An EEG-fNIRS hybridization technique in the four-class classification of alzheimer’s disease

Cicalese, Pietro Antonio; Li, Rihui; Ahmadi, Mohammad Badri; Wang, Chushan; Francis, Joseph T.; Selvaraj, Sudhakar; Schulz, Paul E.; Zhang, Yingchun

doi:10.1016/j.jneumeth.2020.108618

Cited by 75 publications

(42 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These two scalp-located techniques could be used concurrently, providing a multimodal neuroimaging tool that is able to measure the electrical and associated hemodynamic brain activity [ 20 ]. Multimodal EEG-fNIRS has been already utilized to assess cortical connectivity alterations associated with AD [ 21 ] and to perform a data-driven identification of AD, obtaining superior performances with respect to those obtained with standalone systems [ 22 ]. Notably, probing both the electrical and hemodynamic brain activity, it is possible to have information about the functional hyperemia in response to brain activity (i.e., neurovascular coupling, NC), which is known to be dysregulated in AD [ 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS

Perpetuini

Chiarelli

Filippini

et al. 2020

Entropy

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is characterized by working memory (WM) failures that can be assessed at early stages through administering clinical tests. Ecological neuroimaging, such as Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests to support AD early diagnosis within clinical settings. Multimodal EEG-fNIRS could measure brain activity along with neurovascular coupling (NC) and detect their modifications associated with AD. Data analysis procedures based on signal complexity are suitable to estimate electrical and hemodynamic brain activity or their mutual information (NC) during non-structured experimental paradigms. In this study, sample entropy of whole-head EEG and frontal/prefrontal cortex fNIRS was evaluated to assess brain activity in early AD and healthy controls (HC) during WM tasks (i.e., Rey–Osterrieth complex figure and Raven’s progressive matrices). Moreover, conditional entropy between EEG and fNIRS was evaluated as indicative of NC. The findings demonstrated the capability of complexity analysis of multimodal EEG-fNIRS to detect WM decline in AD. Furthermore, a multivariate data-driven analysis, performed on these entropy metrics and based on the General Linear Model, allowed classifying AD and HC with an AUC up to 0.88. EEG-fNIRS may represent a powerful tool for the clinical evaluation of WM decline in early AD.

show abstract

Section: Introductionmentioning

confidence: 99%

Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS

Perpetuini

Chiarelli

Filippini

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…The mCtCCA provided better classification accuracy in over 91.3% of considered subjects and increased performance by 1.8% on average (p < 0.001) than that of mCCCA as the correlation between the two data sets is likely to be highest with a temporal shift. The overall results demonstrated that the proposed approach provides a classification improvement over the existing methods of discriminating stress conditions in the workplace [26,65] as well as over recent studies that have combined EEG and fNIRS based on motor imagery tasks [67,68], Alzheimer's disease [69], and mental workload [70]. Therefore, the combination of EEG and fNIRS using the proposed approach (mCtCCA) has a significant impact on the better analysis of signals.…”

Section: Discussionmentioning

confidence: 70%

Investigating Frontal Neurovascular Coupling in Response to Workplace Design-Related Stress

et al. 2020

View full text Add to dashboard Cite

This research seeks to examine the impact of workstation types on the coupling of neural and vascular activities of the prefrontal cortex (PFC). The design of the workstations was found to impair the performance, physical and mental health of employees. However, the mechanism underlying cognitive activity involved during workstation design-related stress effects in the PFC has not been fully understood. We used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to simultaneously measure electrical activity and hemoglobin concentration changes in the PFC. The multimodal signal was collected from 23 healthy adult volunteers who completed the Montreal imaging stress task in ergonomic and non-ergonomic workstations. A supervised machine learning method based on temporally embedded canonical correlation analysis (tCCA) was utilized to obtain the association between neural activity and local changes in hemoglobin concentrations to enhance localization and accuracy. The results showed deactivation in alpha power rhythms and oxygenated hemoglobin, as well as declined activation pattern of the fused data in the right PFC at the non-ergonomic workstation. Additionally, all participants at the non-ergonomic workstation experienced a substantial rise in salivary alpha-amylase activity in comparison with the ergonomic workstation, indicating the existence of high-stress levels. The proposed tCCA approach obtains excellent results in discriminating workstation types achieving accuracies of 98.8% and a significant improvement of 8.0% (p < 0.0001) and 9.4% (p < 0.0001) over EEG-only and fNIRS-only, respectively. Our study suggests the use of functional neuroimaging in designing the workplace as it provides critical information on the causes of workplace-related stress. INDEX TERMS Electroencephalography (EEG), ergonomic, functional near-infrared spectroscopy (fNIRS), oxygenated hemoglobin, prefrontal cortex (PFC).

show abstract

“…There are several studies assessing EEG and fNIRS simultaneously to investigate whether the combination of electrophysiological and hemodynamic data enables a better understanding of brain activity and may reveal biomarkers for specific disorders. So far, findings are emerging 28 , 29 as correlations are found throughout several studies. For example, Sun et al investigated the neural correlates of automatic facial expressions when assessing EEG and fNIRS simultaneously 30 .…”

Section: Introductionmentioning

confidence: 81%

Neuronal correlates of spider phobia in a combined fNIRS-EEG study

Rosenbaum

Leehr

Kroczek

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Specific phobia is associated with aberrant brain activation in confrontation paradigms with phobic stimuli. In previous EEG research enhanced event-related potentials (ERPs) in the late-positive potential (LPP) window have been observed. Further, studies with functional near-infrared spectroscopy (fNIRS) and fMRI suggest that spider phobia is associated with enhanced activation within cortical and subcortical areas. In the current study we investigated the neuronal correlates of spider phobia in a combined fNIRS–EEG study. To this end, 37 spider phobic patients (PP) and 32 healthy controls (HC) underwent a symptom provocation paradigm during which subjects watched video clips of spiders and domestic animals (confrontation phase) after being primed on the content of the video (anticipation phase). Simultaneously, fNIRS, EEG, electromyography (EMG), electrocardiography and behavioral measures were assessed. Results showed increased LPP amplitudes, increased hemodynamic responses in the cognitive control network, and increased EMG activity and heart rate during spider conditions in PP in comparison to HC. Furthermore, in behavioral ratings PP showed higher emotional distress and avoidance. Behavioral ratings, fNIRS and EEG data showed positive correlations on a between-subject as well as on a within-subject level. Our results merge the existing data on neurophysiological correlates of phobic stimulus processing in hemodynamic and electrophysiological research and extend those of static visual material (pictures) to dynamic visual material (videos).

show abstract

An EEG-fNIRS hybridization technique in the four-class classification of alzheimer’s disease

Cited by 75 publications

References 31 publications

Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS

Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS

Investigating Frontal Neurovascular Coupling in Response to Workplace Design-Related Stress

Neuronal correlates of spider phobia in a combined fNIRS-EEG study

Contact Info

Product

Resources

About