Electrophysiological Brain Responses Research Articles

Auditory mismatch negativity in pre-manifest and manifest Huntington’s disease

AimThe aim of this study was to investigate the characteristics of the electrophysiological brain response elicited in a passive acoustic oddball paradigm, i.e. mismatch negativity (MMN), in patients with Huntington’s disease (HD) in the premanifest (pHD) and manifest (mHD) phases. In this regard, we correlated the results of event-related potentials (ERP) with disease characteristics. MethodsThis was an observational cross-sectional MMN study. In addition to the MMN recording of the passive oddball task, all subjects with first-degree inheritance for HD underwent genetic testing for mutant HTT, the Huntington’s Disease Rating Scale, the Total Functional Capacity Scale, the Problem Behaviors Assessment short form, and the Mini-Mental State Examination. ResultsWe found that global field power (GFP) was reduced in the MMN time window in mHD patients compared to pHD and normal controls (NC). In the pHD group, MMN amplitude was only slightly and not significantly increased compared to mHD, while pHD patients showed increased theta coherence between trials compared to mHD. In the entire sample of HD gene carriers, the main MMN traits were not correlated with motor performance, cognitive impairment and functional disability. ConclusionThese results suggest an initial and subtle deterioration of pre-attentive mechanisms in the presymptomatic phase of HD, with an increasing phase shift in the MMN time frame. This result could indicate initial functional changes with a possible compensatory effect. SignificanceAn initial and slight decrease in MMN associated with increased phase coherence in the corresponding EEG frequencies could indicate an early functional involvement of pre-attentive resources that could precede the clinical expression of HD.

Computer programmers show distinct, expertise-dependent brain responses to violations in form and meaning when reading code

As computer programming becomes more central to the workforce, the need for better models of how it is effectively learned has become more apparent. The current study addressed this gap by recording electrophysiological brain responses as 62 Python programmers with varying skill levels read lines of code with manipulations of form (syntax) and meaning (semantics). At the group level, results showed that manipulations of form resulted in P600 effects, with syntactically invalid code generating more positive deflections in the 500–800 ms range than syntactically valid code. Meaning manipulations resulted in N400 effects, with semantically implausible code generating more negative deflections in the 300–500 ms range than semantically plausible code. Greater Python expertise within the group was associated with greater sensitivity to violations in form. These results support the notion that skilled programming, like skilled natural language learning, is associated with the incorporation of rule-based knowledge into online comprehension processes. Conversely, programmers at all skill levels showed neural sensitivity to meaning manipulations, suggesting that reliance on pre-existing semantic relationships facilitates code comprehension across skill levels.

A generative model of electrophysiological brain responses to stimulation.

Each brain response to a stimulus is, to a large extent, unique. However this variability, our perceptual experience feels stable. Standard decoding models, which utilise information across several areas to tap into stimuli representation and processing, are fundamentally based on averages. Therefore, they can focus precisely on the features that are most stable across stimulus presentations. But which are these features exactly is difficult to address in the absence of a generative model of the signal. Here, I introduce genephys, a generative model of brain responses to stimulation publicly available as a Python package that, when confronted with a decoding algorithm, can reproduce the structured patterns of decoding accuracy that we observe in real data. Using this approach, I characterise how these patterns may be brought about by the different aspects of the signal, which in turn may translate into distinct putative neural mechanisms. In particular, the model shows that the features in the data that support successful decoding-and, therefore, likely reflect stable mechanisms of stimulus representation-have an oscillatory component that spans multiple channels, frequencies, and latencies of response; and an additive, slower response with a specific (cross-frequency) relation to the phase of the oscillatory component. At the individual trial level, still, responses are found to be highly variable, which can be due to various factors including phase noise and probabilistic activations.

