Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges

Zorzos, Ioannis; Kakkos, Ioannis; Ventouras, E.; Matsopoulos, George K.

doi:10.3390/signals2030024

Cited by 19 publications

(17 citation statements)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…EEG-based source modeling majorly suffers from an ill-posed inverse problem and can also result in misleading activity patterns due to, for instance, low SNR, unrealistic head models, invalid constraints, and so on. More accurate EEG source localization requires digitized electrode positions and individual anatomical scans of participants, which can diminish source estimation uncertainty (Shirazi and Huang, 2019; Michel and Brunet, 2019; Zorzos et al, 2021) but were not available in our study. Therefore, EEG source estimates should be interpreted with caution.…”

Section: Discussionmentioning

confidence: 99%

Early visual cortex tracks speech envelope in the absence of visual input

Bednaya

Mirkovic²,

Berto

et al. 2022

Preprint

View full text Add to dashboard Cite

Neural entrainment to continuous speech is typically observed within the language network and can be modulated by both low-level acoustic features and high-level meaningful linguistic units (e.g., phonemes, phrases, and sentences). Recent evidence showed that visual cortex may entrain to speech envelope, however its putative role in the hierarchy of speech processing remains unknown. We tested blindfolded participants who listened to semantically meaningful or meaningless stories, either in quiet or embedded in multi-talker babble noise. Entrainment to speech was assessed with forward linear modeling of participants' EEG activity. We investigated (1) low-level acoustic effects by contrasting neural tracking of speech presented in quiet or noise and (2) high-level linguistic effects by contrasting neural tracking to meaningful or meaningless stories. Results showed that envelope tracking was enhanced and delayed for speech embedded in noise compared to quiet. When semantic information was missing, entrainment to speech envelope was fastened and reduced. Source modeling revealed that envelope tracking engaged wide neural networks beyond the auditory cortex, including early visual cortex. Surprisingly, while no clear influence of semantic content was found, the magnitude of visual cortex entrainment was affected by low-level features. The decrease of sound SNR-level dampened visual cortex tracking, suggesting an active suppressing mechanism in challenging listening conditions. Altogether, these findings provide further evidence of a functional role of early visual cortex in the entrainment to continuous speech.

show abstract

Section: Discussionmentioning

confidence: 99%

Early visual cortex tracks speech envelope in the absence of visual input

Bednaya

Mirkovic²,

Berto

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The solution to the forward problem requires an electrical source configuration, which represents the activated brain neurons, the coordinates of the sensor electrodes and the electrode alignment on the head model. The forward modeling process considers the various head tissues (white and grey matter, cerebrospinal fluid, skull bone, and skin) which express different conductivity of electrical activity [ 38 , 39 ].…”

Section: Methodsmentioning

confidence: 99%

“…Several variables, including head-modeling errors, source-modeling problems, and EEG noise (external or biological), might affect how accurately a source can be found [ 44 ]. There is no unique solution to the inverse problem, hence, mathematical constraints prior to the source estimation are required [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

EEG Network Analysis in Epilepsy with Generalized Tonic–Clonic Seizures Alone

et al. 2022

View full text Add to dashboard Cite

Many contradictory theories regarding epileptogenesis in idiopathic generalized epilepsy have been proposed. This study aims to define the network that takes part in the formation of the spike-wave discharges in patients with generalized tonic–clonic seizures alone (GTCSa) and elucidate the network characteristics. Furthermore, we intend to define the most influential brain areas and clarify the connectivity pattern among them. The data were collected from 23 patients with GTCSa utilizing low-density electroencephalogram (EEG). The source localization of generalized spike-wave discharges (GSWDs) was conducted using the Standardized low-resolution brain electromagnetic tomography (sLORETA) methodology. Cortical connectivity was calculated utilizing the imaginary part of coherence. The network characteristics were investigated through small-world propensity and the integrated value of influence (IVI). Source localization analysis estimated that most sources of GSWDs were in the superior frontal gyrus and anterior cingulate. Graph theory analysis revealed that epileptic sources created a network that tended to be regularized during generalized spike-wave activity. The IVI analysis concluded that the most influential nodes were the left insular gyrus and the left inferior parietal gyrus at 3 and 4 Hz, respectively. In conclusion, some nodes acted mainly as generators of GSWDs and others as influential ones across the whole network.

show abstract

“…Electrical source imaging (ESI) is also useful for noise removal from EEG signals. It is also increasing in the treatment of neurosciences such as epilepsy and in clinical applications [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Few-Electrode EEG from the Wearable Devices Using Domain Adaptation for Depression Detection

Lian

et al. 2022

Biosensors

View full text Add to dashboard Cite

Nowadays, major depressive disorder (MDD) has become a crucial mental disease that endangers human health. Good results have been achieved by electroencephalogram (EEG) signals in the detection of depression. However, EEG signals are time-varying, and the distributions of the different subjects’ data are non-uniform, which poses a bad influence on depression detection. In this paper, the deep learning method with domain adaptation is applied to detect depression based on EEG signals. Firstly, the EEG signals are preprocessed and then transformed into pictures by two methods: the first one is to present the three channels of EEG separately in the same image, and the second one is the RGB synthesis of the three channels of EEG. Finally, the training and prediction are performed in the domain adaptation model. The results indicate that the domain adaptation model can effectively extract EEG features and obtain an average accuracy of 77.0 ± 9.7%. This paper proves that the domain adaptation method can effectively weaken the inherent differences of EEG signals, making the diagnosis of different users more accurate.

show abstract

Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges

Cited by 19 publications

References 96 publications

Early visual cortex tracks speech envelope in the absence of visual input

Early visual cortex tracks speech envelope in the absence of visual input

EEG Network Analysis in Epilepsy with Generalized Tonic–Clonic Seizures Alone

Few-Electrode EEG from the Wearable Devices Using Domain Adaptation for Depression Detection

Contact Info

Product

Resources

About