Electrophysiological correlates of sustained conscious perception.

Previous research on the neural correlates of consciousness (NCC) in visual perception revealed an early event-related potential (ERP), the visual awareness negativity (VAN), to be associated with stimulus awareness. However, due to the use of brief stimulus presentations in previous studies, it remains unclear whether awareness-related negativities represent a transient onset-related response or correspond to the duration of a conscious percept. Studies are required that allow prolonged stimulus presentation under aware and unaware conditions. The present ERP study aimed to tackle this challenge by using a novel stimulation design. Male and female human participants (n = 62) performed a visual task while task-irrelevant line stimuli were presented in the background for either 500 or 1000 ms. The line stimuli sometimes contained a face, which needed so-called visual one-shot learning to be seen. Half of the participants were informed about the presence of the face, resulting in faces being perceived by the informed but not by the uninformed participants. Comparing ERPs between the informed and uninformed group revealed an enhanced negativity over occipitotemporal electrodes that persisted for the entire duration of stimulus presentation. Our results suggest that sustained visual awareness negativities (SVAN) are associated with the duration of stimulus presentation.

Shared EEG correlates between non-REM parasomnia experiences and dreams.

Sleepwalking and related parasomnias result from incomplete awakenings out of non-rapid eye movement sleep. Behavioral episodes can occur without consciousness or recollection, or in relation to dream-like experiences. To understand what accounts for these differences in consciousness and recall, here we recorded parasomnia episodes with high-density electroencephalography (EEG) and interviewed participants immediately afterward about their experiences. Compared to reports of no experience (19%), reports of conscious experience (56%) were preceded by high-amplitude EEG slow waves in anterior cortical regions and activation of posterior cortical regions, similar to previously described EEG correlates of dreaming. Recall of the content of the experience (56%), compared to no recall (25%), was associated with higher EEG activation in the right medial temporal region before movement onset. Our work suggests that the EEG correlates of parasomnia experiences are similar to those reported for dreams and may thus reflect core physiological processes involved in sleep consciousness.

Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine.

The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.

EEG functional connectivity as a Riemannian mediator: An application to malnutrition and cognition.

Mediation analysis assesses whether an exposure directly produces changes in cognitive behavior or is influenced by intermediate "mediators". Electroencephalographic (EEG) spectral measurements have been previously used as effective mediators representing diverse aspects of brain function. However, it has been necessary to collapse EEG measures onto a single scalar using standard mediation methods. In this article, we overcome this limitation and examine EEG frequency-resolved functional connectivity measures as a mediator using the full EEG cross-spectral tensor (CST). Since CST samples do not exist in Euclidean space but in the Riemannian manifold of positive-definite tensors, we transform the problem, allowing for the use of classic multivariate statistics. Toward this end, we map the data from the original manifold space to the Euclidean tangent space, eliminating redundant information to conform to a "compressed CST." The resulting object is a matrix with rows corresponding to frequencies and columns to cross spectra between channels. We have developed a novel matrix mediation approach that leverages a nuclear norm regularization to determine the matrix-valued regression parameters. Furthermore, we introduced a global test for the overall CST mediation and a test to determine specific channels and frequencies driving the mediation. We validated the method through simulations and applied it to our well-studied 50+-year Barbados Nutrition Study dataset by comparing EEGs collected in school-age children (5-11 years) who were malnourished in the first year of life with those of healthy classmate controls. We hypothesized that the CST mediates the effect of malnutrition on cognitive performance. We can now explicitly pinpoint the frequencies (delta, theta, alpha, and beta bands) and regions (frontal, central, and occipital) in which functional connectivity was altered in previously malnourished children, an improvement to prior studies. Understanding the specific networks impacted by a history of postnatal malnutrition could pave the way for developing more targeted and personalized therapeutic interventions. Our methods offer a versatile framework applicable to mediation studies encompassing matrix and Hermitian 3D tensor mediators alongside scalar exposures and outcomes, facilitating comprehensive analyses across diverse research domains.

Evidence of elevated situational awareness for active duty soldiers during navigation of a virtual environment.