A generative model of electrophysiological brain responses to stimulation

Each brain response to a stimulus is, to a large extent, unique. However this variability, our perceptual experience feels stable. Standard decoding models, which utilise information across several areas to tap into stimuli representation and processing, are fundamentally based on averages. Therefore, they can focus precisely on the features that are most stable across stimulus presentations. But which are these features exactly is difficult to address in the absence of a generative model of the signal. Here, I introduce genephys, a generative model of brain responses to stimulation publicly available as a Python package that, when confronted with a decoding algorithm, can reproduce the structured patterns of decoding accuracy that we observe in real data. Using this approach, I characterise how these patterns may be brought about by the different aspects of the signal, which in turn may translate into distinct putative neural mechanisms. In particular, the model shows that the features in the data that support successful decoding—and, therefore, likely reflect stable mechanisms of stimulus representation—have an oscillatory component that spans multiple channels, frequencies, and latencies of response; and an additive, slower response with a specific (cross-frequency) relation to the phase of the oscillatory component. At the individual trial level, still, responses are found to be highly variable, which can be due to various factors including phase noise and probabilistic activations.

Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions.

Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using temporal response functions (TRFs) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here, we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group-level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: (1) Is there a significant neural representation corresponding to this predictor variable? And if so, (2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through computational models with appropriate linking hypotheses.

Separating art from the artist: The effect of negative affective knowledge on ERPs and aesthetic experience.

Some artists do terrible things. But does knowing something bad about an artist affect the way we perceive the work? Despite increased public interest, this question has yet to be addressed empirically. In this pre-registered study, we used aesthetic ratings and electrophysiological brain responses to shed light on the issue. We found that paintings of artists associated with negative-social biographical knowledge were liked less and found more arousing than paintings of artists associated with neutral information. Such paintings also elicited an enhanced brain response associated with fast and reflexive processing of emotional stimuli (early posterior negativity; EPN). Evaluations of quality and later, more controlled brain responses (late positive potential; LPP) were not affected. Reflecting the complexity of aesthetic experience, this pattern of results became more differentiated when the visual relatedness between the contents of the painting and the artist-related information was taken into account. Overall, our findings suggest that emotional aspects involved in art reception are not spontaneously separated from the artist, whilst evaluative judgments and more elaborate processing may be.

Association of Postpartum Maternal Mood With Infant Speech Perception at 2 and 6.5 Months of Age

Language development builds on speech perception, with early disruptions increasing the risk for later language difficulties. Although a major postpartum depressive episode is associated with language development, this association has not been investigated among infants of mothers experiencing a depressed mood at subclinical levels after birth, even though such a mood is frequently present in the first weeks after birth. Understanding whether subclinical depressed maternal mood after birth is associated with early language development is important given opportunities of coping strategies for subclinical depressed mood. To examine whether depressed maternal mood at subclinical levels 2 months after birth is associated with infant speech perception trajectories from ages 2 to 6.5 months. In this longitudinal cohort study conducted between January 1, 2018, and October 31, 2019, 46 healthy, monolingual German mother-infant dyads were tested. The sample was recruited from the infants database of the Max Planck Institute for Human Cognitive and Brain Sciences. Initial statistical analysis was performed between January 1 and March 31, 2021; the moderation analysis (results reported herein) was conducted between July 1 and July 31, 2022. Mothers reported postpartum mood via the German version of the Edinburgh Postnatal Depression Scale (higher scores indicated higher levels of depressed mood, with a cutoff of 13 points indicating a high probability of clinical depression) when their infants were 2 months old. Electrophysiological correlates of infant speech perception (mismatch response to speech stimuli) were tested when the infants were aged 2 months (initial assessment) and 6.5 months (follow-up). A total of 46 mothers (mean [SD] age, 32.1 [3.8] years) and their 2-month-old children (mean [SD] age, 9.6 [1.2] weeks; 23 girls and 23 boys) participated at the initial assessment, and 36 mothers (mean [SD] age, 32.2 [4.1] years) and their then 6.5-month-old children (mean [SD] age, 28.4 [1.5 weeks; 18 girls and 18 boys) participated at follow-up. Moderation analyses revealed that more depressed maternal subclinical postpartum mood (mean [SD] Edinburgh Postnatal Depression Scale score, 4.8 [3.6]) was associated with weaker longitudinal changes of infants' electrophysiological brain responses to syllable pitch speech information from ages 2 to 6.5 months (coefficient: 0.68; 95% CI, 0.03-1.33; P = .04). The results of this cohort study suggest that infant speech perception trajectories are correlated with subclinical depressed mood in postpartum mothers. This finding lays the groundwork for future research on early support for caregivers experiencing depressed mood to have a positive association with children's language development.

