Study of quadrature FIR filters for extraction of low-frequency instantaneous information in biophysical signals

Quadrature bandpass filters take a real-valued signal and output an analytic signal from which the instantaneous amplitude and phase can be computed. For this reason, they represent a useful tool to extract time-varying, narrow-band information from electrophysiological signals such as electroencephalogram (EEG) or electrocardiogram. One of the defining characteristics of quadrature filters is its null response to negative frequencies. However, when the frequency band of interest is close to 0 Hz, a careless filter design could let through negative frequencies, producing distortions in the amplitude and phase of the output. In this work, three types of quadrature filters (Ideal, Gabor and Sinusoidal) have been evaluated using both artificial and real EEG signals. For the artificial signals, the performance of each filter was measured in terms of the distortion in amplitude and phase, and sensitivity to noise and bandwidth selection. For the real EEG signals, a qualitative evaluation of the dynamics of the synchronization between two EEG channels was performed. The results suggest that, while all filters under study behave similarly under noise, they differ in terms of their sensitivity to bandwidth choice. In this study, the Sinusoidal filter showed clear advantages for the estimation of low-frequency EEG synchronization.

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Deep Learning-Based Classification of Fine Hand Movements from Low Frequency EEG

Future Internet ◽

10.3390/fi13050103 ◽

2021 ◽

Vol 13 (5) ◽

pp. 103

Author(s):

Giulia Bressan ◽

Giulia Cisotto ◽

Gernot R. Müller-Putz ◽

Selina Christin Wriessnegger

Keyword(s):

Low Frequency ◽

Classification Performance ◽

Hand Movements ◽

Eeg Signals ◽

Low Frequencies ◽

Linear Discriminant ◽

Comparable Performance ◽

Research Challenge ◽

Electroencephalogram Eeg

The classification of different fine hand movements from electroencephalogram (EEG) signals represents a relevant research challenge, e.g., in BCI applications for motor rehabilitation. Here, we analyzed two different datasets where fine hand movements (touch, grasp, palmar, and lateral grasp) were performed in a self-paced modality. We trained and tested a newly proposed CNN, and we compared its classification performance with two well-established machine learning models, namely, shrinkage-linear discriminant analysis (LDA) and Random Forest (RF). Compared to previous literature, we included neuroscientific evidence, and we trained our Convolutional Neural Network (CNN) model on the so-called movement-related cortical potentials (MRCPs). They are EEG amplitude modulations at low frequencies, i.e., (0.3,3) Hz that have been proved to encode several properties of the movements, e.g., type of grasp, force level, and speed. We showed that CNN achieved good performance in both datasets (accuracy of 0.70±0.11 and 0.64±0.10, for the two datasets, respectively), and they were similar or superior to the baseline models (accuracy of 0.68±0.10 and 0.62±0.07 with sLDA; accuracy of 0.70±0.15 and 0.61±0.07 with RF, with comparable performance in precision and recall). In addition, compared to the baseline, our CNN requires a faster pre-processing procedure, paving the way for its possible use in online BCI applications.

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Fully integrated active filter design forultra low frequency application

2007 International Conference on Design & Technology of Integrated Systems in Nanoscale Era ◽

10.1109/dtis.2007.4449525 ◽

2007 ◽

Cited By ~ 2

Author(s):

Hala Y. Darweesh ◽

Yaser A. Khalaf ◽

Fathi A. Farag

Keyword(s):

Filter Design ◽

Low Frequency ◽

Active Filter ◽

Fully Integrated

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Detecting driver mental fatigue based on Electroencephalogram (EEG) signals during simulated driving

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1070/1/012096 ◽

2021 ◽

Vol 1070 (1) ◽

pp. 012096

Author(s):

S Pradeep Kumar ◽

Suganiya Murugan ◽

Jerritta Selvaraj ◽

Arun Sahayadhas

Keyword(s):

Mental Fatigue ◽

Eeg Signals ◽

Simulated Driving ◽

Electroencephalogram Eeg

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EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

Brain Sciences ◽

10.3390/brainsci9110326 ◽

2019 ◽

Vol 9 (11) ◽

pp. 326 ◽

Cited By ~ 5

Author(s):

Hong Zeng ◽

Zhenhua Wu ◽

Jiaming Zhang ◽

Chen Yang ◽

Hua Zhang ◽

...

