From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People.

OBJECTIVE: Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG. METHODS AND PROCEDURES: The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches. RESULTS: Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process. CONCLUSION: Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques. CLINICAL IMPACT: An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.

A Spatiotemporal Deep Learning Framework for Scalp EEG-Based Automated Pain Assessment in Children.

OBJECTIVE: Common pain assessment approaches such as self-evaluation and observation scales are inappropriate for children as they require patients to have reasonable communication ability. Subjective, inconsistent, and discontinuous pain assessment in children may reduce therapeutic effectiveness and thus affect their later life. METHODS: To address the need for suitable assessment measures, this paper proposes a spatiotemporal deep learning framework for scalp electroencephalogram (EEG)-based automated pain assessment in children. The dataset comprises scalp EEG data recorded from 33 pediatric patients with an arterial puncture as a pain stimulus. Two electrode reduction plans in line with clinical findings are proposed. Combining three-dimensional hand-crafted features and preprocessed raw signals, the proposed transformer-based pain assessment network (STPA-Net) integrates both spatial and temporal information. RESULTS: STPA-Net achieves superior performance with a subject-independent accuracy of 87.83% for pain recognition, and outperforms other state-of-the-art approaches. The effectiveness of electrode combinations is explored to analyze pain-related cortical activities and correspondingly reduce cost. The two proposed electrode reduction plans both demonstrate competitive pain assessment performance qualitatively and quantitatively. CONCLUSION AND SIGNIFICANCE: This study is the first to develop a scalp EEG-based automated pain assessment for children adopting a method that is objective, standardized, and consistent. The findings provide a potential reference for future clinical research.

One out of ten independent components shows flipped polarity with poorer data quality: EEG database study.

Independent component analysis (ICA) is widely used today for scalp-recorded EEG analysis. One of the limitations of ICA-based analysis is polarity indeterminacy. It is not easy to find detailed documentations that explains engineering solutions of how the polarity indeterminacy is addressed in a given implementation. We investigated how it is implemented in the case of EEGLAB and also the relation between the outcome of the polarity determination and classification of independent components (ICs) in terms of the estimated nature of the sources (brain, muscle, eye, etc.) using an open database of n = 212 EEG dataset of resting state recordings. We found that (1) about 91% of ICs showed positive-dominant IC scalp topographies; (2) positive-dominant ICs were more associated with brain-originated signals; (3) positive-dominant ICs showed more radial (peaked at 10-30 degrees deviations from the radial axis) dipolar projection pattern with less residual variance from fitting the equivalent current dipole. In conclusion, using the EEGLAB's default ICA algorithm, one out of 10 ICs results in flipping its polarity to negative, which is associated with non-radial dipole orientation with higher residual variance. Thus, we determined EEGLAB biases toward positive polarity in decomposing high-quality brain ICs.

Comparison of Sub-Scalp EEG and Endovascular Stent-Electrode Array for Visual Evoked Potential Brain-Computer Interface.

Brain-computer interfaces (BCI) have the potential to improve the quality of life for persons with paralysis. Sub-scalp EEG provides an alternative BCI signal acquisition method that compromises between the limitations of traditional EEG systems and the risks associated with intracranial electrodes, and has shown promise in long-term seizure monitoring. However, sub-scalp EEG has not yet been assessed for suitability in BCI applications. This study presents a preliminary comparison of visual evoked potentials (VEPs) recorded using sub-scalp and endovascular stent electrodes in a sheep. Sub-scalp electrodes recorded comparable VEP amplitude, signal-to-noise ratio and bandwidth to the stent electrodes.Clinical relevance-This is the first study to report a comparision between sub-scalp and stent electrode array signals. The use of sub-scalp EEG electrodes may aid in the long-term use of brain-computer interfaces.

A true response of the brain network during electroacupuncture stimulation at scalp acupoints: An fMRI with simultaneous EAS study.

