Frontiers in Neuroscience

Humans can easily distinguish many sounds in the environment, but speech and music are uniquely important. Previous studies, mostly using fMRI, have identified separate regions of the brain that respond selectively for speech and music. Yet there is little evidence that brain responses are larger and more temporally precise for human-specific sounds like speech and music, as has been found for responses to species-specific sounds in other animals. We recorded EEG as healthy, adult subjects listened to various types of two-second-long natural sounds. By classifying each sound based on the EEG response, we found that speech, music, and impact sounds were classified better than other natural sounds. But unlike impact sounds, the classification accuracy for speech and music dropped for synthesized sounds that have identical \"low-level\" acoustic statistics based on a subcortical model, indicating a selectivity for higher-order features in these sounds. Lastly, the trends in average power and phase consistency of the two-second EEG responses to each sound replicated the patterns of speech and music selectivity observed with classification accuracy. Together with the classification results, this suggests that the brain produces temporally individualized responses to speech and music sounds that are stronger than the responses to other natural sounds. In addition to highlighting the importance of speech and music for the human brain, the techniques used here could be a cost-effective and efficient way to study the human brains selectivity for speech and music in other populations.\n\nHighlightsO_LIEEG responses are stronger to speech and music than to other natural sounds\nC_LIO_LIThis selectivity was not replicated using stimuli with the same acoustic statistics\nC_LIO_LIThese techniques can be a cost-effective way to study speech and music selectivity\nC_LI

This study examines cortical responses to chromatic and luminance stimuli in individuals with normal trichromatic vision, Daltonism, and achromatopsia. Functional magnetic resonance imaging (fMRI) data were collected using stimuli modeled after Wade et al. (2008) to evaluate the differential activation of visual cortical areas. In normal trichromats, hV4 demonstrated the highest chromatic sensitivity, while ventral areas showed stronger responses to color compared to dorsal regions. In Daltonic and achromatopsia participants, cortical activation was observed under combined chromatic and luminance conditions; however, no significant color-specific activity was detected, even in hV4. This work establishes a baseline for understanding cortical responses in color vision deficiencies and preceded gene therapy studies in the same achromatopsia patients (Fischer et al., 2020; Seitz et al., 2022). These findings contribute to ongoing research into neural plasticity and targeted therapeutic interventions.

Natural sounds are easily perceived and identified by humans and animals. Despite this, the neural transformations that enable sound perception remain largely unknown. Neuroscientists are drawing important conclusions about neural decoding that may eventually aid research into the design of brain-machine interfaces (BCIs). It is thought that the time-frequency correlation characteristics of sounds may be reflected in auditory assembly responses in the midbrain and that this may play an important role in identification of natural sounds. In our study, natural sounds will be predicted from multi-unit activity (MUA) signals collected in the inferior colliculus. The temporal correlation values of the MUA signals are converted into images. We used two different segment sizes and thus generated four subsets for the classification. Using pre-trained convolutional neural networks (CNNs), features of the images were extracted and the type of sound heard was classified. For this, we applied transfer learning from Alexnet, GoogleNet and Squeezenet CNNs. The classifiers support vector machines (SVM), k-nearest neighbour (KNN), Naive Bayes and Ensemble were used. The accuracy, sensitivity, specificity, precision and F1 score were measured as evaluation parameters. Considering the trials one by one in each, we obtained an accuracy of 85.69% with temporal correlation images over 1000 ms windows. Using all trials and removing noise, the accuracy increased to 100%.

The quality of stable long-term recordings from chronically implanted electrode arrays is essential for experimental neu-roscience and brain-computer interfaces. This work uses scanning electron microscopy (SEM) to image and analyze eight 96-channel Utah arrays previously implanted in motor cortical regions of four subjects (subject H = 2242 days implanted, F = 1875, U = 2680, C = 594), providing important contributions to a growing body of long-term implant research leveraging this imaging technology. Four of these arrays have been used in electrolytic lesioning experiments (H = 10 lesions, F = 1, U = 4, C = 1), a novel electrolytic perturbation technique using small direct currents. In addition to surveying physical damage, such as biological debris and material deterioration, this work also analyzes whether electrolytic lesioning created damage beyond what is typical for these arrays. These findings also indicate that there are no statistically significant differences between the damage observed on normal electrodes versus electrodes used for electrolytic lesioning, providing evidence that electrolytic lesioning does not significantly affect the quality of chronically implanted electrode arrays. Finally, this work also includes the largest collection of single-electrode SEM images for previously implanted multielectrode Utah arrays, spanning eleven different intact arrays and one broken array. As the clinical relevance of chronically implanted electrodes with single-neuron resolution continues to grow, these images may be used to provide the foundation for a larger public database and inform further electrode design and analyses.

