Coherence Analysis of EEG Signal Using Power Spectral Density

Unde, Sukhada A.; Shriram, Revati

doi:10.1109/csnt.2014.181

Cited by 28 publications

(13 citation statements)

References 6 publications

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“…PSD is a measure of the mean square value of a random variable, which is the average power scale per unit frequency. Its advantage is the transformation of EEG waves with amplitude varying with time into a spectrum of EEG power varying with frequency, thus allowing visualization of the distribution and transformation of EEG rhythms [ 36 , 43 ].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

Zhong

Wang

et al. 2023

Brain Sciences

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Depression has become one of the most common mental illnesses, causing serious physical and mental harm. However, there remain unclear and uniform physiological indicators to support the diagnosis of clinical depression. This study aimed to use machine learning techniques to investigate the abnormal multidimensional EEG features in patients with depression. Resting-state EEG signals were recorded from 41 patients with depression and 34 healthy controls. Multiple dimensional characteristics were extracted, including power spectral density (PSD), fuzzy entropy (FE), and phase lag index (PLI). These three different dimensional characteristics with statistical differences between two groups were ranked by three machine learning algorithms. Then, the ranked characteristics were placed into the classifiers according to the importance of features to obtain the optimal feature subset with the highest classification accuracy. The results showed that the optimal feature subset contained 86 features with the highest classification accuracy of 98.54% ± 0.21%. According to the statistics of the optimal feature subset, PLI had the largest number of features among the three categories, and the number of beta features was bigger than other rhythms. Moreover, compared to the healthy controls, the PLI values in the depression group increased in theta and beta rhythms, but decreased in alpha1 and alpha2 rhythms. The PSD of theta and beta rhythms were significantly greater in depression group than that in healthy controls, and the FE of beta rhythm showed the same trend. These findings indicate that the distribution of abnormal multidimensional features is potentially useful for the diagnosis of depression and understanding of neural mechanisms.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

Zhong

Wang

et al. 2023

Brain Sciences

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“…PSD is a frequency response of any signal irrespective of periodic or random signal data and it signifies the average power distribution as a function of frequency. Here Welch's power spectral density estimation [ [27] , [28] , [29] ] was used. Fig.…”

Section: Resultsmentioning

confidence: 99%

Piezoelectric point-of-care biosensor for the detection of SARS-COV-2 (COVID-19) antibodies

Mandal

Indaleeb

Younan

et al. 2022

Sensing and Bio-Sensing Research

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“…Figure 1 shows the block diagram of whole process. EEG signals recorded in this paper are based on the international 10-20 system [13] which are AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, and AF4 respectively. Signals were recorded at 128 Hz sampling rate and they are spontaneous signals as these signals have rhythmicity.…”

Section: Methodology and Data Acquisitionmentioning

confidence: 99%

“…In this contribution EEG data is collected from brain using EMOTIV Headset [13] with fourteen channel electrodes. The main benefit of using emotive headset is that it provides better portability along with providing a noninvasive medium for collection of EEG data.…”

Section: Introductionmentioning

confidence: 99%

Temporal based EEG Signals Classification for Talocrural and Knee Joint Movements using Emotive Head Set

Malik

Iqbal

Tiwana

2015

JBEMi

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Recent developments in Brain Computer Interfacing (BCI) and neuroprosthetics have played a vital role for disable people to expect better life quality. In this contribution Electroencephalographic (EEG) signals acquired from six healthy test subjects, are used for the offline analysis of BCI through classification of four lower limb movements including talocrural (ankle) joint dorsi-planter flexion and knee joint extension-flexion. Fourteen channel Emotive EPOC head set is used to acquire EEG signals from sensorimotor cortex area of brain, using a particular data acquisition timeline protocol. Features are extracted in time domain from raw EEG data. Power spectral density, variance, mean value and kurtosis features are applied on raw EEG signals. Multiple classification algorithms are implemented for discrimination of four lower limb movements within data set. The paper uses Quadratic discriminant analysis, Naïve Bayes and Support vector machine classifiers to stratify the movement intent of lower limb. Maximum classification accuracies achieved through various classifiers are; 86.35% with average band power & QDA, 84.38% with mean value & QDA and 78.13% with power spectral density & Quadratic-SVM. The presented findings are optimistic in making the path easier towards the development of BCIs with rich EEG based control signals using noninvasive technology.

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Coherence Analysis of EEG Signal Using Power Spectral Density

Cited by 28 publications

References 6 publications

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

Piezoelectric point-of-care biosensor for the detection of SARS-COV-2 (COVID-19) antibodies

Temporal based EEG Signals Classification for Talocrural and Knee Joint Movements using Emotive Head Set

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