OBJECTIVES: The aim of this study was to explore simultaneous brain network responses to electroacupuncture stimulation (EAS) at scalp acupoints by accounting for placebo effects. MATERIALS AND METHODS: Sixty healthy subjects were recruited and randomly divided into two groups: Group 1 and Group 2. Functional magnetic resonance imaging (fMRI) was performed in Group 1 with sham acupuncture stimulation at acupoints Shenting (GV24) and Touwei (ST8) without EAS. Group 2 underwent verum EAS at the same acupoints during fMRI. Independent component analysis was used to analyze the fMRI data. Full-factor statistical analysis was used to compare the differences in fMRI data between the two groups and evaluate the changes in functional connectivity in brain networks after verum electrical stimulation (Group 1 [after sham electrical current stimulation - before sham electrical current stimulation] - Group 2 [after verum electrical current stimulation - before verum electrical current stimulation]) (p <.001, extent threshold k = 20 voxels). RESULTS: Six brain networks were identified. Significant increased functional connectivity was observed in the right and left executive control networks, sensorimotor network, and attention network, while decreased functional connectivity was mainly found in the default mode network. There were no statistically significant differences in the salience network. CONCLUSIONS: fMRI with simultaneous EAS provides a method to explore brain network responses due to EAS at scalp acupoints. The networks responsible for cognition are differentially activated by EAS in a coordinated manner.

Temporal scaling of human scalp-recorded potentials.

Much of human behavior is governed by common processes that unfold over varying timescales. Standard event-related potential analysis assumes fixed-duration responses relative to experimental events. However, recent single-unit recordings in animals have revealed neural activity scales to span different durations during behaviors demanding flexible timing. Here, we employed a general linear modeling approach using a combination of fixed-duration and variable-duration regressors to unmix fixed-time and scaled-time components in human magneto-/electroencephalography (M/EEG) data. We use this to reveal consistent temporal scaling of human scalp-recorded potentials across four independent electroencephalogram (EEG) datasets, including interval perception, production, prediction, and value-based decision making. Between-trial variation in the temporally scaled response predicts between-trial variation in subject reaction times, demonstrating the relevance of this temporally scaled signal for temporal variation in behavior. Our results provide a general approach for studying flexibly timed behavior in the human brain.

Scalp recorded theta activity is modulated by reward, direction, and speed during virtual navigation in freely moving humans.

Theta oscillations (~ 4-12 Hz) are dynamically modulated by speed and direction in freely moving animals. However, due to the paucity of electrophysiological recordings of freely moving humans, this mechanism remains poorly understood. Here, we combined mobile-EEG with fully immersive virtual-reality to investigate theta dynamics in 22 healthy adults (aged 18-29 years old) freely navigating a T-maze to find rewards. Our results revealed three dynamic periods of theta modulation: (1) theta power increases coincided with the participants' decision-making period; (2) theta power increased for fast and leftward trials as subjects approached the goal location; and (3) feedback onset evoked two phase-locked theta bursts over the right temporal and frontal-midline channels. These results suggest that recording scalp EEG in freely moving humans navigating a simple virtual T-maze can be utilized as a powerful translational model by which to map theta dynamics during "real-life" goal-directed behavior in both health and disease.

Scalp HFO rates decrease after successful epilepsy surgery and are not impacted by the skull defect resulting from craniotomy.

Epilepsy surgery can achieve seizure freedom in selected pediatric candidates, but reliable postsurgical predictors of seizure freedom are missing. High frequency oscillations (HFO) in scalp EEG are a new and promising biomarker of treatment response. However, it is unclear if the skull defect resulting from craniotomy interferes with HFO detection in postsurgical recordings. We considered 14 children with focal lesional epilepsy who underwent presurgical evaluation, epilepsy surgery, and postsurgical follow-up of ≥ 1 year. We identified the nearest EEG electrodes to the skull defect in the postsurgical MRI. We applied a previously validated automated HFO detector to determine HFO rates in presurgical and postsurgical EEG. Overall, HFO rates showed a positive correlation with seizure frequency (p < 0.001). HFO rates in channels over the HFO area decreased following successful epilepsy surgery, irrespective of their proximity to the skull defect (p = 0.005). HFO rates in channels outside the HFO area but near the skull defect showed no increase following surgery (p = 0.091) and did not differ from their contralateral channels (p = 0.726). Our observations show that the skull defect does not interfere with postsurgical HFO detection. This supports the notion that scalp HFO can predict postsurgical seizure freedom and thus guide therapy management in focal lesional epilepsy.

Developmentally interdependent stretcher-compressor relationship between the embryonic brain and the surrounding scalp in the preosteogenic head.