Using brain activity directly as input for assistive tool control can circumvent muscular dysfunction and increase functional independence for physically impaired people. Most invasive motor decoding studies focus on decoding neural signals from the primary motor cortex, which provides a rich but superficial and spatially local signal. Initial non-primary motor cortex decoding endeavors have used distributed recordings to demonstrate decoding of motor activity by grouping electrodes in mesoscale brain regions. While these studies show that there is relevant and decodable movement related information outside the primary motor cortex, these methods are still exclusionary to other mesoscale areas, and do not capture the full informational content of the motor system. In this work, we recorded intracranial EEG of 8 epilepsy patients, including all electrode contacts except those contacts in or adjacent to the central sulcus. We show that executed and imagined movements can be decoded from non-motor areas; combining all non-motor contacts into a lower dimensional representation provides enough information for a Riemannian decoder to reach an area under the curve of 0.83 {+/-} 0.11. Additionally, by training our decoder on executed and testing on imagined movements, we demonstrate that between these two conditions there exists shared distributed information in the beta frequency range. By combining relevant information from all areas into a lower dimensional representation, the decoder was able to achieve high decoding results without information from the primary motor cortex. This representation makes the decoder more robust to perturbations, signal non-stationarities and neural tissue degradation. Our results indicate to look beyond the motor cortex and open up the way towards more robust and more versatile brain-computer interfaces.

In the last decades, planar multi-electrode arrays (MEAs) have been widely used to record activity from in vitro neuronal cell cultures and tissue slices. Though successful, this technique bears some limitations, particularly relevant when applied to three-dimensional (3D) tissue, such as brain slices, spheroids or organoids. For example, planar MEAs signals are informative on just one side of a 3D-organized structure. This limits the interpretation of the results in terms of network functions in a complex structured and hyperconnected brain tissue. Moreover, the side in contact with the MEAs often shows lower oxygenation rates and related vitality issues. To overcome these problems, we empowered a CMOS high-density multi-electrode array (HD-MEA) with thousands of microneedles (needles) of 65-90 m height, able to penetrate and record in-tissue signals, providing for the first time a 3D HD-MEA chip. We propose a CMOS-compatible fabrication process to produce arrays of needles of different widths mounted on large pedestals to create microchannels underneath the tissue. By using cerebellar and cortico-hippocampal slices as a model, we show that the needles efficiently penetrate the 3D tissue while the microchannels allow the flowing of maintenance solutions to increase tissue vitality in the recording sites. These improvements are reflected by the increase in electrodes sensing capabilities, the number of sampled neuronal units (compared to matched planar technology), and the efficiency of compound effects. Importantly, each electrode can also be used to stimulate the tissue with optimal efficiency due to the 3D structure. Furthermore, we demonstrate how the 3D HD-MEA can efficiently penetrate and get outstanding signals from in vitro 3D cellular models as brain spheroids. In conclusion, we describe a new recording device characterized by the highest spatio-temporal resolution reported for a 3D MEA and significant improvements in the quality of recordings, with a high signal-to-noise ratio and improved tissue vitality. The applications of this game-changing technique are countless, opening unprecedented possibilities in the neuroscience field and beyond.

Object Recognition is a task which involves multiple brain areas for successful completion. This assay is non-invasive, is an enriched learning task, and relies upon on encoded memory for successful completion. In this study, we have demonstrated that neonatal mice can perform the task

Somatosensory feedback and attention are critical for the execution of dexterous behaviors. Indeed, tactile and proprioceptive signals are sent to motor cortex such that they can be integrated into the motor plan while the expectation of feedback can shape motor responses. One downside to many of the studies that have found these interactions is that motor cortex has always been studied within the framework of a motor task, potentially squashing many of the smaller signals present and making others hard to disentangle. In this study we specifically investigate the presence of sensory expectations in human motor cortex in the absence of a motor task using two participants implanted with electrode arrays in both sensory and motor cortices, allowing us to identify signals corresponding to sensory expectation with fewer confounds. We found that the deployment of attention to individual fingers results in digit-specific activation of motor cortex while sensory cortex remains unperturbed. Moreover, we observed that, compared to an imagined movement task, the expectation signal in motor cortex existed in a distinct subspace that does not represent pure motor intent. Finally, we found that the expectation signals existed along a somatotopic axis that matched the somatotopic axis revealed by movement intent. This work highlights the fact that many signals are multiplexed in motor cortex.

