Recording and Characterization of EEGs by Using Wearable EEG Device

Inoue, Ryo; Sugi, Takenao; Matsuda, Yoshitaka; Goto, Satoru; Nohira, Haruhiko; Mase, Ryuzo

doi:10.23919/iccas47443.2019.8971564

Cited by 9 publications

(5 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Where the variable a modifies the function over x(t) and variable b modifies the temporal scale of the probing function w. The wavelet function, 𝜑, is stretched down the time axis if an is larger than one, and it touches the function if an is less than one (but still positive) [13].The probing function w can be any of a variety of functions, but it always takes on an oscillating form, hence the word "wavelet." For all values of a. and the term 1 √𝑎 is the normalization factor which ensures the energy is the same [14]. In DWT signal analysis, choosing the right wavelet and the right number of layers of decomposition is crucial [15].…”

Section: Research Methods 31 Methods Forthe Feature Extraction 311 Wa...mentioning

confidence: 99%

Classification of EEG Signal by Using Optimized Quantum Neural Network

Abdul-Zahra

Jawad

Gheni³

et al. 2021

IJEEI

View full text Add to dashboard Cite

In recent years the algorithms of machine learning were used for brain signals identification as a useful technique for diagnosing diseases like Alzheimer's and epilepsy. In this paper, the Electroencephalogram (EEG) signals are classified using an optimized Quantum neural network (QNN) after normalizing these signals. The wavelet transform (WT) and the independent component analysis (ICA) were utilized for feature extraction. These algorithms were used to reduce the dimensions of the data, which is an input to the optimized QNN for the purpose of performing the classification process after the feature extraction process. This research uses an optimized QNN, a form of feedforward neural network (FFNN), to recognize the EEG signals.The Particle swarm optimization (PSO) algorithm was used to optimize the quantum neural network, which improved the training process of the system's performance. The optimized QNN provided us with somewhat faster and more realistic results. According to simulation results, the total classification for ICA is 82.4 percent, while the total classification for WT is 78.43 percent; from these results, using the ICA for feature extraction is better than using WT.

show abstract

Section: Research Methods 31 Methods Forthe Feature Extraction 311 Wa...mentioning

confidence: 99%

Classification of EEG Signal by Using Optimized Quantum Neural Network

Abdul-Zahra

Jawad

Gheni³

et al. 2021

IJEEI

View full text Add to dashboard Cite

show abstract

“…It is an advanced tool for the analysis of neurophysiological data, providing a robust environment for the simulation of brain signals. In addition, use has been made of PhysioNet, a comprehensive platform that offers a vast database of physiological signals and health-related records, thus allowing access to a wide spectrum of data for algorithm testing and validation (Inoue et al, 2019).…”

Section: Description Of Materialsmentioning

confidence: 99%

Innovations in the Acquisition and Simulation of EEG Signals An Integrated Study of Literature Review and Application of Biomedical Technology

Sanclemente,

Vega,

Olarte

2024

Preprint

View full text Add to dashboard Cite

In this research the search for information is carried out in which the multiple applications that biomedical uses in brain signals can be made known. The main objective is to provide the information obtained in the web database that collects the creation of multiple projects based on the arrest and management of a wide variety of electroencephalographic signals, developed in devices comprising BCI systems that control the processing of signals generated by the human brain, in which it is possible to carry out an analysis of multiple information through which one can even get to know disorders and brain diseases that cause a destabilization that prevents the correct functioning of different organs and even in its entirety the peripheral nervous system. Through the feedback of the information is sought to give the creation of a device that meets the fusion of neural signal capture through multiple channels containing filtering stages that allow the reading of the signal obtained from different brain hemispheres

show abstract

“…One primary challenge is designing wearable EEG devices that are comfortable and capable of capturing reliable signals. Accurate placement of EEG electrodes on the scalp is crucial for optimal signal quality [134,135]. Developments in materials science and nanotechnology are leading to more user-friendly and efficient electrodes.…”

Section: Challenges and Opportunities Of Wearable And Seamlessly Inte...mentioning

confidence: 99%

Review of EEG-Based Biometrics in 5G-IoT: Current Trends and Future Prospects

Beyrouthy,

Mostafa,

Roshdy

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The increasing integration of the Internet of Things (IoT) into daily life has led to significant changes in our social interactions. The advent of innovative IoT solutions, combined with the enhanced capabilities and expanded reach of 5G wireless networks, is altering the way humans interact with machines. Notably, the advancement of edge computing, underpinned by 5G networks within IoT frameworks, has markedly extended human sensory perception and interaction. A key biometric within these IoT applications is electroencephalography (EEG), recognized for its sensitivity, cost-effectiveness, and distinctiveness. Traditionally linked to brain–computer interface (BCI) applications, EEG is now finding applications in a wider array of fields, from neuroscience research to the emerging area of neuromarketing. The primary aim of this article is to offer a comprehensive review of the current challenges and future directions in EEG data acquisition, processing, and classification, with a particular focus on the increasing reliance on data-driven methods in the realm of 5G wireless network-supported EEG-enabled IoT solutions. Additionally, the article presents a case study on EEG-based emotion recognition, exemplifying EEG’s role as a biometric tool in the IoT domain, propelled by 5G technology.

show abstract

Recording and Characterization of EEGs by Using Wearable EEG Device

Cited by 9 publications

References 1 publication

Classification of EEG Signal by Using Optimized Quantum Neural Network

Classification of EEG Signal by Using Optimized Quantum Neural Network

Innovations in the Acquisition and Simulation of EEG Signals An Integrated Study of Literature Review and Application of Biomedical Technology

Review of EEG-Based Biometrics in 5G-IoT: Current Trends and Future Prospects

Contact Info

Product

Resources

About