Selective spatial attention involves two alpha-band components associated with distinct spatiotemporal and functional characteristics

The neural oscillatory mechanism underlying human brain fingerprint recognition using a portable EEG acquisition device

In recent years, brainprint recognition has emerged as a novel method of personal identity verification. Although studies have demonstrated the feasibility of this technology, some limitations hinder its further development into the society, such as insufficient efficiency (extended wear time for multi-channel EEG cap), complex experimental paradigms (more time in learning and completing experiments), and unclear neurobiological characteristics (lack of intuitive biomarkers and an inability to eliminate the impact of noise on individual differences). Overall, these limitations are due to the incomplete understanding of the underlying neural mechanisms. Therefore, this study aims to investigate the neural mechanisms behind brainwave recognition and simplify the operation process. We recorded prefrontal resting-state EEG data from 40 participants, which is followed up over nine months using a single-channel portable brainwave device. We found that portable devices can effectively and stably capture the characteristics of different subjects in the alpha band (8-13Hz) over long periods, as well as capturing their individual differences (no alpha peak, 1 alpha peak, or 2 alpha peaks). Through correlation analysis, alpha-band activity can reveal the uniqueness of the subjects compared to others within one minute. We further used a descriptive model to dissect the oscillatory and non-oscillatory components in the alpha band, demonstrating the different contributions of fine oscillatory features to individual differences (especially amplitude and bandwidth). Our study validated the feasibility of portable brainwave devices in brainwave recognition and the underlying neural oscillation mechanisms. The fine characteristics of various alpha oscillations will contribute to the accuracy of brainwave recognition, providing new insights for the development of future brainwave recognition technology.

Characterizing Major Depressive Disorder (MDD) using alpha-band activity in resting-state electroencephalogram (EEG) combined with MATRICS Consensus Cognitive Battery (MCCB)

BACKGROUND

The diagnosis of major depressive disorder (MDD) is commonly based on the subjective evaluation by experienced psychiatrists using clinical scales. Hence, it is particularly important to find more objective biomarkers to aid in diagnosis and further treatment. Alpha-band activity (7-13 Hz) is the most prominent component in resting electroencephalogram (EEG), which is also thought to be a potential biomarker. Recent studies have shown the existence of multiple sub-oscillations within the alpha band, with distinct neural underpinnings. However, the specific contribution of these alpha sub-oscillations to the diagnosis and treatment of MDD remains unclear.

METHODS

In this study, we recorded the resting-state EEG from MDD and HC populations in both open and closed-eye state conditions. We also assessed cognitive processing using the MATRICS Consensus Cognitive Battery (MCCB).

RESULTS

We found that the MDD group showed significantly higher power in the high alpha range (10.5-11.5 Hz) and lower power in the low alpha range (7-8.5 Hz) compared to the HC group. Notably, high alpha power in the MDD group is negatively correlated with working memory performance in MCCB, whereas no such correlation was found in the HC group. Furthermore, using five established classification algorithms, we discovered that combining alpha oscillations with MCCB scores as features yielded the highest classification accuracy compared to using EEG or MCCB scores alone.

CONCLUSIONS

Our results demonstrate the potential of sub-oscillations within the alpha frequency band as a potential distinct biomarker. When combined with psychological scales, they may provide guidance relevant for the diagnosis and treatment of MDD.

EEG-based speaker-listener neural coupling reflects speech-selective attentional mechanisms beyond the speech stimulus

When we pay attention to someone, do we focus only on the sound they make, the word they use, or do we form a mental space shared with the speaker we want to pay attention to? Some would argue that the human language is no other than a simple signal, but others claim that human beings understand each other because they form a shared mental ground between the speaker and the listener. Our study aimed to explore the neural mechanisms of speech-selective attention by investigating the electroencephalogram-based neural coupling between the speaker and the listener in a cocktail party paradigm. The temporal response function method was employed to reveal how the listener was coupled to the speaker at the neural level. The results showed that the neural coupling between the listener and the attended speaker peaked 5 s before speech onset at the delta band over the left frontal region, and was correlated with speech comprehension performance. In contrast, the attentional processing of speech acoustics and semantics occurred primarily at a later stage after speech onset and was not significantly correlated with comprehension performance. These findings suggest a predictive mechanism to achieve speaker-listener neural coupling for successful speech comprehension.

