Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection

Emotion detection is an important research issue in electroencephalogram (EEG). Signal preprocessing and feature selection are parts of feature engineering, which determines the performance of emotion detection and reduces the training time of the deep learning models. To select the efficient features for emotion detection, we propose a maximum marginal approach on EEG signal preprocessing. The approach selects the least similar segments between two EEG signals as features that can represent the difference between EEG signals caused by emotions. The method defines a signal similarity described as the distance between two EEG signals to find the features. The frequency domain of EEG is calculated by using a wavelet transform that exploits a wavelet to calculate EEG components in a different frequency. We have conducted experiments by using the selected feature from real EEG data recorded from 10 college students. The experimental results show that the proposed approach performs better than other feature selection methods by 17.9% on average in terms of accuracy. The maximum marginal approach-based models achieve better performance than the models without feature selection by 21% on average in terms of accuracy.

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Denoising of EEG signal based on word imagination using ICA for artifact and noise removal on unspoken speech

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v22.i1.pp83-88 ◽

2021 ◽

Vol 22 (1) ◽

pp. 83

Author(s):

Efy Yosrita ◽

Rosida Nur Aziza ◽

Rahma Farah Ningrum ◽

Givary Muhammad

Keyword(s):

Noise Removal ◽

Physical Activities ◽

Eeg Signal ◽

Eeg Signals ◽

Signal Ratio ◽

Eye Blink ◽

Eeg Data ◽

Word Classification ◽

Electroencephalogram Eeg ◽

Electrical Activities

<span>The purpose of this research is to observe the effectiveness of independent component analysis (ICA) method for denoising raw EEG signals based on word imagination, which will be used for word classification on unspoken speech. The electroencephalogram (EEG) signals are signals that represent the electrical activities of the human brain when someone is doing activities, such as sleeping, thinking or other physical activities. EEG data based on the word imagination used for the research is accompanied by artifacts, that come from muscle movements, heartbeat, eye blink, voltage and so on. In previous studies, the ICA method has been widely used and effective for relieving physiological artifacts. Artifact to signal ratio (ASR) is used to measure the effectiveness of ICA in this study. If the ratio is getting larger, the ICA method is considered effective for clearing noise and artifacts from the EEG data. Based on the experiment, the obtained ASR values from 11 subjects on 14 electrodes amounted are within the range of 0,910 to 1,080. Thus, it can be concluded that ICA is effective for removing artifacts from EEG signals based on word imagination.</span>

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Removal of Artifacts from EEG Signals: A Review

Sensors ◽

10.3390/s19050987 ◽

2019 ◽

Vol 19 (5) ◽

pp. 987 ◽

Cited By ~ 51

Author(s):

Xiao Jiang ◽

Gui-Bin Bian ◽

Zean Tian

Keyword(s):

Comparative Analysis ◽

Open Problem ◽

State Of The Art ◽

Brain Activity ◽

Eeg Signal ◽

Eeg Signals ◽

Artifact Removal ◽

Eeg Data ◽

And Behavior ◽

Electroencephalogram Eeg

Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.

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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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An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm

International Journal of Biomedical Imaging ◽

10.1155/2018/5812872 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

Ahmed I. Sharaf ◽

Mohamed Abu El-Soud ◽

Ibrahim M. El-Henawy

Keyword(s):

Feature Selection ◽

Correlation Energy ◽

Epileptic Seizures ◽

Compact Set ◽

Eeg Signal ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Eeg Signals ◽

Computer Aided ◽

Firefly Optimization

Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable Q-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew’s correlation coefficient.

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EEG data processing in ADHD diagnosis and neurofeedback

Engineering review ◽

10.30765/er.40.3.12 ◽

2020 ◽

Vol 40 (3) ◽

pp. 116-123

Author(s):

Zoran Šverko ◽

Ivan Markovinović ◽

Miroslav Vrankić ◽

Saša Vlahinić

Keyword(s):

Data Processing ◽

Mean Value ◽

Adhd Group ◽

Eeg Signal ◽

Statistical Features ◽

Eeg Signals ◽

New Approach ◽

Mean Values ◽

Adhd Diagnosis ◽

Eeg Data

In this paper, EEG data processing was conducted in order to define the parameters for neurofeedback. A new survey was conducted based on a brief review of previous research. Two groups of participants were chosen: ADHD (3) and nonADHD (14). The main part of this study includes EEG signal data pre-processing and processing. We have outlined statistical features of observed EEG signals such as mean value, grand-mean value and their ratios. It can be concluded that an increase in grand-mean values of power theta-low beta ratio on Cz electrode gives confirmation of previous research. The value of alpha-delta power ratio higher than 1 on C3, Cz, P3, Pz, P4 in ADHD group is proposed as a new approach to classification. Based on these conclusions we will design a neurofeedback protocol as a continuation of this work.

