EEG and fMRI agree: Mental arithmetic is the easiest form of imagery to detect

Harrison, A; Noseworthy, Michael D.; Reilly, J.P.; Guan, Weiguang; Connolly, John F.

doi:10.1016/j.concog.2016.10.006

Cited by 14 publications

(9 citation statements)

References 51 publications

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“…Thus, many healthy people cannot cooperate in active paradigms. Of note, however, mental arithmetic seems to generate the most robust activation in the majority of healthy subjects for both EEG and fMRI (Harrison et al, 2017). Obviously, in the present study we used relatively difficult multiplication tasks, and easier ones such as serial 7’s (Steinhauer, Condray & Pless, 2015) might have resulted in a greater fraction of participants being able to comply with our paradigm.…”

Section: Discussionmentioning

confidence: 99%

Automated pupillometry to detect command following in neurological patients: a proof-of-concept study

Vassilieva

Olsen

Peinkhofer

et al. 2019

PeerJ

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Background Levels of consciousness in patients with acute and chronic brain injury are notoriously underestimated. Paradigms based on electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) may detect covert consciousness in clinically unresponsive patients but are subject to logistical challenges and the need for advanced statistical analysis. Methods To assess the feasibility of automated pupillometry for the detection of command following, we enrolled 20 healthy volunteers and 48 patients with a wide range of neurological disorders, including seven patients in the intensive care unit (ICU), who were asked to engage in mental arithmetic. Results Fourteen of 20 (70%) healthy volunteers and 17 of 43 (39.5%) neurological patients, including 1 in the ICU, fulfilled prespecified criteria for command following by showing pupillary dilations during ≥4 of five arithmetic tasks. None of the five sedated and unconscious ICU patients passed this threshold. Conclusions Automated pupillometry combined with mental arithmetic appears to be a promising paradigm for the detection of covert consciousness in people with brain injury. We plan to build on this study by focusing on non-communicating ICU patients in whom the level of consciousness is unknown. If some of these patients show reproducible pupillary dilation during mental arithmetic, this would suggest that the present paradigm can reveal covert consciousness in unresponsive patients in whom standard investigations have failed to detect signs of consciousness.

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Section: Discussionmentioning

confidence: 99%

Automated pupillometry to detect command following in neurological patients: a proof-of-concept study

Vassilieva

Olsen

Peinkhofer

et al. 2019

PeerJ

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“…In the main experiment, we used a MA task that is one of the widely employed cognitive tasks for developing an endogenous BCI system [ 31 , 33 , 66 , 67 , 68 , 69 ]. A significant alpha ERS and a wide-band ERD in the β- and γ-bands were observed for most electrodes during MA, which is consistent with previous studies [ 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Choi

Han

Choi

et al. 2018

Sensors

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Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main experiments where EEGs were measured on the scalp and behind the ears to check the reliability of ear-EEGs as compared to scalp-EEGs. In the preliminary and main experiments, subjects performed eyes-open and eyes-closed tasks, and they performed mental arithmetic (MA) and light cognitive (LC) tasks, respectively. For data analysis, the brain area was divided into four regions of interest (ROIs) (i.e., frontal, central, occipital, and ear area). The preliminary experiment showed that the degree of alpha activity increase of the ear area with eyes closed is comparable to those of other ROIs (occipital > ear > central > frontal). In the main experiment, similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs during MA and LC, and all ROIs showed the mean classification accuracies above 70% required for effective binary communication (MA vs. LC) (occipital = ear = central = frontal). From the results, we demonstrated that ear-EEG can be used to develop an endogenous BCI system based on cognitive tasks without external stimuli, which allows the usability of ear-EEGs to be extended.

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“…To accomplish this, we developed a machine learning model that learns EEG signatures of the ICNs using supervisory labels derived from simultaneously recorded fMRI. The simultaneous EEG-fMRI data were collected from two cohorts of participants that performed two different multi-task paradigms: a dual taskswitching paradigm designed to activate the DMN, CEN and SN [44]; and a multi-task paradigm that cycles through a series of seven tasks, a subset of which rely on the three networks of interest [16]. A large battery of approximately 40M amplitude and phase-based features were computed from the EEG data collected during these tasks.…”

Section: Introductionmentioning

confidence: 99%

Tracking the Brain’s Intrinsic Connectivity Networks in EEG

Shaw

Heisz

et al. 2021

Preprint

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Functional magnetic resonance imaging (fMRI) has identified dysfunctional network dynamics underlying a number of psychopathologies, including post-traumatic stress disorder, depression and schizophrenia. There is tremendous potential for the development of network-based clinical biomarkers to better characterize these disorders. However, to realize this potential requires the ability to track brain networks using a more affordable imaging modality, such as Electroencephalography (EEG). Here we present a novel analysis pipeline capable of tracking brain networks from EEG alone, after training on supervisory signals derived from data simultaneously recorded in EEG and fMRI, while people engaged in various cognitive tasks. EEG-based features were then used to classify three cognitively-relevant brain networks with up to 75\% accuracy. These findings could lead to affordable and non-invasive methods to objectively diagnose brain disorders involving dysfunctional network dynamics, and to track and even predict treatment responses.

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EEG and fMRI agree: Mental arithmetic is the easiest form of imagery to detect

Cited by 14 publications

References 51 publications

Automated pupillometry to detect command following in neurological patients: a proof-of-concept study

Automated pupillometry to detect command following in neurological patients: a proof-of-concept study

On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Tracking the Brain’s Intrinsic Connectivity Networks in EEG

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