Nonadditive integration of visual information in ensemble processing

Statistically summarizing information from a stimulus array into an ensemble representation (e.g., the mean) improves the efficiency of visual processing. However, little is known about how the brain computes the ensemble statistics. Here, we propose that ensemble processing is realized by nonadditive integration, rather than linear averaging, of individual items. We used a linear regression model approach to extract EEG responses to three levels of information: the individual items, their local interactions, and their global interaction. The local and global interactions, representing nonadditive integration of individual items, elicited rapid and independent neural responses. Critically, only the neural representation of the global interaction predicted the precision of the ensemble perception at the behavioral level. Furthermore, spreading attention over the global pattern to enhance ensemble processing directly promoted rapid neural representation of the global interaction. Taken together, these findings advocate a global, nonadditive mechanism of ensemble processing in the brain.

Different patterns of foreground and background processing contribute to texture segregation in humans: an electrophysiological study

Background

Figure-ground segregation is a necessary process for accurate visual recognition. Previous neurophysiological and human brain imaging studies have suggested that foreground-background segregation relies on both enhanced foreground representation and suppressed background representation. However, in humans, it is not known when and how foreground and background processing play a role in texture segregation.

Methods

To answer this question, it is crucial to extract and dissociate the neural signals elicited by the foreground and background of a figure texture with high temporal resolution. Here, we combined an electroencephalogram (EEG) recording and a temporal response function (TRF) approach to specifically track the neural responses to the foreground and background of a figure texture from the overall EEG recordings in the luminance-tracking TRF. A uniform texture was included as a neutral condition. The texture segregation visual evoked potential (tsVEP) was calculated by subtracting the uniform TRF from the foreground and background TRFs, respectively, to index the specific segregation activity.

Results

We found that the foreground and background of a figure texture were processed differently during texture segregation. In the posterior region of the brain, we found a negative component for the foreground tsVEP in the early stage of foreground-background segregation, and two negative components for the background tsVEP in the early and late stages. In the anterior region, we found a positive component for the foreground tsVEP in the late stage, and two positive components for the background tsVEP in the early and late stages of texture processing.

Discussion

In this study we investigated the temporal profile of foreground and background processing during texture segregation in human participants at a high time resolution. The results demonstrated that the foreground and background jointly contribute to figure-ground segregation in both the early and late phases of texture processing. Our findings provide novel evidence for the neural correlates of foreground-background modulation during figure-ground segregation in humans.

Uncovering the Interplay of Oscillatory Processes During Creative Problem Solving: A Dynamic Modeling Approach

To solve a new problem, people spontaneously engage multiple cognitive processes. Previous work has identified a diverse set of oscillatory components critical at different stages of creative problem solving. In this project, we use hidden state modeling to untangle the roles of oscillation processes over time as people solve puzzles. Building on earlier work, we further developed analytical methods, such as incorporating source separating techniques and identifying the optimal number of states using cross-validation. We extracted brain states characterized by spatio-spectral topographies from time-resolved EEG spectral powers. The data driven approach allowed us to infer the dynamic, trial-by-trial, state sequences, and provided a comprehensive depiction of how various oscillation components interact recurrently throughout the trial. The properties of the states suggest their dissociable cognitive functions. For example, we identified three states with dominant activation in alpha bands but having distinct spatial distributions. People were differentially engaged in these states depending on the stages (e.g., onset or response) and outcomes of the trials (solved with insight or analysis). The current approach, applicable to many tasks requiring extended trial duration, can potentially reconcile findings from previous EEG studies and drive new hypotheses to further our understanding of the complex creative process.

