Influences of cognitive load on sensorimotor contributions to working memory: An EEG investigation of mu rhythm activity during speech discrimination

Fast & scrupulous: Gesture-based alarms improve accuracy and reaction times under various mental workload levels. An ERSP study

In high-risk environments, fast and accurate responses to warning systems are essential to efficiently handle emergency situations. The aim of the present study was twofold: 1) investigating whether hand action videos (i.e., gesture alarms) trigger faster and more accurate responses than text alarm messages (i.e., written alarms), especially when mental workload (MWL) is high; and 2) investigating the brain activity in response to both types of alarms as a function of MWL. Regardless of MWL, participants (N = 28) were found to be both faster and more accurate when responding to gesture alarms than to written alarms. Brain electrophysiological results suggest that this greater efficiency might be due to a facilitation of the action execution, reflected by the decrease in mu and beta power observed around the response time window observed at C3 and C4 electrodes. These results suggest that gesture alarms may improve operators' performances in emergency situations.

Dynamic auditory contributions to error detection revealed in the discrimination of Same and Different syllable pairs

During speech production auditory regions operate in concert with the anterior dorsal stream to facilitate online error detection. As the dorsal stream also is known to activate in speech perception, the purpose of the current study was to probe the role of auditory regions in error detection during auditory discrimination tasks as stimuli are encoded and maintained in working memory. A priori assumptions are that sensory mismatch (i.e., error) occurs during the discrimination of Different (mismatched) but not Same (matched) syllable pairs. Independent component analysis was applied to raw EEG data recorded from 42 participants to identify bilateral auditory alpha rhythms, which were decomposed across time and frequency to reveal robust patterns of event related synchronization (ERS; inhibition) and desynchronization (ERD; processing) over the time course of discrimination events. Results were characterized by bilateral peri-stimulus alpha ERD transitioning to alpha ERS in the late trial epoch, with ERD interpreted as evidence of working memory encoding via Analysis by Synthesis and ERS considered evidence of speech-induced-suppression arising during covert articulatory rehearsal to facilitate working memory maintenance. The transition from ERD to ERS occurred later in the left hemisphere in Different trials than in Same trials, with ERD and ERS temporally overlapping during the early post-stimulus window. Results were interpreted to suggest that the sensory mismatch (i.e., error) arising from the comparison of the first and second syllable elicits further processing in the left hemisphere to support working memory encoding and maintenance. Results are consistent with auditory contributions to error detection during both encoding and maintenance stages of working memory, with encoding stage error detection associated with stimulus concordance and maintenance stage error detection associated with task-specific retention demands.

Preparing for the unknown: How working memory provides a link between perception and anticipated action

What mechanisms underlie the transfer of a working memory representation into a higher-level code for guiding future actions? Electrophysiological correlates of attentional selection and motor preparation processes within working memory were investigated in two retrospective cuing tasks. In the first experiment, participants stored the orientation and location of a grating. Subsequent feature cues (selective vs. neutral) indicated which feature would be the target for later report. The oscillatory response in the mu and beta frequency range with an estimated source in the sensorimotor cortex contralateral to the responding hand was used as correlate of motor preparation. Mu/beta suppression was stronger following the selective feature cues compared to the neutral cue, demonstrating that purely feature-based selection is sufficient to form a prospective motor plan. In the second experiment, another retrospective cue was included to study whether knowledge of the task at hand is necessary to initiate motor preparation. Following the feature cue, participants were cued to either compare the stored feature(s) to a probe stimulus (recognition task) or to adjust the memory probe to match the target feature (continuous report task). An analogous suppression of mu oscillations was observed following a selective feature cue, even ahead of task specification. Further, a subsequent selective task cue again elicited a mu/beta suppression, which was stronger after a continuous report task cue. This indicates that working memory is able to flexibly store different types of information in higher-level mental codes to provide optimal prerequisites for all required action possibilities.