Electrophysiological Brain Response to Error in Solving Mathematical Tasks

Objective: to identify energy patterns in the electrophysiological bands of the brain as possible indicators of overconfidence in students when they receive feedback indicating they have erred while solving a mathematical task. Methodology: EEG were recorded from 20 subjects while they performed mathematical exercises. Energy changes in the delta and theta bands before, during, and after solving the task were analyzed. Results: when the answers to the exercises were shown, an increase of energy in the delta band was observed in participants with correct answers but a reduction in that band in those who answered incorrectly. Subjects with incorrect answers received feedback and then attempted to solve a second, similar, exercise. Subjects who answered correctly showed an increase of energy theta, while those with incorrect answers showed a decrease. Conclusions: the energy changes when subjects erred while solving a mathematical task could serve as a quantitative indicator for characterizing overconfidence.

On-scalp magnetocorticography with optically pumped magnetometers: Simulated performance in resolving simultaneous sources

Currently, the gold standard for high-resolution mapping of cortical electrophysiological activity is invasive electrocorticography (ECoG), a procedure that carries with it the risk of serious morbidity and mortality. Due to these risks, the use of ECoG is largely limited to pre-surgical mapping in intractable epilepsy. Nevertheless, many seminal studies in neuroscience have utilized ECoG to explore domains such as visual perception, attention, auditory processing, and sensorimotor behavior. Studies such as these, occurring in patients with epilepsy rather than healthy controls, may lack generalizability, and are limited by the placement of the electrode arrays over the presumed seizure focus. This manuscript explores the use of optically pumped magnetometers (OPMs) to create a non-invasive alternative to ECoG, which we refer to as magnetocorticography. Because prior ECoG studies reveal that most cognitive processes are driven by multiple, simultaneous independent neuronal assemblies, we characterize the ability of a theoretical 56-channel dense OPM array to resolve simultaneous independent sources, and compare it to currently available SQUID devices, as well as OPM arrays with inter-sensor spacings more typical of other systems in development. Our evaluation of this theoretical system assesses many potential sources of error, including errors of sensor calibration and position. In addition, we investigate the influence of geometrical and anatomical factors on array performance. Our simulations reveal the potential of high-density, on-scalp OPM MEG devices to localize electrophysiological brain responses at unprecedented resolution for a non-invasive device.

Predictive Processing in Poetic Language: Event-Related Potentials Data on Rhythmic Omissions in Metered Speech.

Predictions during language comprehension are currently discussed from many points of view. One area where predictive processing may play a particular role concerns poetic language that is regularized by meter and rhyme, thus allowing strong predictions regarding the timing and stress of individual syllables. While there is growing evidence that these prosodic regularities influence language processing, less is known about the potential influence of prosodic preferences (binary, strong-weak patterns) on neurophysiological processes. To this end, the present electroencephalogram (EEG) study examined whether the predictability of strong and weak syllables within metered speech would differ as a function of meter (trochee vs. iamb). Strong, i.e., accented positions within a foot should be more predictable than weak, i.e., unaccented positions. Our focus was on disyllabic pseudowords that solely differed between trochaic and iambic structure, with trochees providing the preferred foot in German. Methodologically, we focused on the omission Mismatch Negativity (oMMN) that is elicited when an anticipated auditory stimulus is omitted. The resulting electrophysiological brain response is particularly interesting because its elicitation does not depend on a physical stimulus. Omissions in deviant position of a passive oddball paradigm occurred at either first- or second-syllable position of the aforementioned pseudowords, resulting in a 2-by-2 design with the factors foot type and omission position. Analyses focused on the mean oMMN amplitude and latency differences across the four conditions. The result pattern was characterized by an interaction of the effects of foot type and omission position for both amplitudes and latencies. In first position, omissions resulted in larger and earlier oMMNs for trochees than for iambs. In second position, omissions resulted in larger oMMNs for iambs than for trochees, but the oMMN latency did not differ. The results suggest that omissions, particularly in initial position, are modulated by a trochaic preference in German. The preferred strong-weak pattern may have strengthened the prosodic prediction, especially for matching, trochaic stimuli, such that the violation of this prediction led to an earlier and stronger prediction error. Altogether, predictive processing seems to play a particular role in metered speech, especially if the meter is based on the preferred foot type.

