SPoC: A novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters

Abstract

Previously, modulations in power of neuronal oscillations have been functionally linked to sensory, motor and cognitive operations. Such links are commonly established by relating the power modulations to specific target variables such as reaction times or task ratings. Consequently, the resulting spatio-spectral representation is subjected to neurophysiological interpretation. As an alternative, independent component analysis (ICA) or alternative decomposition methods can be applied and the power of the components may be related to the target variable. In this paper we show that these standard approaches are suboptimal as the first does not take into account the superposition of many sources due to volume conduction, while the second is unable to exploit available information about the target variable. To improve upon these approaches we introduce a novel (supervised) source separation framework called Source Power Comodulation (SPoC). SPoC makes use of the target variable in the decomposition process in order to give preference to components whose power comodulates with the target variable. We present two algorithms that implement the SPoC approach. Using simulations with a realistic head model, we show that the SPoC algorithms are able extract neuronal components exhibiting high correlation of power with the target variable. In this task, the SPoC algorithms outperform other commonly used techniques that are based on the sensor data or ICA approaches. Furthermore, using real electroencephalography (EEG) recordings during an auditory steady state paradigm, we demonstrate the utility of the SPoC algorithms by extracting neuronal components exhibiting high correlation of power with the intensity of the auditory input. Taking into account the results of the simulations and real EEG recordings, we conclude that SPoC represents an adequate approach for the optimal extraction of neuronal components showing coupling of power with continuously changing behaviorally relevant parameters.

Prediction of Difficulty Levels in Video Games from Ongoing EEG

“…The preprocessed data was analysed using the SPoC analysis proposed by Dähne et al [9]. SPoC optimizes a set of spatial filters w based on a presumed covariation between band power dynamics and an external target signal, which in the present study corresponds to the level of difficulty.…”

“…Typical EEG estimators of workload are based on the fact that changes in workload are associated with modulations in the power of oscillatory activity in particular frequency bands of the EEG, the most prominent ones being theta (4-7 Hz) and alpha (8)(9)(10)(11)(12)(13). Theta power has been shown to be positively correlated with workload, most notably over frontal regions, whereas alpha power is typically found to be negatively correlated with workload, in particular over parietal regions [4,5].…”

“…However, if the goal is to predict from multiple difficulty levels, we leave the regime of binary classification, thus foiling the use of CSP as a method for extracting workload induced power modulations. Here, we employ a novel method called the Source Power Co-Modulation (SPoC) analysis that finds spatial filters such that their corresponding bandpower dynamics maximally covary with a given target variable [9], in this case the difficulty level. A recent study that employed a computer game-like experimental task, demonstrated that SPoC can be used to detect workload states from the EEG with high accuracy [10].…”

An Efficient Decoder for the Recognition of Event-Related Potentials in High-Density MEG Recordings

“…So far, in the field of processing electrophysiological data optimization of correlation was mainly used for analyzing band power in brain signals, such as extracting components that correlate with intensity modulation of auditory stimuli [27] and discovering underlying power-to-power couplings of neuronal sources [28]. Similarly, CCA was used to detect steady-state visual evoked potentials for BCI control [14,15].…”

Real-time Hand Motion Reconstruction System for Trans-Humeral Amputees Using EEG and EMG

“…The first one was the result obtained in the previous analysis with the Zero Substitution method. In addition, we used the Source Power Comodulation (SPoC) method (Dähne et al, 2014) between the hand's position and the raw data signal. SPoC returns a group of spatial filters based on the covariance between two signals.…”