Green tea consumption and the management of adrenal stress hormones in adolescents who stutter

Green tea and its polyphenolic compounds have been shown to exert positive effects in individuals with psychological disorders. The protective role of green tea against stuttering or its related consequences, depression, anxiety and stress, were evaluated in adolescents with moderate stuttering (MS). A total of 60 adolescents aged (12-18) years old were enrolled in this study. Patients were classified according to standardized test material Stuttering Severity Instrument, 4th Edition was used to estimate the severity of stuttering; participants were classified into two groups: a normal healthy group (n=30) and a MS group (n=30). The Depression Anxiety Stress Scale and General Health Questionnaire were used to estimate the degree of depression, anxiety and stress as well as general mental health. The physiological profile of stress hormones, as a measure of the response to green tea response, was also measured amongst participants. Adrenal stress hormones cortisol, dehydroepiandrosterone (DHEA), acetylcholine (ACTH), corticosterone and the cortisol:DHEA ratio were assayed. In addition, the constituent green tea polyphenols and their quantities were determined using liquid chromatography analysis. Decaffeinated green tea was administered six cups/day for 6 weeks, and this significantly improved the depression, anxiety, stress and mental health consequences associated with stuttering in adolescents. In addition, increased consumption of green tea significantly reduced elevated levels of adrenal stress hormones; cortisol, DHEA, ACTH and corticosterone, and increased the cortisol:DHEA ratio in the control and adolescents who stuttered. The data showed that drinking six cups of decaffeinated green tea, which is enriched in catechins (1,580 mg) and other related polyphenols, was sufficient to improve the consequences of mental health associated with stuttering in younger aged individuals.

Audiovisual incongruence differentially impacts left and right hemisphere sensorimotor oscillations: Potential applications to production

Speech production gives rise to distinct auditory and somatosensory feedback signals which are dynamically integrated to enable online monitoring and error correction, though it remains unclear how the sensorimotor system supports the integration of these multimodal signals. Capitalizing on the parity of sensorimotor processes supporting perception and production, the current study employed the McGurk paradigm to induce multimodal sensory congruence/incongruence. EEG data from a cohort of 39 typical speakers were decomposed with independent component analysis to identify bilateral mu rhythms; indices of sensorimotor activity. Subsequent time-frequency analyses revealed bilateral patterns of event related desynchronization (ERD) across alpha and beta frequency ranges over the time course of perceptual events. Right mu activity was characterized by reduced ERD during all cases of audiovisual incongruence, while left mu activity was attenuated and protracted in McGurk trials eliciting sensory fusion. Results were interpreted to suggest distinct hemispheric contributions, with right hemisphere mu activity supporting a coarse incongruence detection process and left hemisphere mu activity reflecting a more granular level of analysis including phonological identification and incongruence resolution. Findings are also considered in regard to incongruence detection and resolution processes during production.

Sensorimotor contributions to working memory differ between the discrimination of Same and Different syllable pairs

Sensorimotor activity during speech perception is both pervasive and highly variable, changing as a function of the cognitive demands imposed by the task. The purpose of the current study was to evaluate whether the discrimination of Same (matched) and Different (unmatched) syllable pairs elicit different patterns of sensorimotor activity as stimuli are processed in working memory. Raw EEG data recorded from 42 participants were decomposed with independent component analysis to identify bilateral sensorimotor mu rhythms from 36 subjects. Time frequency decomposition of mu rhythms revealed concurrent event related desynchronization (ERD) in alpha and beta frequency bands across the peri- and post-stimulus time periods, which were interpreted as evidence of sensorimotor contributions to working memory encoding and maintenance. Left hemisphere alpha/beta ERD was stronger in Different trials than Same trials during the post-stimulus period, while right hemisphere alpha/beta ERD was stronger in Same trials than Different trials. A between-hemispheres contrast revealed no differences during Same trials, while post-stimulus alpha/beta ERD was stronger in the left hemisphere than the right during Different trials. Results were interpreted to suggest that predictive coding mechanisms lead to repetition suppression effects in Same trials. Mismatches arising from predictive coding mechanisms in Different trials shift subsequent working memory processing to the speech-dominant left hemisphere. Findings clarify how sensorimotor activity differentially supports working memory encoding and maintenance stages during speech discrimination tasks and have potential to inform sensorimotor models of speech perception and working memory.