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A portable real-time EEG signal acquisition and tele-medicine system

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-204582 ◽

2020 ◽

pp. 1-11

Author(s):

Qiang Zhang ◽

Peng Wang ◽

Shanshan Li ◽

Yonghao Jing

Keyword(s):

Real Time ◽

Sql Server ◽

Signal Acquisition ◽

Eeg Signal ◽

Eeg Signals ◽

Eeg Data ◽

Sql Server Database ◽

High Power Consumption ◽

Machine Interface ◽

Sd Card

Since electroencephalogram (EEG) signals contain a variety of physiological and pathological information, they are widely used in medical diagnosis, brain machine interface and other fields. The existing EEG apparatus are not perfect due to big size, high power consumption and using cables to transmit data. In this paper, a portable real-time EEG signal acquisition and tele-medicine system is developed in order to improve performance of EEG apparatus. The weak EEG signals are induced to the pre-processing circuits via a noninvasive method with bipolar leads. After multi-level amplifying and filtering, these signals are transmitted to DSP (TMS320C5509) to conduct digital filtering. Then, the EEG signals are displayed on the LCD screen and stored in the SD card so that they can be uploaded to the server through the internet. The server employs SQL Server database to manage patients’ information and to store data in disk. Doctors can download, look up and analyze patients’ EEG data using the doctor client. Experimental results demonstrate that the system can acquire weak EEG signals in real time, display the processed results, save data and carry out tele-medicine. The system can meet the requirement of the EEG signals’ quality, and are easy to use and carry.

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QUANTIFICATION OF DEPTH OF ANESTHESIA BY MEANS OF ADAPTIVE CALCULATION OF CORRELATION DIMENSION PARAMETERS

Fractals ◽

10.1142/s0218348x09004594 ◽

2009 ◽

Vol 17 (04) ◽

pp. 473-483

Author(s):

BEHZAD AHMADI ◽

BAHAREH ZAGHARI ◽

RASSOUL AMIRFATTAHI ◽

MOJTABA MANSOURI

Keyword(s):

Correlation Dimension ◽

Single Channel ◽

Drug Effects ◽

Eeg Signal ◽

Depth Of Anesthesia ◽

Anesthetic Drugs ◽

Eeg Data ◽

Prediction Probability ◽

Analysis Application ◽

Electroencephalogram Eeg

This paper proposes an approach for quantifying Depth of Anesthesia (DOA) based on correlation dimension (D2) of electroencephalogram (EEG). The single-channel EEG data was captured in both ICU and operating room while different anesthetic drugs, including propofol and isoflurane, were used. Correlation dimension was computed using various optimized parameters in order to achieve the maximum sensitivity to anesthetic drug effects and to enable real time computation. For better analysis, application of adaptive segmentation on EEG signal for estimating DOA was evaluated and compared to fixed segmentation, too. Prediction probability (PK) was used as a measure of correlation between the predictors and BIS index to evaluate the proposed methods. Appropriate correlation between DOA and correlation dimension is achieved while choosing (D2) parameters adaptively in comparison to fixed parameters due to the nonstationary nature of EEG signal.

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P300 Feature Extraction of Visual and Auditory Evoked EEG Signal

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.490-491.1374 ◽

2014 ◽

Vol 490-491 ◽

pp. 1374-1377 ◽

Cited By ~ 1

Author(s):

Xiao Yan Qiao ◽

Jia Hui Peng

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Evoked Potentials ◽

Brain Computer Interface ◽

Auditory Stimulation ◽

Computer Interface ◽

Eeg Signal ◽

Significant Issue ◽

Eeg Signals ◽

Electroencephalogram Eeg

It is a significant issue to accurately and quickly extract brain evoked potentials under strong noise in the research of brain-computer interface technology. Considering the non-stationary and nonlinearity of the electroencephalogram (EEG) signal, the method of wavelet transform is adopted to extract P300 feature from visual, auditory and visual-auditory evoked EEG signal. Firstly, the imperative pretreatment to EEG acquisition signals was performed. Secondly, respectivly obtained approximate and detail coefficients of each layer, by decomposing the pretreated signals for five layers using wavelet transform. Finally, the approximate coefficients of the fifth layer were reconstructed to extract P300 feature. The results have shown that the method can effectively extract the P300 feature under the different visual-auditory stimulation modes and lay a foundation for processing visual-auditory evoked EEG signals under the different mental tasks.

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A Portable Electroencephalogram Recording System for Rats

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.577.1236 ◽

2014 ◽

Vol 577 ◽

pp. 1236-1240

Author(s):

Dian Zhang ◽

Bo Wang ◽

Qing Liang Qin

Keyword(s):

Recording System ◽

Band Pass Filter ◽

Test Results ◽

Electronic Components ◽

Eeg Signals ◽

Pass Filter ◽

Wireless Transmitter ◽

Eeg Data ◽

Band Pass ◽

Electroencephalogram Eeg

A wireless portable electroencephalogram (EEG) recording system for animals was designed, manufactured and then tested in rats. The system basically consisted of four modules: 1) EEG collecting module with the wireless transmitter and receiver (designed by NRF24LE1), 2) filter bank consisting of pre-amplifier, band pass filter and 50Hz trapper, 3) power management module and 4) display interface for showing EEG signals. The EEG data were modulated firstly and emitted by the wireless transmitter after being amplified and filtered. The receiver demodulated and displayed the signals in voltage through serial port. The system was designed as surface mount devices (SMD) with small size (20mm×25mm×3mm) and light weight (4g), and was fabricated of electronic components that were commercially available. The test results indicated that in given environment the system could stably record more than 8 hours and transmit EEG signals over a distance of 20m. Our system showed the features of small size, low power consumption and high accuracy which were suitable for EEG telemetry in rats.

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