Alpha oscillations reflect suppression of distractors with increased perceptual load

Attention serves an essential role in cognition and behavior allowing us to focus on behaviorally-relevant objects while ignoring distraction. Perceptual load theory states that attentional resources are allocated according to the requirements of the task, i.e., its 'load'. The theory predicts that the resources left to process irrelevant, possibly distracting stimuli, are reduced when the perceptual load is high. However, it remains unclear how this allocation of attentional resources specifically relates to neural excitability and suppression mechanisms. In this magnetoencephalography (MEG) study, we show that brain oscillations in the alpha band (8-13 Hz) implemented the suppression of distracting objects when the perceptual load was high. In parallel, high load increased the neuronal excitability for target objects, as reflected by rapid invisible frequency tagging. We suggest that the allocation of resources in tasks with high perceptual load is implemented by a gain increase for targets, complemented by distractor suppression reflected by alpha-band oscillations closing the 'gate' for interference.

Ensemble size perception: Its neural signature and the role of global interaction over individual items

To efficiently process complex visual scenes, the visual system often summarizes statistical information across individual items and represents them as an ensemble. However, due to the lack of techniques to disentangle the representation of the ensemble from that of the individual items constituting the ensemble, whether there exists a specialized neural mechanism for ensemble processing and how ensemble perception is computed in the brain remain unknown. To address these issues, we used a frequency-tagging EEG approach to track brain responses to periodically updated ensemble sizes. Neural responses tracking the ensemble size were detected in parieto-occipital electrodes, revealing a global and specialized neural mechanism of ensemble size perception. We then used the temporal response function to isolate neural responses to the individual sizes and their interactions. Notably, while the individual sizes and their local and global interactions were encoded in the EEG signals, only the global interaction contributed directly to the ensemble size perception. Finally, distributed attention to the global stimulus pattern enhanced the neural signature of the ensemble size, mainly by modulating the neural representation of the global interaction between all individual sizes. These findings advocate a specialized, global neural mechanism of ensemble size perception and suggest that global interaction between individual items contributes to ensemble perception.

The dynamics of microsaccade amplitude reflect shifting of covert attention

Attention flexibly shifts between spatial locations to accommodate task demands. The present study examined if the dynamics of attentional shifting are seen in microsaccades whose direction has been shown to accompany the shifts of covert attention. In a spatial cueing task, the cue predicted the target location on 100%, 75%, or 50% of the trials. The results revealed that microsaccade rate and amplitude were both reduced following cue onset and then rebounded. Both microsaccade rate and amplitude were biased towards the opposite direction of the cue and then returned to the cued direction. Importantly, the cue validity modulated the temporal profile of microsaccade amplitude but had little impact on the temporal profile of microsaccade rate. In line with this, the cueing effect measured with target response accuracy was correlated with the microsaccade amplitude only. These results indicate that the temporal dynamics of microsaccade amplitude reflect shifting of covert attention.

Distinct networks coupled with parietal cortex for spatial representations inside and outside the visual field

Our mental representation of egocentric space is influenced by the disproportionate sensory perception of the body. Previous studies have focused on the neural architecture for egocentric representations within the visual field. However, the space representation underlying the body is still unclear. To address this problem, we applied both functional Magnitude Resonance Imaging (fMRI) and Magnetoencephalography (MEG) to a spatial-memory paradigm by using a virtual environment in which human participants remembered a target location left, right, or back relative to their own body. Both experiments showed larger involvement of the frontoparietal network in representing a retrieved target on the left/right side than on the back. Conversely, the medial temporal lobe (MTL)-parietal network was more involved in retrieving a target behind the participants. The MEG data showed an earlier activation of the MTL-parietal network than that of the frontoparietal network during retrieval of a target location. These findings suggest that the parietal cortex may represent the entire space around the self-body by coordinating two distinct brain networks.