U.S. service members maintain constant situational awareness (SA) due to training and experience operating in dynamic and complex environments. Work examining how military experience impacts SA during visual search of a complex naturalistic environment, is limited. Here, we compare Active Duty service members and Civilians' physiological behavior during a navigational visual search task in an open-world virtual environment (VE) while cognitive load was manipulated. We measured eye-tracking and electroencephalogram (EEG) outcomes from Active Duty (N = 21) and Civilians (N = 15) while they navigated a desktop VE at a self-regulated pace. Participants searched and counted targets (N = 15) presented among distractors, while cognitive load was manipulated with an auditory Math Task. Results showed Active Duty participants reported significantly greater/closer to the correct number of targets compared to Civilians. Overall, Active Duty participants scanned the VE with faster peak saccade velocities and greater average saccade magnitudes compared to Civilians. Convolutional Neural Network (CNN) response (EEG P-300) was significantly weighted more to initial fixations for the Active Duty group, showing reduced attentional resources on object refixations compared to Civilians. There were no group differences in fixation outcomes or overall CNN response when comparing targets versus distractor objects. When cognitive load was manipulated, only Civilians significantly decreased their average dwell time on each object and the Active Duty group had significantly fewer numbers of correct answers on the Math Task. Overall, the Active Duty group explored the VE with increased scanning speed and distance and reduced cognitive re-processing on objects, employing a different, perhaps expert, visual search strategy indicative of increased SA. The Active Duty group maintained SA in the main visual search task and did not appear to shift focus to the secondary Math Task. Future work could compare how a stress inducing environment impacts these groups' physiological or cognitive markers and performance for these groups.

Subacute sclerosing panencephalitis presenting as severe depression in an adult.

Subacute sclerosing panencephalitis (SSPE) is a fatal disorder that occurs as a rare complication of childhood measles. Symptoms typically manifest between the ages of 5 and 15. While the incidence of SSPE is declining globally, it is still prevalent in regions where measles remains common and vaccination rates are low due to poverty and lack of health education. Diagnosing SSPE can be challenging, particularly when patients exhibit unusual symptoms. A thorough clinical evaluation, including vaccination history, physical examination, electroencephalogram (EEG) and Cerebrospinal fluid (CSF) analysis, can help in making a diagnosis. We present the case of a young woman in her early 20s who initially experienced depressive symptoms, followed by myoclonus, dementia and visual impairment. The patient was ultimately diagnosed with SSPE based on characteristic EEG findings, neuroimaging results, CSF analysis and elevated serum measles antibody levels.

Animal cognition: Dogs build semantic expectations between spoken words and objects.

A recent study has used scalp-recorded electroencephalography to obtain evidence of semantic processing of human speech and objects by domesticated dogs. The results suggest that dogs do comprehend the meaning of familiar spoken words, in that a word can evoke the mental representation of the object to which it refers.

Eye movement intervention facilitates concurrent perception and memory processing.

A widely used psychotherapeutic treatment for post-traumatic stress disorder (PTSD) involves performing bilateral eye movement (EM) during trauma memory retrieval. However, how this treatment-described as eye movement desensitization and reprocessing (EMDR)-alleviates trauma-related symptoms is unclear. While conventional theories suggest that bilateral EM interferes with concurrently retrieved trauma memories by taxing the limited working memory resources, here, we propose that bilateral EM actually facilitates information processing. In two EEG experiments, we replicated the bilateral EM procedure of EMDR, having participants engaging in continuous bilateral EM or receiving bilateral sensory stimulation (BS) as a control while retrieving short- or long-term memory. During EM or BS, we presented bystander images or memory cues to probe neural representations of perceptual and memory information. Multivariate pattern analysis of the EEG signals revealed that bilateral EM enhanced neural representations of simultaneously processed perceptual and memory information. This enhancement was accompanied by heightened visual responses and increased neural excitability in the occipital region. Furthermore, bilateral EM increased information transmission from the occipital to the frontoparietal region, indicating facilitated information transition from low-level perceptual representation to high-level memory representation. These findings argue for theories that emphasize information facilitation rather than disruption in the EMDR treatment.

Minimum spanning tree analysis of EEG resting-state functional networks in schizophrenia.

Schizophrenia is a serious and complex mental disease, known to be associated with various subtle structural and functional deviations in the brain. Recently, increased attention is given to the analysis of brain-wide, global mechanisms, strongly altering the communication of long-distance brain areas in schizophrenia. Data of 32 patients with schizophrenia and 28 matched healthy control subjects were analyzed. Two minutes long 64-channel EEG recordings were registered during resting, eyes closed condition. Average connectivity strength was estimated with Weighted Phase Lag Index (wPLI) in lower frequencies: delta and theta, and Amplitude Envelope Correlation with leakage correction (AEC-c) in higher frequencies: alpha, beta, lower gamma and higher gamma. To analyze functional network topology Minimum Spanning Tree (MST) algorithms were applied. Results show that patients have weaker functional connectivity in delta and alpha frequency bands. Concerning network differences, the result of lower diameter, higher leaf number, and also higher maximum degree and maximum betweenness centrality in patients suggest a star-like, and more random network topology in patients with schizophrenia. Our findings are in accordance with some previous findings based on resting-state EEG (and fMRI) data, suggesting that MST network structure in schizophrenia is biased towards a less optimal, more centralized organization.