Murine organotypic brain slice cultures have been widely used in neuroscientific research and are offering the opportunity to study neuronal function under normal and disease conditions. Despite the brought application, the mechanisms governing the maturation of immature cortical circuits in vitro are not well understood. In this study, we present a detailed investigation into the development of the neocortex in vitro. Utilizing a holistic approach, we studied organotypic whole-hemisphere brain slice cultures from postnatal mice and tracked the development of the somatosensory area over a five-week period. Our analysis revealed the maturation of passive and active intrinsic properties of pyramidal cells together with their morphology, closely resembling in vivo development. Detailed Multi-electrode array (MEA) electrophysiological assessments and RNA expression profiling demonstrated stable network properties by two weeks in culture, followed by the transition of spontaneous activity towards more complex patterns including high-frequency oscillations. However, weeks 4 and 5 exhibited increased variability and initial signs of neuronal loss, highlighting the importance of considering developmental stages in experimental design. This comprehensive characterization is vital for understanding the temporal dynamics of the neocortical development in vitro, with implications for neuroscientific research methodologies, particularly in the investigation of diseases such as epilepsy and other neurodevelopmental disorders.

Direction selective neurons in primary visual cortex (area V1) are affected by the aperture problem, i.e., they are only sensitive to motion orthogonal to their preferred orientation. A solution to this problem first emerges in the middle temporal (MT) area, where a subset of neurons (called pattern cells) combine motion information across multiple orientations and directions, becoming sensitive to pattern motion direction. These cells are expected to play a prominent role in subsequent neural processing, but they are intermixed with cells that behave like V1 cells (component cells), and others that do not clearly fall in either group. The picture is further complicated by the finding that cells that behave like pattern cells with one type of pattern, might behave like component cells for another. We recorded from macaque MT neurons using multi-contact electrodes while presenting both type I and unikinetic plaids, in which the components were 1D noise patterns. We found that the indices that have been used in the past to classify neurons as pattern or component cells work poorly when the properties of the stimulus are not optimized for the cell being recorded, as is always the case with multi-contact arrays. We thus propose alternative measures, which considerably ameliorate the problem, and allow us to gain insights in the signals carried by individual MT neurons. We conclude that arranging cells along a component-to-pattern continuum is an oversimplification, and that the signals carried by individual cells only make sense when embodied in larger populations.

Language impairments often arise from severe neurological disorders, prompting the development of neural prosthetics based on electrophysiological signals for the restoration of comprehensible language information. Previous decoding efforts have focused mainly on signals from the cerebral cortex, neglecting the potential contributions of subcortical brain structures to speech decoding in brain-computer interfaces (BCIs). This study aims to explore the role of subcortical structures for speech decoding by utilizing stereotactic electroencephalography (sEEG). Two native Mandarin Chinese speakers, who underwent sEEG implantation for pharmaco-resistant epilepsy, participated in this study. sEEG contacts were primarily located in the superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, thalamus, hippocampus, insular gyrus, amygdala, and parahippocampal gyrus. The participants were asked to read Chinese text, which included 407 Chinese characters (covering all Chinese syllables), displayed on a screen after receiving prompts. 1-30, 30-70 and 70-150 Hz frequency band powers of sEEG signals were used as key features. A deep learning model based on long short-term memory (LSTM) was developed to evaluate the contribution of different brain structures during encoding of speech. Prediction of speech characteristics of consonants (articulatory place and manner) and tone within single words based on the selected features and electrode contact locations was made. Cortical signals were generally better at articulatory place prediction (86.5% accuracy, chance level = 12.5%), while cortical and subcortical signals predicted articulatory manner at similar level (51.5% vs 51.7% accuracy, respectively, chance level = 14.3%). Subcortical signals generated better prediction for tone (around 58.3% accuracy, chance level = 25%). Superior temporal gyrus remains highly relevant during speech decoding for both consonants and tone. Prediction reached the highest level when cortical and subcortical inputs were combined, especially for tone prediction. Our findings indicate that both cortical and subcortical structures can play crucial roles for speech decoding, each contributing to different aspects of speech.