Data for: Eelbrain: A Python toolkit for time-continuous analysis with temporal response functions

Abstract Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have different, interdependent temporal structures. Deconvolution analysis has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of the time dimension. The deconvolution analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate temporal response function (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: 1) Is there a significant neural representation corresponding to this predictor variable? And if so, 2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through appropriate linking models.

Multisine frequency modulation of intra-epidermal electric pulse sequences: A novel tool to study nociceptive processing

A sustained sensory stimulus with a periodic variation of intensity creates an electrophysiological brain response at associated frequencies, referred to as the steady-state evoked potential (SSEP). The SSEPs elicited by the periodic stimulation of nociceptors in the skin may represent activity of a brain network that is primarily involved in nociceptive processing. Exploring the behavior of this network could lead to valuable insights regarding the pathway from nociceptive stimulus to pain perception.We present a method to directly modulate the pulse rate of nociceptive afferents in the skin with a multisine waveform through intra-epidermal electric stimulation. The technique was demonstrated in healthy volunteers. Each subject was stimulated using a pulse sequence modulated by a multisine waveform of 3, 7 and 13 Hz. The EEG was analyzed for the presence of the base frequencies and associated (sub)harmonics.Topographies showed significant central and contralateral SSEP responses at 3, 7 and 13 Hz in respectively 7, 4 and 3 out of the 9 participants included for analysis. As such, we found that intra-epidermal stimulation with a multisine frequency modulated pulse sequence can generate nociceptive SSEPs. The possibility to stimulate the nociceptive system using multisine frequency modulated pulses offers novel opportunities to study the temporal dynamics of nociceptive processing.

Functional dissociation of multiple-object tracking mechanisms based on hemispheric asymmetries.

A number of theoretical accounts have been put forward to explain the ability to simultaneously track multiple visually indistinguishable objects over a period of time. Serial processing models of visual tracking focus on the maintenance of the spatial locations of every single item over time. A more recent mechanism describes multiple object tracking as the ability to maintain a higher order representation of an abstract spatial configuration built by the illusory connection of the tracked items through their transition. The current study investigates the correspondence between these serial and parallel tracking accounts and the right hemispheric specialization for the space-based vs. left hemispheric for object-based attentional processing. Electrophysiological brain responses were recorded in two groups of patients with right- and left hemispheric lesions while performing in a multiple object tracking task. The results suggest a failure to distinguish single item information for the right hemispheric patients accompanied by the absence of a known electrophysiological marker associated with single item tracking. Importantly, left hemispheric patients showed a graded behavioral and electrophysiological response to probe stimuli as a function of the congruence of the probe with the relevant target stimuli. The current data suggest that the differential contribution of serial and parallel tracking mechanisms during object tracking can partly be explained by the different functional contributions of the right and left brain hemispheres.