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network.

Emotion is a human sense that can influence an individual's life quality in both positive and negative ways. The ability to distinguish different types of emotion can lead researchers to estimate the current situation of patients or the probability of future disease. Recognizing emotions from images have problems concealing their feeling by modifying their facial expressions. This led researchers to consider Electroencephalography (EEG) signals for more accurate emotion detection. However, the complexity of EEG recordings and data analysis using conventional machine learning algorithms caused inconsistent emotion recognition. Therefore, utilizing hybrid deep learning models and other techniques has become common due to their ability to analyze complicated data and achieve higher performance by integrating diverse features of the models. However, researchers prioritize models with fewer parameters to achieve the highest average accuracy. This study improves the Convolutional Fuzzy Neural Network (CFNN) for emotion recognition using EEG signals to achieve a reliable detection system. Initially, the pre-processing and feature extraction phases are implemented to obtain noiseless and informative data. Then, the CFNN with modified architecture is trained to classify emotions. Several parametric and comparative experiments are performed. The proposed model achieved reliable performance for emotion recognition with an average accuracy of 98.21% and 98.08% for valence (pleasantness) and arousal (intensity), respectively, and outperformed state-of-the-art methods.

The relationship between gamma-band neural oscillations and language skills in youth with Autism Spectrum Disorder and their first-degree relatives.

BACKGROUND: Most children with Autism Spectrum Disorder (ASD) have co-occurring language impairments and some of these autism-specific language difficulties are also present in their non-autistic first-degree relatives. One of the possible neural mechanisms associated with variability in language functioning is alterations in cortical gamma-band oscillations, hypothesized to be related to neural excitation and inhibition balance. METHODS: We used a high-density 128-channel electroencephalography (EEG) to register brain response to speech stimuli in a large sex-balanced sample of participants: 125 youth with ASD, 121 typically developing (TD) youth, and 40 unaffected siblings (US) of youth with ASD. Language skills were assessed with Clinical Evaluation of Language Fundamentals. RESULTS: First, during speech processing, we identified significantly elevated gamma power in ASD participants compared to TD controls. Second, across all youth, higher gamma power was associated with lower language skills. Finally, the US group demonstrated an intermediate profile in both language and gamma power, with nonverbal IQ mediating the relationship between gamma power and language skills. LIMITATIONS: We only focused on one of the possible neural contributors to variability in language functioning. Also, the US group consisted of a smaller number of participants in comparison to the ASD or TD groups. Finally, due to the timing issue in EEG system we have provided only non-phase-locked analysis. CONCLUSIONS: Autistic youth showed elevated gamma power, suggesting higher excitation in the brain in response to speech stimuli and elevated gamma power was related to lower language skills. The US group showed an intermediate pattern of gamma activity, suggesting that the broader autism phenotype extends to neural profiles.

Delineating memory reactivation in sleep with verbal and non-verbal retrieval cues.

Sleep supports memory consolidation via the reactivation of newly formed memory traces. One way to investigate memory reactivation in sleep is by exposing the sleeping brain to auditory retrieval cues; a paradigm known as targeted memory reactivation. To what extent the acoustic properties of memory cues influence the effectiveness of targeted memory reactivation, however, has received limited attention. We addressed this question by exploring how verbal and non-verbal memory cues affect oscillatory activity linked to memory reactivation in sleep. Fifty-one healthy male adults learned to associate visual stimuli with spoken words (verbal cues) and environmental sounds (non-verbal cues). Subsets of the verbal and non-verbal memory cues were then replayed during sleep. The voice of the verbal cues was either matched or mismatched to learning. Memory cues (relative to unheard control cues) prompted an increase in theta/alpha and spindle power, which have been heavily implicated in sleep-associated memory processing. Moreover, verbal memory cues were associated with a stronger increase in spindle power than non-verbal memory cues. There were no significant differences between the matched and mismatched verbal cues. Our findings suggest that verbal memory cues may be most effective for triggering memory reactivation in sleep, as indicated by an amplified spindle response.