Alpha-Band Phase Modulates Bottom-up Feature Processing

Recent studies reveal that attention operates in a rhythmic manner, that is, sampling each location or feature alternatively over time. However, most evidence derives from top-down tasks, and it remains elusive whether bottom-up processing also entails dynamic coordination. Here, we developed a novel feature processing paradigm and combined time-resolved behavioral measurements and electroencephalogram (EEG) recordings to address the question. Specifically, a salient color in a multicolor display serves as a noninformative cue to capture attention and presumably reset the oscillations of feature processing. We then measured the behavioral performance of a probe stimulus associated with either high- or low-salient color at varied temporal lags after the cue. First, the behavioral results (i.e., reaction time) display an alpha-band (~8 Hz) profile with a consistent phase lag between high- and low-salient conditions. Second, simultaneous EEG recordings show that behavioral performance is modulated by the phase of alpha-band neural oscillation at the onset of the probes. Finally, high- and low-salient probes are associated with distinct preferred phases of alpha-band neural oscillations. Taken together, our behavioral and neural results convergingly support a central function of alpha-band rhythms in feature processing, that is, features with varied saliency levels are processed at different phases of alpha neural oscillations.

Attention reduces the burstiness of V1 neurons involved in attended target enhancement

Attention-dependent reduction in the tendency for neurons to fire bursts (burstiness) is widely observed in the visual cortex. However, the underlying mechanism and the functional role of this phenomenon remain unclear. We recorded well-isolated single-unit activities in primary visual cortex (V1) from two primates (Macaca mulatta) while they performed a detection task engaging spatial attention with two levels of difficulty (hard/easy). We found that attention modulated burstiness of V1 neurons in a cell-type specific manner. For neurons whose net response enhanced with the increase of task difficulty (difficulty-enhanced neuron), representing their involvement in boosting the signal of the attended stimulus, attention led to a reduction in burstiness during hard task but not during easy task. In contrast, regardless of the level of task difficulty, attention-dependent reduction in burstiness was not observed in neurons that showed a net suppression in firing rate with the increase of task difficulty (difficulty-suppressed neuron), indicating their commitment in filtering out the interference of distractor. This differentiation in the effects of attentional modulation on burstiness among the cells with distinct functional roles in attention suggests that the reduction in burstiness by attention is linked to target enhancement and is not associated with distractor suppression.

Cortical Distance but Not Physical Distance Modulates Attentional Rhythms

It has been well documented that the spotlight of attention is intrinsically rhythmic and oscillates by discretely sampling either single or multiple objects. However, the neural site of attentional rhythms remains poorly understood. Considering the topography of visual cortical areas, we modulated the cortical distances of two gratings while fixing the corresponding retinal distance by setting the gratings on different sides (cortically far, Experiment 1) or on the same side (cortically near, Experiment 2) of the vertical median, to investigate the interhemispheric divide effect in attentional rhythms. The cue-target stimulus onset asynchrony (SOA) varied from 0.1 s to 1.08 s in 20-ms increments, allowing fluctuations below 50 Hz to be examined. The results showed that when the two stimuli were on opposite sides of the vertical meridian, attentional rhythms were observed at theta and alpha frequencies, consistent with the results reported in previous studies. However, when the two stimuli were located on the same side of the vertical meridian, attentional rhythms were not observed. This study indicates that attentional rhythms are modulated by cortical distance but not by physical distance.