Invasive Brain Computer Interface (BCI) systems through Electrocorticographic (ECoG) signals require efficient recognition of spatiotemporal patterns from a multi-electrodes sensor array. Such signals are excellent candidates for automated pattern recognition through machine learning algorithms. The importance of these patterns can be highlighted through feature extraction techniques. However, the signal variability due to non-stationarity is ignored while extracting features, and which features to use can be challenging to figure out by visual inspection. In this study, we introduce the signal split parameter to account for the variability of the signal and increase the accuracy of the machine learning classifier. We use genetic selection, which allows the selection of the optimal combination of features from a pool of 8 different feature sets. Genetic selection of features increases accuracy and reduces the BCIs prediction time. Along with Genetic selection, we also use a reduced signal length, which leads to a higher Information Transfer Rate. Thus this approach enables the design of a fast and accurate motorrelated EcoG BCI system.

A key challenge in correlating neuronal activity with brain function is the limited sampling probability of neuronal activity in real time. It is crucial to increase the sampling probability substantially and in real time. We hypothesized that 3-dimensional (3D) neural probes offer faster and stronger prospects for cell yield than 2D electrode arrays. We simulated a 1000-neuron neuronal network, mimicking the granular layer of the barrel cortical column, recorded signals from inserted 384 electrodes (organized in 3D or 2D), and sorted units using Kilosrt or triangulation localization. We demonstrated that 3D electrode arrays converge more space for triangulation spike sorting than 2D probes do. 3D neural probes, together with triangulation, could isolate up to 80% of the simulated 1000 neurons (as ground truth) and have a cell yield of up to 5, which is, to the best of our knowledge, significantly higher than standard 2D electrodes with Kilosort or triangulation. With a signal-to-noise ratio (SNR) of 10, which is close to the real world, the simulation data suggest that 3D electrode arrays in a face-centric cubic (FCC) arrangement provide a better cell yield. However, larger background noise (e.g. an SNR of 1, which can be improved with lower electrode impedance) has a stronger impact on the triangulation spike sorting. Since only the peak value of spikes are required for triangulation localization, the computing loading is much less than spike waveform-based spike sorting approach. Thus, combining 3D electrode arrays with triangulation localization is ideal for real-time spike sorting. Thus, we demonstrated that adding one more dimension in designing neural probes can dramatically increase cell yield and speed up isolating neuronal unit activity. We, for the first time, provide a tool for utilizing computer simulations to optimize the design of neural electrode arrays before time-consuming probe fabrication.

BackgroundSuper-resolution reconstruction (SRR) of fetal brain magnetic resonance imaging has the potential to enable the development of new imaging biomarkers to better study in utero neurodevelopment. However, potential biases in 2D biometric and 3D volumetric measurements due to different SRR techniques remain understudied. PurposeTo assess the consistency of biometric and volumetric measurements across three hospitals using three widely used SRR pipelines. Materials and MethodsThis retrospective study used T2-weighted (T2w) fetal brain MRI scans acquired in routine clinical practice at three hospitals. MRIs from each subject were reconstructed with each of the 3 SRR methods. Four experts did biometric measurements on each SRR volume blinded to the method used. Automated 3D volumetry was performed using a state-of-the-art segmentation method. A univariate analysis was first carried out with Friedman tests with post-hoc Wilcoxon rank-sum tests, and results were confirmed in a multivariate analysis accounting for the effect of gestational age and different raters, using a t-distributed generalized additive model. An additional qualitative evaluation was performed to assess how likely clinicians would be to use the current SRR volumes in their practice, and whether they would prefer it to low-resolution T2w acquisitions. Differences were assessed with Friedman tests and post-hoc Wilcoxon rank-sum tests. Results84 healthy subjects were included in three gestational age groups ([21-28): 25.4{+/-}1.9, [28-32): 29.3{+/-}1.3, [32-36): 33.5{+/-}1.2). Statistically significant differences in biometric measurements were found, but consistently remained below voxel width (0.8 mm). Automated 3D volumetry revealed systematic but very small effects (<2.8%). The qualitative evaluation showed systematic differences between SRR methods for the perception of white matter intensity (p=0.02) and sharpness of the image (p=0.01). ConclusionVariations in 2D and 3D quantitative measurements did not show any large systematic bias when using different SRR methods for radiological assessment in clinical routine across multiple centers, scanners, and raters. SummaryDifferent super-resolution reconstruction methods for fetal brain MRI volumes lead to negligible variations in 2D or 3D quantitative measurements; this may help achieve larger sample sizes in prenatal development studies. Key Results- In this multi-centric retrospective study, 252 super-resolution reconstructions (SRR) scans from 84 healthy subjects showed negligible variations in 2D in biometric measures (below the voxel with of 0.8 mm; p<0.001). - 3D measurements revealed small variations ranging from 0.8 % in supratentorial tissues (p<0.001) to 2.8% in the extra-cerebral cerebrospinal fluid (p<0.001). - Clinicians favored having both low resolution and SRR volumes available.