Testing conditional quantile independence with functional covariate.

We propose a new non-parametric conditional independence test for a scalar response and a functional covariate over a continuum of quantile levels. We build a Cramer-von Mises type test statistic based on an empirical process indexed by random projections of the functional covariate, effectively avoiding the "curse of dimensionality" under the projected hypothesis, which is almost surely equivalent to the null hypothesis. The asymptotic null distribution of the proposed test statistic is obtained under some mild assumptions. The asymptotic global and local power properties of our test statistic are then investigated. We specifically demonstrate that the statistic is able to detect a broad class of local alternatives converging to the null at the parametric rate. Additionally, we recommend a simple multiplier bootstrap approach for estimating the critical values. The finite-sample performance of our statistic is examined through several Monte Carlo simulation experiments. Finally, an analysis of an EEG data set is used to show the utility and versatility of our proposed test statistic.

[Risk of epilepsy after a first epileptic seizure with unknown etiology in elderly patients]. / Riesgo de epilepsia tras una primera crisis epiléptica de etiología desconocida en pacientes de edad avanzada.

AIM: Patients whose epilepsy begins with seizures with unknown etiology in old age have been studied to a limited extent. The aim is to clinically characterise these patients, and predict their risk of developing epilepsy in the long term. MATERIALS AND METHODS: This is a retrospective observational study of patients over 55 years old experiencing a first epileptic seizure with unknown etiology. The data were collected from their clinical history, including electroencephalogram (EEG) and brain magnetic resonance imaging (MRI) results. RESULTS: Eighty-seven patients (58.6% male; 71.5 ± 8.1 years) were included. The mean follow-up was 7.3 ± 4.9 years. The most common vascular risk factor was arterial hypertension (77%; n = 67). Focal seizures with altered consciousness were the most frequent type of seizure (44.8%; n = 39), followed by focal seizures evolving to bilateral tonic-clonic seizures (39.1%; n = 34). Brain MRI showed cortical atrophy (50%; n = 42) and signs of small-vessel vascular disease (SVVD) (67.8%; n = 57). Interictal epileptiform EEG abnormalities were observed in 43.7% (n = 38) of the patients, mostly with temporal localisations (94.7%; n = 36). 44.8% (n = 39) had mild cognitive impairment at baseline. Recurrence of seizures, which was observed in 49 patients (56.1%), occurred after a median of 12 months (interquartile range: 4.4-25.9). Finally, 71 patients (81.6%) developed epilepsy. CONCLUSION: The risk of epilepsy in the long term following a single seizure of unknown etiology in elderly patients is greater than 80%. Arterial hypertension and mild cognitive impairment at baseline are the most common clinical features. Cortical atrophy and the presence of SVVD are frequent in MRI, and routine EEGs do not usually show epileptiform alterations.

TITLE: Riesgo de epilepsia tras una primera crisis epiléptica de etiología desconocida en pacientes de edad avanzada.Objetivo. Los pacientes que comienzan con crisis de origen desconocido en la edad avanzada no están bien estudiados. El objetivo es caracterizar clínicamente a estos pacientes y predecir el riesgo de desarrollar epilepsia a largo plazo. Materiales y métodos. Es un estudio observacional retrospectivo en pacientes mayores de 55 años con una primera crisis epiléptica de causa desconocida. Se recogieron los datos desde la historia clínica, incluyendo electroencefalograma (EEG) y resonancia magnética (RM) cerebral. Resultados. Se incluyó a 87 pacientes (58,6% varones; 71,5 ± 8,1 años). El seguimiento medio fue de 7,3 ± 4,9 años. El factor de riesgo vascular más frecuente fue la hipertensión arterial (77%; n = 67). Las crisis focales con alteración de la conciencia fueron el tipo de crisis más frecuente (44,8%; n = 39), seguidas de las crisis focales con evolución a bilaterales tonicoclónicas (39,1%; n = 34). La RM cerebral mostró atrofia cortical (50%; n = 42) y signos de enfermedad vascular de pequeño vaso (EVPV) (67,8%; n = 57). Se observaron anomalías epileptiformes intercríticas en el EEG en un 43,7% (n = 38) de los pacientes, mayoritariamente con localización temporal (94,7%; n = 36). Hasta un 44,8% (n = 39) presentaba deterioro cognitivo leve basalmente. La recurrencia de crisis, observada en 49 pacientes (56,1%), sucedió con una mediana de 12 meses (rango intercuartílico: 4,4-25,9). Finalmente, 71 pacientes (81,6%) desarrollaron epilepsia. Conclusión. El riesgo de epilepsia a largo plazo tras una crisis única de etiología desconocida en pacientes de edad avanzada es superior al 80%. La hipertensión arterial y el deterioro cognitivo leve en el inicio son las características clínicas más frecuentes. En la RM, la atrofia cortical y la presencia de EVPV son frecuentes, y los EEG de rutina no suelen mostrar alteraciones epileptiformes.