Symmetrical electrophysiological brain responses to unilateral and bilateral auditory stimuli suggest disrupted spatial processing in schizophrenia

Research has found auditory spatial processing deficits in patients with schizophrenia (SCZ), but no study has examined SCZ patients’ auditory spatial processing at both pre-attentional and attentional stages. To address this gap, we investigated schizophrenics’ brain responses to sounds originating from different locations (right, left, and bilateral sources). The event-related potentials (ERPs) of 25 chronic schizophrenic patients and 25 healthy subjects were compared. Mismatch negativity (MMN) in response to frequency and duration deviants was assessed. Two P3 components (P3a and P3b) were elicited via a frequency discrimination task, and MMN and P3 were recorded through separate monaural and dichotic stimulation paradigms. Our results corroborated the previously published finding that MMN, P3a, and P3b amplitudes are reduced in SCZ patients, but they showed no significant effect of stimulus location on either MMN or P3. These results indicated similarity between the SCZ patients and healthy individuals as regards patterns of ERP responses to stimuli that come from different directions. No evidence of auditory hemispatial bias in the SCZ patients was found, supporting the existence of non-lateralized spatial processing deficits in such patients and suggesting compensatory changes in the hemispheric laterality of patients’ brains.

Rhythmic structure facilitates learning from auditory input in newborn infants

Rhythm and metrical regularities are fundamental properties of music and poetry - and all of those are used in the interaction between infants and their parents. Music and rhythm perception have been shown to support auditory and language skills. Here we compare newborn infants’ learning from a song, a nursery rhyme, and normal speech for the first time in the same study. Infants’ electrophysiological brain responses revealed that the nursery rhyme condition facilitated learning from auditory input, and thus led to successful detection of deviations. These findings suggest that coincidence of prosodic cue patterns and to-be-learned items is more important than the format of the input. Overall, the present results support the view that rhythm is likely to create a template for future events, which allows auditory system to predict prospective input and thus facilitates language development.

The time-course of component processes of selective attention

Attentional selection shapes human perception, enhancing relevant information, according to behavioral goals. While many studies have investigated individual neural signatures of attention, here we used multivariate decoding of electrophysiological brain responses (MEG/EEG) to track and compare multiple component processes of selective attention. Auditory cues instructed participants to select a particular visual target, embedded within a subsequent stream of displays. Combining single and multi-item displays with different types of distractors allowed multiple aspects of information content to be decoded, distinguishing distinct components of attention, as the selection process evolved. Although the task required comparison of items to an attentional “template” held in memory, signals consistent with such a template were largely undetectable throughout the preparatory period but re-emerged after presentation of a non-target choice display. Choice displays evoked strong neural representation of multiple target features, evolving over different timescales. We quantified five distinct processing operations with different time-courses. First, visual properties of the stimulus were strongly represented. Second, the candidate target was rapidly identified and localized in multi-item displays, providing the earliest evidence of modulation by behavioral relevance. Third, the identity of the target continued to be enhanced, relative to distractors. Fourth, only later was the behavioral significance of the target explicitly represented in single-item displays. Finally, if the target was not identified and search was to be resumed, then an attentional template was weakly reactivated. The observation that an item's behavioral relevance directs attention in multi-item displays prior to explicit representation of target/non-target status in single-item displays is consistent with two-stage models of attention.

Deaf adolescents have bigger responses for somatosensory and visual stimulations

The functions of the sensory systems on disabled people have been one of the most investigated topics in brain research. In these studies, mostly visual stimuli had been employed while investigating the deaf participants. Limited number of electrophysiological studies revealed better visual sensory processing in deaf participants. On the other hand, studies deploying tactile stimuli especially used either electrical or painful stimulus or they focused the psychophysical assessments of thresholds associated with tactile stimuli. The present study tried to evaluate electrophysiological brain responses in deaf and control group with a unimodal study design including both the visual and non-painful tactile stimuli, and to reveal the possible changes in brain plasticity within modality basis.Thirteen congenitally deaf adolescents (mean: 14.61 ± 1.06 years; 7 girls) and 10 adolescents with normal hearing (16.6 ± 2.72 years; 4 girls) were recruited for the study. Somatosensory evoked potentials (SEP) and visual evoked potentials (VEP) were separately delivered to the participants and in order to maintain neutrality among sessions they were presented in random order while the electroencephalography (EEG) recordings were taken.Brain responses to non-painful tactile and visual stimuli were measured for N1, P2, and N2 components. All amplitudes of deaf group were significantly larger than all amplitudes of control group in SEP session whereas in VEP session only P2 and N2 amplitudes of deaf group were statistically larger. In addition, the latency of N1 component in VEP session was significantly earlier in deaf group.These findings suggest early cortical excitability, less neuronal capacity usage and also more efficient sensory processing in deaf group.