Recordings of electroencephalographic (EEG) rhythms and their analyses have been instrumental in basic Neuroscience, clinical diagnostics, and the field of brain-computer interfaces (BCIs). While in the past such measurements have been conducted mostly in laboratory settings, recent advancements in dry electrode technology pave way to a broader range of consumer and medical application because of their greater convenience compared to gel-based electrodes. Here we conducted resting-state EEG recordings in two groups of healthy participants using three dry-electrode devices, the Neiry Headband, the Neiry Headphones and the Muse Headband, and one standard gel electrode-based system, the NVX. We examined signal quality for various spatial and spectral ranges which are essential for cognitive monitoring and consumer applications. Distinctive characteristics of signal quality were found, with the Neiry Headband showing sensitivity in low-frequency ranges and replicating the modulations of delta, theta and alpha power corresponding to the eyes-open and eyes-closed conditions, and the NVX system performing well in capturing high-frequency oscillations. The Neiry Headphones were more prone to low-frequency artifacts compared to the Neiry Headband, yet recorded modulations in alpha power and had a strong alignment with the NVX at higher frequencies. The Muse Headband had several limitations in signal quality. We suggest that while dry-electrode technology appears to be appropriate for the EEG rhythm-based applications the potential benefits of these technologies in terms of ease of use and accessibility should be carefully weighted against the capacity of each concrete system.

A current challenge of neurotechnologies is the development of speech brain-computer interfaces to restore communication in people unable to speak. To achieve a proof of concept of such system, neural activity of patients implanted for clinical reasons can be recorded while they speak. Using such simultaneously recorded audio and neural data, decoders can be built to predict speech features using features extracted from brain signals. A typical neural feature is the spectral power of field potentials in the high-gamma frequency band (between 70 and 200 Hz), a range that happens to overlap the fundamental frequency of speech. Here, we analyzed human electrocorticographic (ECoG) and intracortical recordings during speech production and perception as well as rat microelectrocorticographic ({micro}-ECoG) recordings during sound perception. We observed that electrophysiological signals, recorded with different recording setups, often contain spectrotemporal features highly correlated with those of the sound, especially within the high-gamma band. The characteristics of these correlated spectrotemporal features support a contamination of electrophysiological recordings by sound. In a recording showing high contamination, using neural features within the high-gamma frequency band dramatically increased the performance of linear decoding of acoustic speech features, while such improvement was very limited for another recording showing weak contamination. Further analysis and in vitro replication suggest that the contamination is caused by a mechanical action of the sound waves onto the cables and connectors along the recording chain, transforming sound vibrations into an undesired electrical noise that contaminates the biopotential measurements. This study does not question the existence of relevant physiological neural information underlying speech production or sound perception in the high-gamma frequency band, but alerts on the fact that care should be taken to evaluate and eliminate any possible acoustic contamination of neural signals in order to investigate the cortical dynamics of these processes.

The states of sleep and wakefulness are critical physiological processes associated with different brain patterns of activity. The intracranial electroencephalogram allows us to measure these changes, thus, it is a critical tool for its study. Recently, we showed that the electrocortical temporal complexity decreased from wakefulness to sleep. Nevertheless, the origin of this complex activity remains a controversial topic due to the existence of possible artifacts contaminating the brain signals. In this work, we showed that complexity decreases during sleep, independently of the electrode configuration employed. This fact strongly suggests that the basis for the behavioral-state differences in complexity does not have an extracranial origin; i.e., generated from the brain.

