Abstract
A key objective in systems and cognitive neuroscience is to establish associations between behavioral measures and concurrent neuronal activity. Single-trial analysis has been proposed as a novel method for characterizing such correlates by first extracting neural components that maximally discriminate trials on a categorical variable, (e.g., hard vs. easy, correct vs. incorrect etc.), and then correlate those components to a continues dependent variable of interest, e.g., reaction time, difficulty Index, etc. However, often times in experiment design it is difficult to either define meaningful categorical variables, or to record enough trials for the method to extract the discriminant components. Experiments designed for the study of the effects of stimulus presentation modality in working memory provide such a scenario, as will be exemplified. In this paper, we proposed a new approach to single-trial analysis in which we directly extract neural activity that maximally correlates to single-trial manual response times; eliminating the need to define an arbitrary categorical variable. We demonstrate our method on real electroencephalography (EEG) data recordings from the study of stimulus presentation modality effect (SPME).
Highlights
A common challenge in the study of cognitive systems is to identify neural correlates of the different cognitive functions
Time window selection For each training dataset, D of modality m, and frequencyband F we identified candidate time-windows associated with correlation peaks in the respective component correlation trace (CCT)
Similar to the CCMs obtained in the Fα1 band, trials with faster responses are associated with lower amplitudes of their corresponding correlated components and vice versa
Summary
A common challenge in the study of cognitive systems is to identify neural correlates of the different cognitive functions. The underlying neural activity is often measured using multi-channel electroencephalography (EEG), while the cognitive function is characterized behaviorally; typically by recording subjects’ responses to external stimuli during performance of a task designed to elicit the cognitive function under study. To increase the signalto-noise ratio of these attributes, they are often obtained by averaging across multiple-trials These mean values are correlated with a behavioral/psychological parameter of interest (individual subject characteristic or performance measure). These methods require a priori decisions regarding which recording channels, time points and frequency bands are more likely to capture the neuronal activity of interest; which is often not the case, in particular in novel paradigms. Traditional analysis methods are limited to identifying signal parameter modulation across subjects, whereas in typical experiments it is the instantaneous variations in behavioral and electrophysiological parameters that best capture the psychological phenomenon under investigation (e.g., the recognition of a particular stimulus)
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