Decoding attention control and selection in visual spatial attention

Event‐related potentials (ERPs) are used extensively to investigate the neural mechanisms of attention control and selection. The univariate ERP approach, however, has left important questions inadequately answered. We addressed two questions by applying multivariate pattern classification to multichannel ERPs in two cued visual spatial attention experiments (N = 56): (a) impact of cueing strategies (instructional vs. probabilistic) on attention control and selection and (b) neural and behavioral effects of individual differences. Following cue onset, the decoding accuracy (cue left vs. cue right) began to rise above chance level earlier and remained higher in instructional cueing (~80 ms) than in probabilistic cueing (~160 ms), suggesting that unilateral attention focus leads to earlier and more distinct formation of the attention control set. A similar temporal sequence was also found for target‐related processing (cued target vs. uncued target), suggesting earlier and stronger attention selection under instructional cueing. Across the two experiments: (a) individuals with higher cue‐related decoding accuracy showed higher magnitude of attentional modulation of target‐evoked N1 amplitude, suggesting that better formation of anticipatory attentional state leads to stronger modulation of target processing, and (b) individuals with higher target‐related decoding accuracy showed faster reaction times (or larger cueing effects), suggesting that stronger selection of task‐relevant information leads to better behavioral performance. Taken together, multichannel ERPs combined with machine learning decoding yields new insights into attention control and selection that complement the univariate ERP approach, and along with the univariate ERP approach, provides a more comprehensive methodology to the study of visual spatial attention.

Visual target detection in a distracting background relies on neural encoding of both visual targets and background

The ability to detect visual targets in complex background varies across individuals and are affected by factors such as stimulus saliency and top-down attention. Here, we investigated how the saliency of visual background (naturalistic cartoon video vs. blank screen) and top-down attention (single vs. dual tasks) separately affect individual ability to detect visual targets. Behaviorally, we found that target detection accuracy decreased and reaction time elongated when the background was salient or during dual tasking. The EEG response to visual background was recorded using a novel stimulus tagging technique. This response was strongest in occipital electrodes and was sensitive to background saliency but not dual tasking. In contrast, the event-related potential (ERP) evoked by the visual target was strongest in central electrodes, and was affected by both background saliency and dual tasking. With a cartoon background, the EEG responses to visual targets, presented in the central visual field, and the EEG responses to peripheral visual background could both predict individual target detection performance. When these two responses were combined, better prediction was achieved. These results suggest that neural processing of visual targets and background jointly contribute to individual visual target detection performance.

Alpha Oscillations in the Human Brain Implement Distractor Suppression Independent of Target Selection

In principle, selective attention is the net result of target selection and distractor suppression. The way in which both mechanisms are implemented neurally has remained contested. Neural oscillatory power in the alpha frequency band (∼10 Hz) has been implicated in the selection of to-be-attended targets, but there is lack of empirical evidence for its involvement in the suppression of to-be-ignored distractors. Here, we use electroencephalography recordings of N = 33 human participants (males and females) to test the preregistered hypothesis that alpha power directly relates to distractor suppression and thus operates independently from target selection. In an auditory spatial pitch discrimination task, we modulated the location (left vs right) of either a target or a distractor tone sequence, while fixing the other in the front. When the distractor was fixed in the front, alpha power relatively decreased contralaterally to the target and increased ipsilaterally. Most importantly, when the target was fixed in the front, alpha lateralization reversed in direction for the suppression of distractors on the left versus right. These data show that target-selection-independent alpha power modulation is involved in distractor suppression. Although both lateralized alpha responses for selection and for suppression proved reliable, they were uncorrelated and distractor-related alpha power emerged from more anterior, frontal cortical regions. Lending functional significance to suppression-related alpha oscillations, alpha lateralization at the individual, single-trial level was predictive of behavioral accuracy. These results fuel a renewed look at neurobiological accounts of selection-independent suppressive filtering in attention.SIGNIFICANCE STATEMENT Although well established models of attention rest on the assumption that irrelevant sensory information is filtered out, the neural implementation of such a filter mechanism is unclear. Using an auditory attention task that decouples target selection from distractor suppression, we demonstrate that two sign-reversed lateralized alpha responses reflect target selection versus distractor suppression. Critically, these alpha responses are reliable, independent of each other, and generated in more anterior, frontal regions for suppression versus selection. Prediction of single-trial task performance from alpha modulation after stimulus onset agrees with the view that alpha modulation bears direct functional relevance as a neural implementation of attention. Results demonstrate that the neurobiological foundation of attention implies a selection-independent alpha oscillatory mechanism to suppress distraction.