EEG Correlates of Learning From Speech Presented in Environmental Noise

How the human brain retains relevant vocal information while suppressing irrelevant sounds is one of the ongoing challenges in cognitive neuroscience. Knowledge of the underlying mechanisms of this ability can be used to identify whether a person is distracted during listening to a target speech, especially in a learning context. This paper investigates the neural correlates of learning from the speech presented in a noisy environment using an ecologically valid learning context and electroencephalography (EEG). To this end, the following listening tasks were performed while 64-channel EEG signals were recorded: (1) attentive listening to the lectures in background sound, (2) attentive listening to the background sound presented alone, and (3) inattentive listening to the background sound. For the first task, 13 lectures of 5 min in length embedded in different types of realistic background noise were presented to participants who were asked to focus on the lectures. As background noise, multi-talker babble, continuous highway, and fluctuating traffic sounds were used. After the second task, a written exam was taken to quantify the amount of information that participants have acquired and retained from the lectures. In addition to various power spectrum-based EEG features in different frequency bands, the peak frequency and long-range temporal correlations (LRTC) of alpha-band activity were estimated. To reduce these dimensions, a principal component analysis (PCA) was applied to the different listening conditions resulting in the feature combinations that discriminate most between listening conditions and persons. Linear mixed-effect modeling was used to explain the origin of extracted principal components, showing their dependence on listening condition and type of background sound. Following this unsupervised step, a supervised analysis was performed to explain the link between the exam results and the EEG principal component scores using both linear fixed and mixed-effect modeling. Results suggest that the ability to learn from the speech presented in environmental noise can be predicted by the several components over the specific brain regions better than by knowing the background noise type. These components were linked to deterioration in attention, speech envelope following, decreased focusing during listening, cognitive prediction error, and specific inhibition mechanisms.

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.

The Application of EEG Mu Rhythm Measures to Neurophysiological Research in Stuttering

Deficits in basal ganglia-based inhibitory and timing circuits along with sensorimotor internal modeling mechanisms are thought to underlie stuttering. However, much remains to be learned regarding the precise manner how these deficits contribute to disrupting both speech and cognitive functions in those who stutter. Herein, we examine the suitability of electroencephalographic (EEG) mu rhythms for addressing these deficits. We review some previous findings of mu rhythm activity differentiating stuttering from non-stuttering individuals and present some new preliminary findings capturing stuttering-related deficits in working memory. Mu rhythms are characterized by spectral peaks in alpha (8-13 Hz) and beta (14-25 Hz) frequency bands (mu-alpha and mu-beta). They emanate from premotor/motor regions and are influenced by basal ganglia and sensorimotor function. More specifically, alpha peaks (mu-alpha) are sensitive to basal ganglia-based inhibitory signals and sensory-to-motor feedback. Beta peaks (mu-beta) are sensitive to changes in timing and capture motor-to-sensory (i.e., forward model) projections. Observing simultaneous changes in mu-alpha and mu-beta across the time-course of specific events provides a rich window for observing neurophysiological deficits associated with stuttering in both speech and cognitive tasks and can provide a better understanding of the functional relationship between these stuttering symptoms. We review how independent component analysis (ICA) can extract mu rhythms from raw EEG signals in speech production tasks, such that changes in alpha and beta power are mapped to myogenic activity from articulators. We review findings from speech production and auditory discrimination tasks demonstrating that mu-alpha and mu-beta are highly sensitive to capturing sensorimotor and basal ganglia deficits associated with stuttering with high temporal precision. Novel findings from a non-word repetition (working memory) task are also included. They show reduced mu-alpha suppression in a stuttering group compared to a typically fluent group. Finally, we review current limitations and directions for future research.