Keyword(s):

Deep Learning ◽

Speaker Recognition ◽

Classification Accuracy ◽

Deep Neural Network ◽

Signal To Noise Ratio ◽

Single Subject ◽

Emotion Classification ◽

Eeg Signals ◽

Electroencephalogram Eeg ◽

Deep Learning Model

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

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Bandwidth selection for kernel density estimation using Fourier domain constraints

IET Signal Processing ◽

10.1049/iet-spr.2015.0076 ◽

2016 ◽

Vol 10 (3) ◽

pp. 280-283

Author(s):

Alexander Suhre ◽

Orhan Arikan ◽

Ahmed Enis Cetin

Keyword(s):

Density Estimation ◽

Kernel Density Estimation ◽

Bandwidth Selection ◽

Kernel Density ◽

Fourier Domain ◽

Selection For ◽

Domain Constraints

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AGE-BASED VARIATIONS OF FRACTAL STRUCTURE OF EEG SIGNAL IN PATIENTS WITH EPILEPSY

Fractals ◽

10.1142/s0218348x18500512 ◽

2018 ◽

Vol 26 (04) ◽

pp. 1850051 ◽

Cited By ~ 32

Author(s):

HAMIDREZA NAMAZI ◽

SAJAD JAFARI

Keyword(s):

Fractal Dimension ◽

Brain Research ◽

Age Groups ◽

Eeg Signal ◽

Brain Disorders ◽

Eeg Signals ◽

Important Challenge ◽

Patients With Epilepsy ◽

Process Complexity ◽

Electroencephalogram Eeg

It is known that aging affects neuroplasticity. On the other hand, neuroplasticity can be studied by analyzing the electroencephalogram (EEG) signal. An important challenge in brain research is to study the variations of neuroplasticity during aging for patients suffering from epilepsy. This study investigates the variations of the complexity of EEG signal during aging for patients with epilepsy. For this purpose, we employed fractal dimension as an indicator of process complexity. We classified the subjects in different age groups and computed the fractal dimension of their EEG signals. Our investigations showed that as patients get older, their EEG signal will be more complex. The method of investigation that has been used in this study can be further employed to study the variations of EEG signal in case of other brain disorders during aging.

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A High Efficient Baseband GNSS Signal Narrow Band Anti-Jamming Approach in Frequency Domain

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.1857 ◽

2014 ◽

Vol 926-930 ◽

pp. 1857-1860

Author(s):

Zhou Zheng ◽

Meng Yuan Li ◽

Wei Jiang Wang

Keyword(s):

Signal Processing ◽

Frequency Domain ◽

Narrow Band ◽

Low Frequency ◽

Frequency Resolution ◽

Interference Rejection ◽

High Efficient ◽

Digital Down Conversion ◽

Down Conversion ◽

Low Rate

In order to reduce the burden of the calculation and the low frequency resolution of the tradition GNSS signal intermediate narrow band anti-jamming method, it introduces a high efficient approach of narrow band interference rejection based on baseband GNSS signal processing. After digital down conversion to baseband and down sampling to a low rate, the interference is removed in frequency domain. According to the theoretical analysis and simulation, it claims that the method can reduce the calculation and increase the detection resolution in frequency domain which will realize a high efficient interference rejection.

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Time-ResNeXt for epilepsy recognition based on EEG signals in wireless networks

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01810-5 ◽

2020 ◽

Vol 2020 (1) ◽

Cited By ~ 1

Author(s):

Shaoqiang Wang ◽

Shudong Wang ◽

Song Zhang ◽

Yifan Wang

Keyword(s):

Deep Learning ◽

Network Structure ◽

Signal Recognition ◽

Eeg Signals ◽

Real World Data ◽

Data Set ◽

Practical Applications ◽

Epilepsy Diagnosis ◽

Single Data ◽

Electroencephalogram Eeg

Abstract To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice.

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Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation

Technology and Health Care ◽

10.3233/thc-213136 ◽

2021 ◽

pp. 1-11

Author(s):

Najmeh Pakniyat ◽

Mohammad Hossein Babini ◽

Vladimir V. Kulish ◽

Hamidreza Namazi

Keyword(s):

Time Series ◽

Biomedical Science ◽

Strongly Correlated ◽

Eeg Signals ◽

Ecg Signals ◽

Olfactory Stimuli ◽

Electrocardiogram Ecg ◽

First Time ◽

Electroencephalogram Eeg ◽

The Brain

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart’s activity, a relationship should exist among their activities. OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18–22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION: We conclude that heart and brain activities are related.

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