Spatio-temporal electroencephalographic power distribution in experimental pigs receiving propofol.

INTRODUCTION: When assessing the spatio-temporal distribution of electroencephalographic (EEG) activity, characteristic patterns have been identified for several anesthetic drugs in humans. A shift in EEG power from the occipital to the prefrontal regions has been widely observed during anesthesia induction. This has been called "anteriorization" and has been correlated with loss of consciousness in humans. The spatio-temporal distribution of EEG spectral power in pigs and its modulation by anesthetics have not been described previously. The aim of the present study was to analyze EEG power across an anterior-posterior axis in pigs receiving increasing doses of propofol to 1) characterize the region of highest EEG power during wakefulness, 2) depict its spatio-temporal modification during propofol infusion, and 3) determine the region demonstrating the most significant modulations across different doses administered. MATERIALS AND METHODS: Six pigs with a body weight of 33.3 ± 3.6 kg and aged 11.3 ± 0.5 weeks were included in a prospective experimental study. Electroencephalographic activity was collected at the occipital, parietal and prefrontal regions at increasing doses of propofol (starting at 10 mg kg-1 h-1 and increasing it by 10 mg kg-1 h-1 every 15 minutes). The EEG power was assessed using a generalized linear mixed model in which propofol doses and regions were treated as fixed effects, whereas pig was used as a random effect. Pairwise comparisons of marginal linear predictions were used to assess the change in power when the specific propofol dose (or region) was considered. RESULTS: During both wakefulness and propofol infusion, the highest EEG power was located in the prefrontal region (p<0.001). The EEG power, both total and for each frequency band, mostly followed the same pattern, increasing from awake until propofol 20 mg kg-1 h-1 and then decreasing at propofol 30 mg kg-1 h-1. The region showing the strongest differences in EEG power across propofol doses was the prefrontal. CONCLUSION: In juvenile pigs receiving increasing doses of propofol, the prefrontal region showed the highest EEG power both during wakefulness and propofol administration and was the area in which the largest frequency-band specific variations were observed across different anesthetic doses. The assessment of the spectral EEG activity at this region could be favorable to distinguish DoA levels in pigs.

Investigating sensitivity to multi-domain prediction errors in chronic auditory phantom perception.

The perception of a continuous phantom in a sensory domain in the absence of an external stimulus is explained as a maladaptive compensation of aberrant predictive coding, a proposed unified theory of brain functioning. If this were true, these changes would occur not only in the domain of the phantom percept but in other sensory domains as well. We confirm this hypothesis by using tinnitus (continuous phantom sound) as a model and probe the predictive coding mechanism using the established local-global oddball paradigm in both the auditory and visual domains. We observe that tinnitus patients are sensitive to changes in predictive coding not only in the auditory but also in the visual domain. We report changes in well-established components of event-related EEG such as the mismatch negativity. Furthermore, deviations in stimulus characteristics were correlated with the subjective tinnitus distress. These results provide an empirical confirmation that aberrant perceptions are a symptom of a higher-order systemic disorder transcending the domain of the percept.

FetchEEG: a hybrid approach combining feature extraction and temporal-channel joint attention for EEG-based emotion classification.

Objective. Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting.Approach. To address these challenges, this paper proposes a hybrid approach named FetchEEG that combines feature extraction and temporal-channel joint attention. Leveraging the advantages of both traditional feature extraction and deep learning, the FetchEEG adopts a multi-head self-attention mechanism to extract representations between different time moments and channels simultaneously. The joint representations are then concatenated and classified using fully-connected layers for emotion recognition. The performance of the FetchEEG is verified by comparison experiments on a self-developed dataset and two public datasets.Main results. In both subject-dependent and subject-independent experiments, the FetchEEG demonstrates better performance and stronger generalization ability than the state-of-the-art methods on all datasets. Moreover, the performance of the FetchEEG is analyzed for different sliding window sizes and overlap rates in the feature extraction module. The sensitivity of emotion recognition is investigated for three- and five-frequency-band scenarios.Significance. FetchEEG is a novel hybrid method based on EEG for emotion classification, which combines EEG feature extraction with Transformer neural networks. It has achieved state-of-the-art performance on both self-developed datasets and multiple public datasets, with significantly higher training efficiency compared to end-to-end methods, demonstrating its effectiveness and feasibility.

