Tracking of EEG activity using topographic maps

Hooi, Lim Seng; Nisar, Humaira; Voon, Yap Vooi

doi:10.1109/icsipa.2015.7412206

Cited by 17 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The extracted features in the previous section are considered as colour mapping parameters. The bilinear interpolation method is used for navigating the values between the electrodes [ 13 , 27 ]. In this study, the brain topographic map was extracted by the MNE library in Python software.…”

Section: Stacked Autoencoder Topographic Map (Saetm)mentioning

confidence: 99%

“…Te F1 is a combination of the accuracy and recall criteria, which is obtained according to equation (13).…”

Section: Classifer Evaluationmentioning

confidence: 99%

“…One of the common methods in visualizing EEG signal is quantitative EEG (QEEG) analysis, that is well known as topographic brain mapping, which provides a cost-effective and practical method for spatial evaluating of neural activities. This method represents structural and effective communication in nerve cells, nerve complexes, and brain structure [ 13 ]. Brain topography by the QEEG technique is obtained by extracting features from the EEG signal.…”

Section: Introductionmentioning

confidence: 99%

“…Brain topography by the QEEG technique is obtained by extracting features from the EEG signal. Today, with the advancement of topographic maps, the analysis of EEG provides a comprehensive exploration of temporal and spatial characteristics simultaneously [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Extracting a Novel Emotional EEG Topographic Map Based on a Stacked Autoencoder Network

Vafaei

Rahatabad

Setarehdan

et al. 2023

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Emotion recognition based on brain signals has increasingly become attractive to evaluate human’s internal emotional states. Conventional emotion recognition studies focus on developing machine learning and classifiers. However, most of these methods do not provide information on the involvement of different areas of the brain in emotions. Brain mapping is considered as one of the most distinguishing methods of showing the involvement of different areas of the brain in performing an activity. Most mapping techniques rely on projection and visualization of only one of the electroencephalogram (EEG) subband features onto brain regions. The present study aims to develop a new EEG-based brain mapping, which combines several features to provide more complete and useful information on a single map instead of common maps. In this study, the optimal combination of EEG features for each channel was extracted using a stacked autoencoder (SAE) network and visualizing a topographic map. Based on the research hypothesis, autoencoders can extract optimal features for quantitative EEG (QEEG) brain mapping. The DEAP EEG database was employed to extract topographic maps. The accuracy of image classifiers using the convolutional neural network (CNN) was used as a criterion for evaluating the distinction of the obtained maps from a stacked autoencoder topographic map (SAETM) method for different emotions. The average classification accuracy was obtained 0.8173 and 0.8037 in the valence and arousal dimensions, respectively. The extracted maps were also ranked by a team of experts compared to common maps. The results of quantitative and qualitative evaluation showed that the obtained map by SAETM has more information than conventional maps.

show abstract

Section: Stacked Autoencoder Topographic Map (Saetm)mentioning

confidence: 99%

“…Te F1 is a combination of the accuracy and recall criteria, which is obtained according to equation (13).…”

Section: Classifer Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Extracting a Novel Emotional EEG Topographic Map Based on a Stacked Autoencoder Network

Vafaei

Rahatabad

Setarehdan

et al. 2023

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Different approaches to analyze brain dynamics at scalp, sources and networks during picture naming task are discussed in [25]. Spatiotemporal dynamics of electric potentials have been characterized using block-matching motion estimation [26] and calculating peak amplitude trajectories [27] from topographic maps.…”

Section: Introductionmentioning

confidence: 99%

Characterization of spatiotemporal dynamics in EEG data during picture naming with optical flow patterns

Volpert

Xu²,

Tchechmedjiev³

et al. 2022

Preprint

View full text Add to dashboard Cite

We present an analysis of the spatiotemporal dynamics of the oscillations in the electric potential that arises from neural activity. Depending on the frequency and phase of oscillations, these dynamics can be characterized as standing waves or as out-of-phase and modulated waves, which represent a combination of standing and moving waves. We characterize these dynamics as optical flow patterns, in terms of sources, sinks, spirals and saddles. Analytical and numerical solutions are compared with real EEG data acquired during a picture-naming task. Analytical approximation of standing waves allows us to establish some properties of pattern location and number. Namely, sources and sinks have mainly the same location, while saddles are located between them. The number of saddles correlates with the sum of all the other patterns. These properties are confirmed in both the simulated and real EEG data.

show abstract

Visualizing the Electroencephalography Signal Discrepancy When Maintaining Social Distancing: EEG-Based Interactive Moiré Patterns

Li,

Yang,

Zhang

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Tracking of EEG activity using topographic maps

Cited by 17 publications

References 10 publications

Extracting a Novel Emotional EEG Topographic Map Based on a Stacked Autoencoder Network

Extracting a Novel Emotional EEG Topographic Map Based on a Stacked Autoencoder Network

Characterization of spatiotemporal dynamics in EEG data during picture naming with optical flow patterns

Visualizing the Electroencephalography Signal Discrepancy When Maintaining Social Distancing: EEG-Based Interactive Moiré Patterns

Contact Info

Product

Resources

About