Analysis of EEG Signal Processing Techniques based on Spectrograms

Ramos, Ricardo R.; Olvera, José Arturo; Olmos, Iván

doi:10.13053/rcs-145-1-12

Cited by 22 publications

(4 citation statements)

References 24 publications

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“…In EEG analysis, the spectrogram plays a crucial role by offering a compelling and intuitive way to examine and interpret the complex dynamics of brain electrical activity [30]. Creating a spectrogram involves dividing the EEG signal into short, overlapping segments, applying the Fourier Transform to each segment to obtain its frequency spectrum, and then plotting these spectra as a function of time [31]. The result is a two-dimensional graph with time on the horizontal axis and frequency on the vertical axis.…”

Section: Spectrogrammentioning

confidence: 99%

Decoding Brain’s Electrical Activity: Leveraging Hilbert Transforming Techniques for EEG Analysis

Kaplanoglu

2024

COJEC

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Section: Spectrogrammentioning

confidence: 99%

Decoding Brain’s Electrical Activity: Leveraging Hilbert Transforming Techniques for EEG Analysis

Kaplanoglu

2024

COJEC

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“…Our sampling frequency is 2 kHz, but some data may be lost when converting the original signal to a spectral image. Spectrograms with too low spatial resolution have been shown to impair predictive model performance (35). Additionally, our dataset consisting of just over 1,000 images is very small compared to the 14 million image database usually used for CNN training (34).…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

Improved predictive diagnosis of diabetic macular edema based on hybrid models: an observational study

Hughes-Cano

Quiroz‐Mercado

Hernández-Zimbrón

et al. 2023

Preprint

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Diabetic Macular Edema (DME) is the most common sight-threatening complication of type 2 diabetes. Our goal was to develop an alternative method to optical coherence tomography (OCT) for DME diagnosis by introducing spectral information derived from spontaneous electroretinogram (ERG) signals as a single input or combined with eye fundus. To this end, an observational study was completed (n = 233 participants). Basal ERGs were used to generate scalograms and spectrograms via Wavelet and Fourier transforms, respectively. Using transfer learning, distinct Convolutional Neural Networks (CNN) were trained as classifiers for DME using OCT, scalogram, spectrogram, and fundus images. Input data were randomly split into training and test sets with a proportion of 80% to 20%, respectively. The top performers for each input type were selected, OpticNet-71 for OCT and DenseNet-201 for fundus and non-evoked ERG-derived scalograms, to generate a combined model by assigning different weights for each of the selected models. Model validation was performed using a dataset alien to the training phase of the models. None of the models powered by non-evoked ERG-derived input performed well. Metrics of the best hybrid models were all above 0.81 for fundus combined with non-evoked ERG-derived information; and above 0.85 for OCT combined with non-evoked ERG-derived scalogram images. These data show that the spontaneous ERG-based model improves all the performance metrics of the fundus and OCT-based models, with the exception of sensitivity for the OCT model, to predict DME. Combining non-evoked ERG with OCT represents an improvement to the existing OCT-based models, and combining non-evoked ERG with fundus is a reliable and economical alternative for the diagnosis of DME in underserved areas where OCT is unavailable.

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“…2) Objective data: The obtained EEG signal will be processed and analyzed through re-processing, feature extraction, and classification [47], [48]. Fig.…”

Section: ) Subjective Datamentioning

confidence: 99%

Electroencephalography Signal Analysis for Virtual Reality Sickness: Head-mounted Display and Screen-based

Suwandi,

Khotimah,

Haryanto

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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The use of virtual reality (VR) technology is growing in the current era of the COVID-19 pandemic. However, the use of VR is causing problems for its users. Some symptoms, such as nausea, headache, and eye strain, are felt after using VR. These symptoms are called VR sickness. This study used electroencephalography (EEG) to record participants' brain activity changes when experiencing VR sickness. Participants are given VR impressions via screens and head-mounted displays (HMD). In addition, the subject also filled out a simulator sickness questionnaire (SSQ) before and after being given a VR. Brain waves in the alpha frequency range (8 Hz-13 Hz) and low beta frequency (13 Hz-21 Hz) were analyzed through the power spectral density (PSD). From the SSQ results, participants who saw VR through the screen experienced increased nausea symptoms. On the other hand, participants who saw VR through HMD experienced an increase in nausea and oculomotor symptoms (p

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Analysis of EEG Signal Processing Techniques based on Spectrograms

Cited by 22 publications

References 24 publications

Decoding Brain’s Electrical Activity: Leveraging Hilbert Transforming Techniques for EEG Analysis

Decoding Brain’s Electrical Activity: Leveraging Hilbert Transforming Techniques for EEG Analysis

Improved predictive diagnosis of diabetic macular edema based on hybrid models: an observational study

Electroencephalography Signal Analysis for Virtual Reality Sickness: Head-mounted Display and Screen-based

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