scholarly journals Design and Characterization of an EEG-Hat for Reliable EEG Measurements

Micromachines ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 635
Author(s):  
Takumi Kawana ◽  
Yuri Yoshida ◽  
Yuta Kudo ◽  
Chiho Iwatani ◽  
Norihisa Miki
Keyword(s):  
Simultaneous Measurement ◽  
Eeg Signals ◽  
High Quality ◽  
Good Contact ◽  
Shutter Mechanism ◽  
Electroencephalogram Eeg

In this study, a new hat-type electroencephalogram (EEG) device with candle-like microneedle electrodes (CMEs), called an EEG-Hat, was designed and fabricated. CMEs are dry EEG electrodes that can measure high-quality EEG signals without skin treatment or conductive gels. One of the challenges in the measurement of high-quality EEG signals is the fixation of electrodes to the skin, i.e., the design of a good EEG headset. The CMEs were able to achieve good contact with the scalp for heads of different sizes and shapes, and the EEG-Hat has a shutter mechanism to separate the hair and ensure good contact between the CMEs and the scalp. Simultaneous measurement of EEG signals from five measurement points on the scalp was successfully conducted after a simple and brief setup process. The EEG-Hat is expected to contribute to the advancement of EEG research.

scholarly journals Estimation of Number of Levels of Scaling the Principal Components in Denoising EEG Signals

2021 ◽  
Vol 14 (01) ◽  
pp. 425-433
Author(s):  
B. Krishna Kumar
Keyword(s):  
Principal Component Analysis ◽  
Standard Method ◽  
Principal Components ◽  
Principal Component ◽  
Data Sets ◽  
Eeg Signal ◽  
Eeg Signals ◽  
High Quality ◽  
Electroencephalogram Eeg ◽  
Ocular Artifacts

Electroencephalogram (EEG) is basically a standard method for investigating the brain’s electrical action in diverse psychological and pathological states. Investigation of Electroencephalogram (EEG) signal is a tough task due to the occurrence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. By and large EEG signals falls in the range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex[1]. In this research paper, Principal Component Analysis is employed in denoising the EEG signals. This paper explains up to what level the scaling of principal components have to be done. This paper explains the number of levels of scaling the principal components to get the high quality EEG signal. The work has been carried out on different data sets and later estimated the SNR.

Microstructural Characterization of Au-Ge-Ni based ohmic contacts to GaAs/AlGaAs modfet device

1987 ◽  
Vol 45 ◽  
pp. 326-327
Author(s):  
A.K. Rai ◽  
A.K. Petford-Long ◽  
A. Ezis ◽  
D.W. Langer
Keyword(s):  
Contact Resistance ◽  
Ohmic Contact ◽  
Ohmic Contacts ◽  
Microstructural Characterization ◽  
Almost Everywhere ◽  
Spacer Layer ◽  
Good Contact ◽  
The Relationship ◽  
Lower Contact

Considerable amount of work has been done in studying the relationship between the contact resistance and the microstructure of the Au-Ge-Ni based ohmic contacts to n-GaAs. It has been found that the lower contact resistivity is due to the presence of Ge rich and Au free regions (good contact area) in contact with GaAs. Thus in order to obtain an ohmic contact with lower contact resistance one should obtain a uniformly alloyed region of good contact areas almost everywhere. This can possibly be accomplished by utilizing various alloying schemes. In this work microstructural characterization, employing TEM techniques, of the sequentially deposited Au-Ge-Ni based ohmic contact to the MODFET device is presented.The substrate used in the present work consists of 1 μm thick buffer layer of GaAs grown on a semi-insulating GaAs substrate followed by a 25 Å spacer layer of undoped AlGaAs.

scholarly journals Editorial for Special Issue “Historical Mineral Pigments”

Minerals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 237
Author(s):  
Carolina Cardell ◽  
Jose Santiago Pozo-Antonio
Keyword(s):  
Chemical Characterization ◽  
Glass Enamel ◽  
Special Issue ◽  
High Quality ◽  
Physical Chemical ◽  
Scientific Disciplines ◽  
Natural And Synthetic

The physical–chemical characterization of natural and synthetic historical inorganic and mineral pigments, which may be found embedded in paintings (real or mock-ups), glass, enamel, ceramics, beads, tesserae, etc., as well as their alteration under different decay scenarios, is a demanding line of investigation. This field of research is now both well established and dynamic, as revealed by the numerous publications in high-quality journals of varied scientific disciplines. [...]

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

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

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

2019 ◽  
Vol 9 (11) ◽  
pp. 326 ◽  
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.

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

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850051 ◽  
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.

Growth and Characterization of High Quality Epitaxial GaN on ZnO(0001) by Reactive Molecular Epitaxy

1998 ◽  
Vol 264-268 ◽  
pp. 1201-1204
Author(s):  
F. Hamdani ◽  
M. Yeadon ◽  
David John Smith ◽  
H. Tang ◽  
W. Kim ◽  
...  
Keyword(s):  
High Quality

Growth and characterization of high-quality GaInAs/AlInAs triple wells

2000 ◽  
Vol 209 (1) ◽  
pp. 8-14 ◽  
Author(s):  
Q.K Yang ◽  
J.X Chen ◽  
A.Z Li
Keyword(s):  
High Quality

Growth and characterization of high-quality MOCVD AlGaAs/GaAs single quantum wells

1984 ◽  
Vol 68 (1) ◽  
pp. 398-405 ◽  
Author(s):  
R.D. Dupuis ◽  
R.C. Miller ◽  
P.M. Petroff
Keyword(s):  
Quantum Wells ◽  
Single Quantum ◽  
High Quality

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

2020 ◽  
Vol 2020 (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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