scholarly journals EEG data processing in ADHD diagnosis and neurofeedback

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.

A new simpler approach to measure the strength of toe plantar flexion requiring no mechanical restraint with a light-weight device

2021 ◽  
pp. 095441192110029
Author(s):  
Yuko Komuro ◽  
Yuji Ohta
Keyword(s):  
Plantar Flexion ◽  
Mean Value ◽  
Simple Approach ◽  
Light Weight ◽  
New Approach ◽  
Mean Values ◽  
Mechanical Restraint ◽  
Significant Difference ◽  
Ankle Joints ◽  
The Mean

Conventionally, the strength of toe plantar flexion (STPF) is measured in a seated position, in which not only the target toe joints but also the knee and particularly ankle joints, are usually restrained. We have developed an approach for the measurement of STPF which does not involve restraint and considers the interactions of adjacent joints of the lower extremities. This study aimed to evaluate this new approach and comparing with the seated approach. A thin, light-weight, rigid plate was attached to the sole of the foot in order to immobilize the toe area. Participants were 13 healthy young women (mean age: 24 ± 4 years). For measurement of STPF with the new approach, participants were instructed to stand, raise the device-wearing leg slightly, plantar flex the ankle, and push the sensor sheet with the toes to exert STPF. The sensor sheet of the F-scan II system was inserted between the foot sole and the plate. For measurement with the seated approach, participants were instructed to sit and push the sensor with the toes. They were required to maintain the hip, knee, and ankle joints at 90°. The mean values of maximum STPF of the 13 participants obtained with each approach were compared. There was no significant difference in mean value of maximum STPF when the two approaches were compared (new: 59 ± 23 N, seated: 47 ± 33 N). The coefficient of variation of maximum STPF was smaller for data obtained with the new approach (new: 39%, seated: 70%). Our simple approach enables measurement of STPF without the need for the restraints that are required for the conventional seated approach. These results suggest that the new approach is a valid method for measurement of STPF.

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

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.

scholarly journals Perceiving Stress on Affective State Based on Features from EEG

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Hafizuddin Muhd Muhd Adnan ◽  
Hamwira Yaacob
Keyword(s):  
Affective State ◽  
Computational Modelling ◽  
Eeg Signal ◽  
Statistical Features ◽  
Mobile System ◽  
Eeg Signals ◽  
Simulation Centre

The EEG (Electroencephalogram) from brain signal has been broadly used to reveal human affects based on Valence, Arousal and dominance through computational modelling. Recently, less study on EEG been done in detail, directly to reveal and quantify the stress from affects dynamically based on EEG signal. In addition. There is no study currently perform to identify stress from the features based on EEG signals simultaneously. As for the objective of this paper, this study will only try to do features analysis of stress by comparing the statistical features of affects from the data obtained from three subjects. The data was collected during the trial session for new assembled EEG mobile system at Simulation Centre Akademi Laut Malaysia (ALAM). Subjects consist of three marine pilot who were given tasks to navigate ships in the simulated scenes. Findings and observations from the analysis are reported in this paper.

scholarly journals A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Jordan J. Bird ◽  
Diego R. Faria ◽  
Luis J. Manso ◽  
Anikó Ekárt ◽  
Christopher D. Buckingham
Keyword(s):  
Short Term Memory ◽  
Evolutionary Approach ◽  
Statistical Features ◽  
Short Term ◽  
Adaptive Boosting ◽  
New Approach ◽  
State Classification ◽  
Eeg Data ◽  
Long Short Term Memory ◽  
Machine Interaction

This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks. An evolutionary algorithm is applied to select the most informative features from an initial set of 2550 EEG statistical features. Optimisation of a Multilayer Perceptron (MLP) is performed with an evolutionary approach before classification to estimate the best hyperparameters of the network. Deep learning and tuning with Long Short-Term Memory (LSTM) are also explored, and Adaptive Boosting of the two types of models is tested for each problem. Three experiments are provided for comparison using different classifiers: one for attention state classification, one for emotional sentiment classification, and a third experiment in which the goal is to guess the number a subject is thinking of. The obtained results show that an Adaptive Boosted LSTM can achieve an accuracy of 84.44%, 97.06%, and 9.94% on the attentional, emotional, and number datasets, respectively. An evolutionary-optimised MLP achieves results close to the Adaptive Boosted LSTM for the two first experiments and significantly higher for the number-guessing experiment with an Adaptive Boosted DEvo MLP reaching 31.35%, while being significantly quicker to train and classify. In particular, the accuracy of the nonboosted DEvo MLP was of 79.81%, 96.11%, and 27.07% in the same benchmarks. Two datasets for the experiments were gathered using a Muse EEG headband with four electrodes corresponding to TP9, AF7, AF8, and TP10 locations of the international EEG placement standard. The EEG MindBigData digits dataset was gathered from the TP9, FP1, FP2, and TP10 locations.

