EEG Decoding Reveals the Strength and Temporal Dynamics of Goal-Relevant Representations

Hubbard, Jason; Kikumoto, Atsushi; Mayr, Ulrich

doi:10.1038/s41598-019-45333-6

Cited by 49 publications

(47 citation statements)

References 44 publications

(64 reference statements)

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“…Our new complexity method for non-linear dynamic analysis for isolated EEG frequency bands and intensity analysis. This work added to the philosophy for decoding the amplitude and temporal dynamics has embedded information 48 and how amplitude of the instantaneous frequency is related to unconsciousness 49 . However, our work demonstrates an elegant method that combines these philosophies to extract embedded information of the instantaneous frequency's intensity (not amplitude) temporal dynamics and this relationships to impaired neuronal firing/neuronal isolation.…”

Section: Discussionmentioning

confidence: 99%

Activation Complexity: A Cognitive Impairment Tool for Characterizing Neuro-isolation

Napoli

Demas

Stephens

et al. 2020

Sci Rep

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Electroencephalography (EEG) is a method for recording electrical activity, indicative of cortical brain activity from the scalp. EEG has been used to diagnose neurological diseases and to characterize impaired cognitive states. When the electrical activity of neurons are temporally synchronized, the likelihood to reach their threshold potential for the signal to propagate to the next neuron, increases. This phenomenon is typically analyzed as the spectral intensity increasing from the summation of these neurons firing. Non-linear analysis methods (e.g., entropy) have been explored to characterize neuronal firings, but only analyze temporal information and not the frequency spectrum. By examining temporal and spectral entropic relationships simultaneously, we can better characterize how neurons are isolated, (the signal's inability to propagate to adjacent neurons), an indicator of impairment. A novel time-frequency entropic analysis method, referred to as Activation Complexity (AC), was designed to quantify these dynamics from key EEG frequency bands. The data was collected during a cognitive impairment study at NASA Langley Research Center, involving hypoxia induction in 49 human test subjects. AC demonstrated significant changes in EEG firing patterns characterize within explanatory (p < 0.05) and predictive models (10% increase in accuracy). The proposed work sets the methodological foundation for quantifying neuronal isolation and introduces new potential technique to understand human cognitive impairment for a range of neurological diseases and insults.Electroencephalography (EEG) detects the electrical activity of the brain and analysis of EEG permits tracking variations in brain wave patterns. EEG analysis provides information about a person's cognitive state such as response inhibition, level of concentration, arousal, and even diagnostic information regarding diseases such as Alzheimer's, post-cardiac arrest syndrome (hypoxic encephalopathies), and epilepsy 1-6 . There are many types of analyses designed to extract features from EEG signals that examine coherence, intensity of frequency bands, signal entropy, coupling, and source localization to acquire information about cognitive states 2,7 . These extracted EEG features are then used as the foundation for explanatory and predictive modeling. Typically, two or more of these features are utilized to generate a feature space for predictive models that can predict epilepsy, hypoxia, etc. 2 . Thus, capturing these new EEG features is paramount to uncovering nascent patterns that provide further insight into the complexities of the human brain and distinguishing impairments.Literature has demonstrated that conditions like hypoxia, Alzheimer's, epilepsy, and other neurological issues cause neuronal impairments that change firing patterns. Modification of firing can potentially occur at the intracellular level of an individual neuron or at the intercellular level in how neurons propagate information to each other (neuronal interactions). However, the non-line...

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Section: Discussionmentioning

confidence: 99%

Activation Complexity: A Cognitive Impairment Tool for Characterizing Neuro-isolation

Napoli

Demas

Stephens

et al. 2020

Sci Rep

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“…43 For that reason, the use of cluster-based non-parametric permutation methods is widespread, not only in fMRI [44][45][46][47] but more recently also in M/EEG studies. [48][49][50][51] In our study, a nonparametric cluster-based permutation approach, proposed in 43 for fMRI data, was adapted and implemented for the statistical analysis.…”

Section: Discussionmentioning

confidence: 99%

Multivariate pattern analysis techniques for electroencephalography data to study interference effects

López-García

Sobrado

Peñalver

et al. 2019

Preprint

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A central challenge in cognitive neuroscience is to understand the neural mechanisms that underlie the capacity to control our behavior according to internal goals. Flanker tasks, which require responding to stimuli surrounded by distracters that trigger incompatible action tendencies, are frequently used to measure this conflict. Even though the interference generated in these situations has been broadly studied, multivariate analysis techniques can shed new light into the underlying neural mechanisms. The current study is an initial approximation to adapt an interference Flanker paradigm embedded in a Demand-Selection Task to a format that allows measuring concurrent high-density electroencephalography. We used multivariate pattern analysis (MVPA) to decode conflictrelated neural processes associated with congruent or incongruent target events in a time-frequency resolved way. Our results replicate findings obtained with other analysis approaches and offer new information regarding the dynamics of the underlying mechanisms, which show signs of reinstantiation. Our findings, some of which could not had been obtained with classic analytical strategies, open novel avenues of research.

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“…For go-trials, the flow of activated representations was highly consistent with our previous results. Rule information appeared in the pre-stimulus period, stimulus information peaked shortly after the stimulus appeared, followed by the emergence of response information (Hubbard, Kikumoto, & Mayr, 2019;Kikumoto & Mayr, 2020). Importantly, conjunctive information was present throughout the entire response-selection period.…”

Section: Experiments 1 Behaviormentioning

confidence: 99%

The Role of Conjunctive Representations in Regulating Actions

Kikumoto

Mayr

2020

Preprint

Self Cite

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Prominent theories of action control propose that conjunctive representations, which integrate task-relevant features in a nonlinear manner, are critical for successful action control. Thus, in order to stop an initiated action, which is a key aspect of self-control, conjunctive representations should be the primary target of the stopping process. We tested this hypothesis by combining a rule-based action selection task with the stop-signal paradigm.Participants selected actions based on abstract stimulus-response rules and occasionally received a stop-signal as a prompt to halt the intended action. Using time-resolved representational similarity analysis of the EEG signal, we decoded both orthogonal, constituent action-relevant representations (rules, stimuli, and responses) and conjunctions of these features in a time-resolved manner and on the level of single trials. In Exp. 1, where a short stop-signal interval (100 ms) ensured high stopping success, simple action-relevant features and their nonlinear conjunction were robustly expressed in the EEG signal. Importantly, the conjunctive representation was selectively suppressed on stop trials. In Exp. 2, an adaptive staircase targeting a stopping success of 50%. Here, conjunctions were selectively suppressed on successful stop trials compared to both go and failed stop trials. Moreover, the strength of conjunctive representations at the time of the stop-signal uniquely predicted stopping failures.Combined, these results clarify that the stopping process does not just target motor output representations. Rather, conjunctive representations seem to be critical for selecting a specific action and for that reason also need to be suppressed by the stopping process in order to cancel intended actions.

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EEG Decoding Reveals the Strength and Temporal Dynamics of Goal-Relevant Representations

Cited by 49 publications

References 44 publications

Activation Complexity: A Cognitive Impairment Tool for Characterizing Neuro-isolation

Activation Complexity: A Cognitive Impairment Tool for Characterizing Neuro-isolation

Multivariate pattern analysis techniques for electroencephalography data to study interference effects

The Role of Conjunctive Representations in Regulating Actions

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