Your Brain on Comics: A Cognitive Model of Visual Narrative Comprehension.

The past decade has seen a rapid growth of cognitive and brain research focused on visual narratives like comics and picture stories. This paper will summarize and integrate this emerging literature into the Parallel Interfacing Narrative‐Semantics Model (PINS Model)—a theory of sequential image processing characterized by an interaction between two representational levels: semantics and narrative structure. Ongoing semantic processes build meaning into an evolving mental model of a visual discourse. Updating of spatial, referential, and event information then incurs costs when they are discontinuous with the growing context. In parallel, a narrative structure organizes semantic information into coherent sequences by assigning images to categorical roles, which are then embedded within a hierarchic constituent structure. Narrative constructional schemas allow for specific predictions of structural sequencing, independent of semantics. Together, these interacting levels of representation engage in an iterative process of retrieval of semantic and narrative information, prediction of upcoming information based on those assessments, and subsequent updating based on discontinuity. These core mechanisms are argued to be domain‐general—spanning across expressive systems—as suggested by similar electrophysiological brain responses (N400, P600, anterior negativities) generated in response to manipulation of sequential images, music, and language. Such similarities between visual narratives and other domains thus pose fundamental questions for the linguistic and cognitive sciences.

The fundamental phonological unit of Japanese word production: An EEG study using the picture-word interference paradigm

Abstract It has been shown that in Germanic languages (e.g. English, Dutch) phonemes are the primary (or proximate) planning units during the early stages of phonological encoding. Contrastingly, in Chinese and Japanese the phoneme does not seem to play an important role but rather the syllable (Chinese) and mora (Japanese) are essential. However, despite the lack of behavioral evidence, neurocorrelational studies in Chinese suggested that electrophysiological brain responses (i.e. preceding overt responses) may indicate some significance for the phoneme. We investigated this matter in Japanese and our data shows that unlike in Chinese (for which the literature shows mixed effects), in Japanese both the behavioral and neurocorrelational data indicate an important role only for the mora (and not the phoneme) during the early stages of phonological encoding.

Written-Word Concreteness Effects in Non-attend Conditions: Evidence From Mismatch Responses and Cortical Oscillations.

It has been widely reported that concrete words have processing advantages over abstract words in terms of speed and efficiency of processing, a phenomenon known as the concreteness effect. However, little is still known about the early time-course of processing concrete and abstract words and whether this concreteness effect can still persist in conditions where attention is not focused on the words presented (automatic processing). This study aimed to shed light on these issues by examining the electrophysiological brain responses to concrete and abstract words. While participants were engaged in a non-linguistic color tracking task presented in the center of the monitor screen, matched Chinese concrete and abstract single-character words appeared within a passive oddball paradigm, out of the focus of attention. In calculating visual Mismatch Negativity (vMMN), Event-related potentials (ERPs) to words of the same semantic category were compared when these words were presented as deviants and standards. Before 320 ms, both abstract and concrete words yielded vMMN with left-lateralized distribution, suggesting similar verbal processing at an initial processing stage. After 320 ms, only concrete words additionally elicited vMMN with a central distribution. Time frequency (TF) analysis of the results also revealed larger theta power increase (200–300 ms) and theta power phase locking (200–450 ms) for concrete than for abstract words. Interestingly, there was more alpha power decrease for abstract than for concrete words from 300 to 450 ms. This may reflect the greater difficulty in processing abstract meaning. Taken together, our ERP and TF results point to the existence of different neural mechanisms underlying non-attentive processing of abstract and concrete words.