Driving Fatigue Detecting Based on EEG Signals of Forehead Area

2017 ◽  
Vol 31 (05) ◽  
pp. 1750011 ◽  
Author(s):  
Zhendong Mu ◽  
Jianfeng Hu ◽  
Jinghai Yin
Keyword(s):  
Brain Region ◽  
Classification Accuracy ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Driving Experience ◽  
Average Accuracy ◽  
Eeg Data ◽  
Driving Fatigue ◽  
Classification Tool ◽  
Fold Cross Validation

This study examined whether prefrontal brain region electroencephalography (EEG) can be used to detect driver's fatigue. The participants were 13 healthy university students with driving experience. They collected EEG experiments in a virtual driving environment, and divided the collected EEG data into normal state and fatigue state. Fuzzy entropy was used for feature extraction; SVM was used as a classification tool. FP1 and FP2 electrode EEG signal was selected from the subject's EEG signal as analysis object. When single electrode signal was used as feature, accuracy of FP1 was higher than FP2, and if mixing FP1 and FP2 as feature, the accuracy is the highest, the average accuracy is 0.85 by 10-fold cross-validation in Prefrontal brain region. Although the signal classification accuracy of the prefrontal brain region is not the highest, from a practical point, the EEG classification accuracy can be used to detect fatigue.

scholarly journals Exploring Sampling in the Detection of Multicategory EEG Signals

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Siuly Siuly ◽  
Enamul Kabir ◽  
Hua Wang ◽  
Yanchun Zhang
Keyword(s):  
Nearest Neighbor ◽  
Optimal Allocation ◽  
Multinomial Logistic Regression ◽  
Support Vector ◽  
Statistical Features ◽  
Eeg Signals ◽  
Ridge Estimator ◽  
Time Period ◽  
Eeg Data ◽  
Machine Leaning

The paper presents a structure based on samplings and machine leaning techniques for the detection of multicategory EEG signals where random sampling (RS) and optimal allocation sampling (OS) are explored. In the proposed framework, before using the RS and OS scheme, the entire EEG signals of each class are partitioned into several groups based on a particular time period. The RS and OS schemes are used in order to have representative observations from each group of each category of EEG data. Then all of the selected samples by the RS from the groups of each category are combined in a one set named RS set. In the similar way, for the OS scheme, an OS set is obtained. Then eleven statistical features are extracted from the RS and OS set, separately. Finally this study employs three well-known classifiers:k-nearest neighbor (k-NN), multinomial logistic regression with a ridge estimator (MLR), and support vector machine (SVM) to evaluate the performance for the RS and OS feature set. The experimental outcomes demonstrate that the RS scheme well represents the EEG signals and thek-NN with the RS is the optimum choice for detection of multicategory EEG signals.

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

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>

scholarly journals Mean Values of Certain Multiplicative Functions and Artin's Conjecture on Primitive Roots

2014 ◽  
Vol Volume 37 ◽  
Author(s):  
Sankar Sitaraman
Keyword(s):  
Dirichlet Series ◽  
Mean Value ◽  
Multiplicative Functions ◽  
New Approach ◽  
Mean Values ◽  
Artin's Conjecture ◽  
International Audience ◽  
The Mean ◽  
Primitive Roots

International audience We discuss how one could study asymptotics of cyclotomic quantities via the mean values of certain multiplicative functions and their Dirichlet series using a theorem of Delange. We show how this could provide a new approach to Artin's conjecture on primitive roots. We focus on whether a fixed prime has a certain order modulo infinitely many other primes. We also give an estimate for the mean value of one such Dirichlet series.

scholarly journals Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection

2020 ◽  
Vol 10 (21) ◽  
pp. 7677
Author(s):  
Gen Li ◽  
Jason J. Jung
Keyword(s):  
Feature Selection ◽  
Eeg Signal ◽  
Emotion Detection ◽  
Eeg Signals ◽  
Training Time ◽  
Eeg Data ◽  
Signal Preprocessing ◽  
The Difference ◽  
Electroencephalogram Eeg ◽  
Signal Similarity

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.

scholarly journals Removal of Artifacts from EEG Signals: A Review

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 987 ◽  
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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