BACKGROUND: How developing brains mechanically interact with the surrounding embryonic scalp layers (ie, epidermal and mesenchymal) in the preosteogenic head remains unknown. Between embryonic day (E) 11 and E13 in mice, before ossification starts in the skull vault, the angle between the pons and the medulla decreases, raising the possibility that when the elastic scalp is directly pushed outward by the growing brain and thus stretched, it recoils inward in response, thereby confining and folding the brain. RESULTS: Stress-release tests showed that the E11-13 scalp recoiled and that the in vivo prestretch prerequisite for this recoil was physically dependent on the brain (pressurization at 77-93 Pa) and on actomyosin and elastin within the scalp. In scalp-removed heads, brainstem folding was reduced, and the spreading of ink from the lateral ventricle to the spinal cord that occurred in scalp-intact embryos (with >5 µL injection) was lost, suggesting roles of the embryonic scalp in brain morphogenesis and cerebrospinal fluid homeostasis. Under nonstretched conditions, scalp cell proliferation declined, while the restretching of the shrunken scalp rescued scalp cell proliferation. CONCLUSIONS: In the embryonic mouse head before ossification, a stretcher-compressor relationship elastically develops between the brain and the scalp, underlying their mechanically interdependent development.

Electrode scalp impedance differences between electroencephalography machines in healthy dogs.

Scalp electrode impedance measurements recorded by wired and wireless electroencephalography (EEG) machines in 7 healthy dogs were compared. Eight recordings resulted in 80 impedance readings from subdermal wire electrodes (locations F7/F8, F3/F4, T3/T4, C3/C4, Fz, and Cz). Impedance values were measured first from the wired and then the wireless EEG machine. Wireless impedance measurements were higher than the wired EEG machine in 79/80 readings (P ≤ 0.05), being on average 2.83 kΩ [P ≤ 0.05, 95% confidence interval (CI): 2.51 to 3.14, SD = 1.42] higher. Impedances from the wired machine ranged between < 0.5 and 9 kΩ (mean = 3.09, median = 2.00, SD = 2.15), whereas impedances from the wireless machine ranged between 2.69 and 6.07 kΩ (mean = 5.92, median = 5.05, SD = 2.59). Despite these differences in impedance measurements, both machines measured similar impedance patterns. The wireless EEG machine's impedance measurements, therefore, should be acceptable for veterinary clinical settings.

Les mesures d'impédance des électrodes du cuir chevelu enregistrées par des appareils EEG filaires et sans fil chez sept chiens en bonne santé ont été comparées. Huit enregistrements ont donné 80 lectures d'impédance à partir de fils-électrodes sous-cutanés (emplacements F7/F8, F3/F4, T3/T4, C3/C4, Fz et Cz). Les valeurs d'impédance ont été mesurées d'abord à partir de la machine EEG filaire puis sans fil. Les mesures d'impédance sans fil étaient plus élevées que l'EEG filaire dans 79/80 lectures (P ≤ 0,05), étant en moyenne de 2,83 kΩ [P ≤ 0,05, intervalle de confiance (IC) à 95 % : 2,51 à 3,14, SD = 1,42] plus élevé. Les impédances de la machine filaire étaient comprises entre < 0,5 et 9 kΩ (moyenne = 3,09, médiane = 2,00, SD = 2,15), tandis que les impédances de la machine sans fil étaient comprises entre 2,69 et 6,07 kΩ (moyenne = 5,92, médiane = 5,05, SD = 2,59). Malgré ces différences dans les mesures d'impédance, les deux machines ont mesuré des patrons d'impédance similaires. Les mesures d'impédance de la machine EEG sans fil doivent donc être acceptables pour les paramètres cliniques vétérinaires.(Traduit par Docteur Serge Messier).

Early nociceptive evoked potentials (NEPs) recorded from the scalp.

OBJECTIVE: Neurophysiological investigation of nociceptive pathway has so far been limited to late cortical responses. We sought to detect early components of the cortical evoked potentials possibly reflecting primary sensory activity. METHODS: The 150 IDE micropatterned electrode was used to selectively activate Aδ intraepidermic fibres of the right hand dorsum in 25 healthy subjects and 3 patients suffering from trigeminal neuralgia. Neurographic recordings were performed to assess type of stimulated fibres and check selectivity. Cortical evoked potentials were recorded from C3'-Fz and Cz-Au1. RESULTS: Neurographic recordings confirmed selective activation of Aδ fibres. Early components were detected after repetitive stimulation (0.83/s rate and 250-500 averages); the first negative component occured at 40 ms (N40) on the contralateral scalp. CONCLUSIONS: The provided data support the hypothesis that N40 could be the cortical primary response conducted by fast Aδ fibres. SIGNIFICANCE: This is the first report of early, possibly primary, cortical responses in humans by nociceptive peripheral stimulation. Although not perfected yet to allow widespread diagnostic use, this is probably the only method to allow fully objective evaluation of the nociceptive system, with important future implications in experimental and clinical neurophysiology.