Neurovascular Coupling Analysis Based on Multivariate Variational Gaussian Process Convergent Cross-Mapping.

Neurovascular coupling (NVC) provides important insights into the intricate activity of brain functioning and may aid in the early diagnosis of brain diseases. Emerging evidences have shown that NVC could be assessed by the coupling between electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). However, this endeavor presents significant challenges due to the absence of standardized methodologies and reliable techniques for coupling analysis of these two modalities. In this study, we introduced a novel method, i.e., the collaborative multi-output variational Gaussian process convergent cross-mapping (CMVGP-CCM) approach to advance coupling analysis of EEG and fNIRS. To validate the robustness and reliability of the CMVGP-CCM method, we conducted extensive experiments using chaotic time series models with varying noise levels, sequence lengths, and causal driving strengths. In addition, we employed the CMVGP-CCM method to explore the NVC between EEG and fNIRS signals collected from 26 healthy participants using a working memory (WM) task. Results revealed a significant causal effect of EEG signals, particularly the delta, theta, and alpha frequency bands, on the fNIRS signals during WM. This influence was notably observed in the frontal lobe, and its strength exhibited a decline as cognitive demands increased. This study illuminates the complex connections between brain electrical activity and cerebral blood flow, offering new insights into the underlying NVC mechanisms of WM.

Extending Subcortical EEG Responses to Continuous Speech to the Sound-Field.

The auditory brainstem response (ABR) is a valuable clinical tool for objective hearing assessment, which is conventionally detected by averaging neural responses to thousands of short stimuli. Progressing beyond these unnatural stimuli, brainstem responses to continuous speech presented via earphones have been recently detected using linear temporal response functions (TRFs). Here, we extend earlier studies by measuring subcortical responses to continuous speech presented in the sound-field, and assess the amount of data needed to estimate brainstem TRFs. Electroencephalography (EEG) was recorded from 24 normal hearing participants while they listened to clicks and stories presented via earphones and loudspeakers. Subcortical TRFs were computed after accounting for non-linear processing in the auditory periphery by either stimulus rectification or an auditory nerve model. Our results demonstrated that subcortical responses to continuous speech could be reliably measured in the sound-field. TRFs estimated using auditory nerve models outperformed simple rectification, and 16 minutes of data was sufficient for the TRFs of all participants to show clear wave V peaks for both earphones and sound-field stimuli. Subcortical TRFs to continuous speech were highly consistent in both earphone and sound-field conditions, and with click ABRs. However, sound-field TRFs required slightly more data (16 minutes) to achieve clear wave V peaks compared to earphone TRFs (12 minutes), possibly due to effects of room acoustics. By investigating subcortical responses to sound-field speech stimuli, this study lays the groundwork for bringing objective hearing assessment closer to real-life conditions, which may lead to improved hearing evaluations and smart hearing technologies.

Long-lasting negativity in the left motoric brain structures during word memory inhibition in the Think/No-Think paradigm.

In this study, we investigated the electrical brain responses in a high-density EEG array (64 electrodes) elicited specifically by the word memory cue in the Think/No-Think paradigm in 46 participants. In a first step, we corroborated previous findings demonstrating sustained and reduced brain electrical frontal and parietal late potentials elicited by memory cues following the No-Think (NT) instructions as compared to the Think (T) instructions. The topographical analysis revealed that such reduction was significant 1000 ms after memory cue onset and that it was long-lasting for 1000 ms. In a second step, we estimated the underlying brain generators with a distributed method (swLORETA) which does not preconceive any localization in the gray matter. This method revealed that the cognitive process related to the inhibition of memory retrieval involved classical motoric cerebral structures with the left primary motor cortex (M1, BA4), thalamus, and premotor cortex (BA6). Also, the right frontal-polar cortex was involved in the T condition which we interpreted as an indication of its role in the maintaining of a cognitive set during remembering, by the selection of one cognitive mode of processing, Think, over the other, No-Think, across extended periods of time, as it might be necessary for the successful execution of the Think/No-Think task.