlated. These results indicate that inhibition plays a central role in the generation and modulation of the alpha-rhythm in humans.

scholarly journals Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4269
Author(s):  
Yoon-A Choi ◽  
Se-Jin Park ◽  
Jong-Arm Jun ◽  
Cheol-Sig Pyo ◽  
Kang-Hee Cho ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Daily Life ◽  
Experimental Results ◽  
Series Data ◽  
Disease Prediction ◽  
Learning Models ◽  
Non Invasive ◽  
Eeg Data ◽  
Bidirectional Lstm

The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and covid-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderly, is a disease that requires continuous medical observation and monitoring, as its recurrence rate and mortality rate are very high. Most studies examining stroke disease to date have used MRI or CT images for simple classification. This clinical approach (imaging) is expensive and time-consuming while requiring bulky equipment. Recently, there has been increasing interest in using non-invasive measurable EEGs to compensate for these shortcomings. However, the prediction algorithms and processing procedures are both time-consuming because the raw data needs to be separated before the specific attributes can be obtained. Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94.0% accuracy with low FPR (6.0%) and FNR (5.7%), thus showing high confidence in our system. These experimental results demonstrate the feasibility of non-invasive methods that can easily measure brain waves alone to predict and monitor stroke diseases in real time during daily life. These findings are expected to lead to significant improvements for early stroke detection with reduced cost and discomfort compared to other measuring techniques.

INVESTIGATION OF HAND KINEMATICS TOWARDS FRONT CRAWL SWIMMING USING NON-INVASIVE REAL-TIME MONITORING SYSTEM

2014 ◽  
Author(s):  
Rozaimi Ghazali ◽  
◽  
Asiah Mohd Pilus ◽  
Wan Mohd Bukhari Wan Daud ◽  
Mohd Juzaila Abd Latif ◽  
...  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Real Time Monitoring ◽  
Front Crawl ◽  
Non Invasive ◽  
Hand Kinematics

scholarly journals Serial Ultrasonographic and Real-Time Elastosonographic Assessment of the Ovine Common Calcaneal Tendon, after an Experimentally Induced Tendinopathy

2021 ◽  
Vol 8 (4) ◽  
pp. 54
Author(s):  
Daniele Serrani ◽  
Antonella Volta ◽  
Franco Cingolani ◽  
Luca Pennasilico ◽  
Caterina Di Bella ◽  
...  
Keyword(s):  
Real Time ◽  
Statistical Significance ◽  
Healing Process ◽  
Tendon Healing ◽  
Tissue Elasticity ◽  
Calcaneal Tendon ◽  
Non Invasive ◽  
Type Ia ◽  
Single Operator ◽  
Experimentally Induced

Real-time elastosonography (RTE) is a recently described, non-invasive, ultrasonographic technique developed to assess tissue elasticity. The main aim of this study was to investigate the ultrasonographic and elastosonographic appearance of the common calcaneal tendon (CCT) in an ovine model, and to monitor the progression of tendon healing after an experimentally-induced tendinopathy. Sound tendons were initially evaluated (T0) with a caliper and by a single operator with ultrasound. Ultrasonographic and elastosonographic images were then acquired. Subsequently, ultrasound-guided tendon lesions were induced by injecting 500 IU of Type IA collagenases proximally to the calcaneal tuberosity. Caliper measurement, ultrasonography and elastosonography were then repeated at 15 (T1), 30 (T2) and 60 (T3) days. Clinically measured width of the tendon, ultrasonographic thickness and width and percentage of hard (Elx-t%hrd) and soft (Elx-t%sft) tissue were recorded. Statistical analysis was performed on the data collected; statistical significance was set at p < 0.05. Intra-class correlation coefficient (ICC) revealed good (0.68) repeatability of elastosonographic evaluation of the CCT. The tendon width was significantly increased when comparing T0 with T1–2 and decreased when comparing T1–2 with T3. Ultrasound-assessed thickness was significantly increased between T0–T1 and decreased between T1-T2–3. Elx-t%hrd was significantly decreased at T1–2–3 and Elx-t%sft was significantly increased at T1–2–3. In conclusion, the ovine CCT is a highly stiff structure that undergoes a severe loss of stiffness during the healing process. Thickness and width of the tendon increased during the first 30 days and then reduced progressively along the subsequent 30 days. Ultrasonographic appearance of the tendon remained severely abnormal and the tendon showed severely reduced elastic proprieties 60 days after lesion induction.

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