Neural silences can be localized rapidly using noninvasive scalp EEG.

A rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

A novel method for calibrating head models to account for variability in conductivity and its evaluation in a sphere model.

The accuracy in electroencephalography (EEG) and combined EEG and magnetoencephalography (MEG) source reconstructions as well as in optimized transcranial electric stimulation (TES) depends on the conductive properties assigned to the head model, and most importantly on individual skull conductivity. In this study, we present an automatic pipeline to calibrate head models with respect to skull conductivity based on the reconstruction of the P20/N20 response using somatosensory evoked potentials and fields. In order to validate in a well-controlled setup without interplay with numerical errors, we evaluate the accuracy of this algorithm in a 4-layer spherical head model using realistic noise levels as well as dipole sources at different eccentricities with strengths and orientations related to somatosensory experiments. Our results show that the reference skull conductivity can be reliably reconstructed for sources resembling the generator of the P20/N20 response. In case of erroneous assumptions on scalp conductivity, the resulting skull conductivity parameter counterbalances this effect, so that EEG source reconstructions using the fitted skull conductivity parameter result in lower errors than when using the standard value. We propose an automatized procedure to calibrate head models which only relies on non-invasive modalities that are available in a standard MEG laboratory, measures under in vivo conditions and in the low frequency range of interest. Calibrated head modeling can improve EEG and combined EEG/MEG source analysis as well as optimized TES.

Stress-sensing in the human greying hair follicle: Ataxia Telangiectasia Mutated (ATM) depletion in hair bulb melanocytes in canities-prone scalp.

Canities (or hair greying) is an age-linked loss of the natural pigment called melanin from hair. While the specific cause(s) underlying the loss of melanogenically-active melanocytes from the anagen hair bulbs of affected human scalp remains unclear, oxidative stress sensing appears to be a key factor involved. In this study, we examined the follicular melanin unit in variably pigmented follicles from the aging human scalp of healthy individuals (22-70 years). Over 20 markers were selected within the following categories: melanocyte-specific, apoptosis, cell cycle, DNA repair/damage, senescence and oxidative stress. As expected, a reduction in melanocyte-specific markers in proportion to the extent of canities was observed. A major finding of our study was the intense and highly specific nuclear expression of Ataxia Telangiectasia Mutated (ATM) protein within melanocytes in anagen hair follicle bulbs. ATM is a serine/threonine protein kinase that is recruited and activated by DNA double-strand breaks and functions as an important sensor of reactive oxygen species (ROS) in human cells. The incidence and expression level of ATM correlated with pigmentary status in canities-affected hair follicles. Moreover, increased staining of the redox-associated markers 8-OHdG, GADD45 and GP-1 were also detected within isolated bulbar melanocytes, although this change was not clearly associated with donor age or canities extent. Surprisingly, we were unable to detect any specific change in the expression of other markers of oxidative stress, senescence or DNA damage/repair in the canities-affected melanocytes compared to surrounding bulbar keratinocytes. By contrast, several markers showed distinct expression of markers for oxidative stress and apoptosis/differentiation in the inner root sheath (IRS) as well as other parts of the hair follicle. Using our in vitro model of primary human scalp hair follicle melanocytes, we showed that ATM expression increased after incubation with the pro-oxidant hydrogen peroxide (H2O2). In addition, this ATM increase was prevented by pre-incubation of cells with antioxidants. The relationship between ATM and redox stress sensing was further evidenced as we observed that the inhibition of ATM expression by chemical inhibition promoted the loss of melanocyte viability induced by oxidative stress. Taken together these new findings illustrate the key role of ATM in the protection of human hair follicle melanocytes from oxidative stress/damage within the human scalp hair bulb. In conclusion, these results highlight the remarkable complexity and role of redox sensing in the status of human hair follicle growth, differentiation and pigmentation.

