A Preliminary Investigation of ERP Components of Attentional Bias in Anxious Adults using Temporospatial Principal Component Analysis

Multidomain Convolution Neural Network Models for Improved Event-Related Potential Classification

Two convolution neural network (CNN) models are introduced to accurately classify event-related potentials (ERPs) by fusing frequency, time, and spatial domain information acquired from the continuous wavelet transform (CWT) of the ERPs recorded from multiple spatially distributed channels. The multidomain models fuse the multichannel Z-scalograms and the V-scalograms, which are generated from the standard CWT scalogram by zeroing-out and by discarding the inaccurate artifact coefficients that are outside the cone of influence (COI), respectively. In the first multidomain model, the input to the CNN is generated by fusing the Z-scalograms of the multichannel ERPs into a frequency-time-spatial cuboid. The input to the CNN in the second multidomain model is formed by fusing the frequency-time vectors of the V-scalograms of the multichannel ERPs into a frequency-time-spatial matrix. Experiments are designed to demonstrate (a) customized classification of ERPs, where the multidomain models are trained and tested with the ERPs of individual subjects for brain-computer interface (BCI)-type applications, and (b) group-based ERP classification, where the models are trained on the ERPs from a group of subjects and tested on single subjects not included in the training set for applications such as brain disorder classification. Results show that both multidomain models yield high classification accuracies for single trials and small-average ERPs with a small subset of top-ranked channels, and the multidomain fusion models consistently outperform the best unichannel classifiers.

Exploiting the Cone of Influence for Improving the Performance of Wavelet Transform-Based Models for ERP/EEG Classification

Features extracted from the wavelet transform coefficient matrix are widely used in the design of machine learning models to classify event-related potential (ERP) and electroencephalography (EEG) signals in a wide range of brain activity research and clinical studies. This novel study is aimed at dramatically improving the performance of such wavelet-based classifiers by exploiting information offered by the cone of influence (COI) of the continuous wavelet transform (CWT). The COI is a boundary that is superimposed on the wavelet scalogram to delineate the coefficients that are accurate from those that are inaccurate due to edge effects. The features derived from the inaccurate coefficients are, therefore, unreliable. In this study, it is hypothesized that the classifier performance would improve if unreliable features, which are outside the COI, are zeroed out, and the performance would improve even further if those features are cropped out completely. The entire, zeroed out, and cropped scalograms are referred to as the "same" (S)-scalogram, "zeroed out" (Z)-scalogram, and the "valid" (V)-scalogram, respectively. The strategy to validate the hypotheses is to formulate three classification approaches in which the feature vectors are extracted from the (a) S-scalogram in the standard manner, (b) Z-scalogram, and (c) V-scalogram. A subsampling strategy is developed to generate small-sample ERP ensembles to enable customized classifier design for single subjects, and a strategy is developed to select a subset of channels from multiple ERP channels. The three scalogram approaches are implemented using support vector machines, random forests, k-nearest neighbor, multilayer perceptron neural networks, and deep learning convolution neural networks. In order to validate the performance hypotheses, experiments are designed to classify the multi-channel ERPs of five subjects engaged in distinguishing between synonymous and non-synonymous word pairs. The results confirm that the classifiers using the Z-scalogram features outperform those using the S-scalogram features, and the classifiers using the V-scalogram features outperform those using the Z-scalogram features. Most importantly, the relative improvement of the V-scalogram classifiers over the standard S-scalogram classifiers is dramatic. Additionally, enabling the design of customized classifiers for individual subjects is an important contribution to ERP/EEG-based studies and diagnoses of patient-specific disorders.

Auditory and cross-modal attentional bias toward positive natural sounds: Behavioral and ERP evidence

Recently, researchers have expanded the investigation into attentional biases toward positive stimuli; however, few studies have examined attentional biases toward positive auditory information. In three experiments, the present study employed an emotional spatial cueing task using emotional sounds as cues and auditory stimuli (Experiment 1) or visual stimuli (Experiment 2 and Experiment 3) as targets to explore whether auditory or visual spatial attention could be modulated by positive auditory cues. Experiment 3 also examined the temporal dynamics of cross-modal auditory bias toward positive natural sounds using event-related potentials (ERPs). The behavioral results of the three experiments consistently demonstrated that response times to targets were faster after positive auditory cues than they were after neutral auditory cues in the valid condition, indicating that healthy participants showed a selective auditory attentional bias (Experiment 1) and cross-modal attentional bias (Experiment 2 and Experiment 3) toward positive natural sounds. The results of Experiment 3 showed that N1 amplitudes were more negative after positive sounds than they were after neutral sounds, which further provided electrophysiological evidence that positive auditory information enhances attention at early stages in healthy adults. The results of the experiments performed in the present study suggest that humans exhibit an attentional bias toward positive natural sounds.

Mindfulness-Based Cognitive Therapy: A Preliminary Examination of the (Event-Related) Potential for Modifying Threat-Related Attentional Bias in Anxiety

Objectives

Mindfulness-based cognitive therapy (MBCT) can reduce anxiety and depression symptoms in adults with anxiety disorders, and changes in threat-related attentional bias may be a key mechanism driving the intervention's effects on anxiety symptoms. Event-related potentials (ERPs) can illuminate the physiological mechanism through which MBCT targets threat bias and reduces symptoms of anxiety. This preliminary study examined whether P1 ERP threat-related attentional bias markers in anxious adults change from pre- to post-MBCT delivered in-person or virtually (via Zoom) and investigated the relationship between P1 threat-related attentional bias markers and treatment response.

Methods

Pre- and post-MBCT, participants with moderate to high levels of anxiety (N = 50) completed a dot-probe task with simultaneous EEG recording. Analyses focused on pre- and post-MBCT P1 amplitudes elicited by angry-neutral and happy-neutral face pair cues, probes, and reaction times in the dot-probe task and anxiety and depression symptoms.

Results

Pre- to post-MBCT, there was a significant reduction in P1-Probe amplitudes (d = .23), anxiety (d = .41) and depression (d = .80) symptoms, and reaction times (d = .10). Larger P1-Angry Cue amplitudes, indexing hypervigilance to angry faces, were associated with higher levels of anxiety both pre- and post-MBCT (d = .20). Post-MBCT, anxiety symptoms were lower in the in-person versus virtual group (d = .80).

Conclusions

MBCT may increase processing efficiency and decreases anxiety and depression symptoms in anxious adults. However, changes in threat bias specifically were generally not supported. Replication with a comparison group is needed to clarify whether changes were MBCT-specific.

Clinical Trials Registration

NCT03571386, June 18, 2018.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12671-022-01910-x.