SYNGAP1 is a critical gene for neuronal development, synaptic structure, and function. Although rare, the disruption of SYNGAP1 directly causes a genetically identifiable neurodevelopmental disorder (NDD) called SYNGAP1-related intellectual disability. Without functional SynGAP1 protein, patients present with intellectual disability, motor impairments, and epilepsy. Previous work using mouse models with a variety of germline and conditional mutations has helped delineate SynGAP1s critical roles in neuronal structure and function, as well as key biochemical signaling pathways essential to synapse integrity. Homozygous loss of SYNGAP1 is embryonically lethal. Heterozygous mutations of SynGAP1 result in a broad range of phenotypes including increased locomotor activity, impaired working spatial memory, impaired cued fear memory, and increased stereotypic behavior. Our in vivo functional data, using the original germline mutation mouse line from the Huganir laboratory, corroborated robust hyperactivity and learning and memory deficits. Here, we describe impairments in the translational biomarker domain of sleep, characterized using neurophysiological data collected with wireless telemetric electroencephalography (EEG). We discovered Syngap1+/- mice exhibited elevated spike trains in both number and duration, in addition to elevated power, most notably in the delta power band. Primary neurons from Syngap1+/- mice displayed increased network firing activity, greater spikes per burst, and shorter inter-burst intervals between peaks using high density micro-electrode arrays (HD-MEA). This work is translational, innovative, and highly significant as it outlines functional impairments in Syngap1 mutant mice. Simultaneously, the work utilized untethered, wireless neurophysiology that can discover potential biomarkers of Syngap1R-ID, for clinical trials, as it has done with other NDDs. Our work is substantial forward progress toward translational work for SynGAP1R-ID as it bridges in-vitro electrophysiological neuronal activity and function with in vivo neurophysiological brain activity and function. These data elucidate multiple quantitative, translational biomarkers in vivo and in vitro for the development of treatments for SYNGAP1-related intellectual disability.

The human auditory sensory area, which includes primary and non-primary auditory cortices, has been considered to locate in the supra-temporal lobe for more than a century. Recently, accumulating evidence shows that the posterior part of insula responses to sounds under non-task states with relevant short latencies. However, whether posterior insula (InsP) contribute to forming auditory sensation remains unclear. Here we addressed this issue by recording and stimulation directly on the supra-temporal and insular areas via intracranial electrodes from 53 epileptic patients. During passive listening to a non-speech sound, the high-{gamma} (60-140 Hz) active rate of InsP (68.8%) was approximate to the non-primary auditory areas (72.4% and 79.0%). Moreover, we could not distinguish InsP from supra-temporal subareas by either activation, latency, temporal pattern or lateral dominance of sound induce high-{gamma}. On the contrary, direct electrical stimulation evoked auditory sensations effectively on supra-temporal subareas (> 65%), while sparsely on InsP (9.49%). The results of cortico-cortical evoked potentials (CCEPs) showed strong bidirectional connectivity within supra-temporal areas, but weak connectivity between supra-temporal areas and InsP. These findings suggest that even the InsP has similar basic auditory response properties to the primary or non-primary cortex, it may not directly participate in the formation of auditory perception.

Many studies have demonstrated that auditory attention to natural speech can be decoded from EEG data. However, most studies focus on selective auditory attention decoding (sAAD) with competing speakers, while the dynamics of absolute auditory attention decoding (aAAD) to a single target remains underexplored. The goal of aAAD is to measure the degree of attention to a single speaker, and it has applications for objective measurements of attention in psychological and educational contexts. To investigate this aAAD paradigm, we designed an experiment where subjects listened to a video lecture under varying attentive conditions. We trained neural decoders to reconstruct the speech envelope from EEG in the baseline attentive condition and use the correlation coefficient between the decoded and real speech envelope as a metric for attention to the speech. Our analysis shows that the envelope standard deviation (SD) of the speech envelope in the 1-4 Hz band strongly correlates with this metric across different segments of the speech stimulus. However, this correlation weakens in the 0.1-4 Hz band, where the degree of separation between the attentive and inattentive state becomes more pronounced. This highlights the unique contribution of the 0.1-1 Hz range, which enhances the distinction of attentional states and remains less affected by confounding factors such as the time-varying dynamic range of the speech envelope.