Inter-Subject Variability of Skull Conductivity and Thickness in Calibrated Realistic Head Models.

Skull conductivity has a substantial influence on EEG and combined EEG and MEG source analysis as well as on optimized transcranial electric stimulation. To overcome the use of standard literature values, we propose a non-invasive two-level calibration procedure to estimate skull conductivity individually in a group study with twenty healthy adults. Our procedure requires only an additional run of combined somatosensory evoked potential and field data, which can be easily integrated in EEG/MEG experiments. The calibration procedure uses the P20/N20 topographies and subject-specific realistic head models from MRI. We investigate the inter-subject variability of skull conductivity and relate it to skull thickness, age and gender of the subjects, to the individual scalp P20/N20 surface distance between the P20 potential peak and the N20 potential trough as well as to the individual source depth of the P20/N20 source. We found a considerable inter-subject variability for (calibrated) skull conductivity (8.44 ± 4.84 mS/m) and skull thickness (5.97 ± 1.19 mm) with a statistically significant correlation between them (rho = 0.52). Age showed a statistically significant negative correlation with skull conductivity (rho = -0.5). Furthermore, P20/N20 surface distance and source depth showed large inter-subject variability of 12.08 ± 3.21 cm and 15.45 ± 4.54 mm, respectively, but there was no significant correlation between them. We also found no significant differences among gender subgroups for the investigated measures. It is thus important to take the inter-subject variability of skull conductivity and thickness into account by means of using subject-specific calibrated realistic head modeling.

Localization of deep brain activity with scalp and subdural EEG.

To what extent electrocorticography (ECoG) and electroencephalography (scalp EEG) differ in their capability to locate sources of deep brain activity is far from evident. Compared to EEG, the spatial resolution and signal-to-noise ratio of ECoG is superior but its spatial coverage is more restricted, as is arguably the volume of tissue activity effectively measured from. Moreover, scalp EEG studies are providing evidence of locating activity from deep sources such as the hippocampus using high-density setups during quiet wakefulness. To address this question, we recorded a multimodal dataset from 4 patients with refractory epilepsy during quiet wakefulness. This data comprises simultaneous scalp, subdural and depth EEG electrode recordings. The latter was located in the hippocampus or insula and provided us with our "ground truth" for source localization of deep activity. We applied independent component analysis (ICA) for the purpose of separating the independent sources in theta, alpha and beta frequency band activity. In all patients subdural- and scalp EEG components were observed which had a significant zero-lag correlation with one or more contacts of the depth electrodes. Subsequent dipole modeling of the correlating components revealed dipole locations that were significantly closer to the depth electrodes compared to the dipole location of non-correlating components. These findings support the idea that components found in both recording modalities originate from neural activity in close proximity to the depth electrodes. Sources localized with subdural electrodes were ~70% closer to the depth electrode than sources localized with EEG with an absolute improvement of around ~2cm. In our opinion, this is not a considerable improvement in source localization accuracy given that, for clinical purposes, ECoG electrodes were implanted in close proximity to the depth electrodes. Furthermore, the ECoG grid attenuates the scalp EEG, due to the electrically isolating silastic sheets in which the ECoG electrodes are embedded. Our results on dipole modeling show that the deep source localization accuracy of scalp EEG is comparable to that of ECoG. SIGNIFICANCE STATEMENT: Deep and subcortical regions play an important role in brain function. However, as joint recordings at multiple spatial scales to study brain function in humans are still scarce, it is still unresolved to what extent ECoG and EEG differ in their capability to locate sources of deep brain activity. To the best of our knowledge, this is the first study presenting a dataset of simultaneously recorded EEG, ECoG and depth electrodes in the hippocampus or insula, with a focus on non-epileptiform activity (quiet wakefulness). Furthermore, we are the first study to provide experimental findings on the comparison of source localization of deep cortical structures between invasive and non-invasive brain activity measured from the cortical surface.

Scalp electroencephalograms over ipsilateral sensorimotor cortex reflect contraction patterns of unilateral finger muscles.

A variety of neural substrates are implicated in the initiation, coordination, and stabilization of voluntary movements underpinned by adaptive contraction and relaxation of agonist and antagonist muscles. To achieve such flexible and purposeful control of the human body, brain systems exhibit extensive modulation during the transition from resting state to motor execution and to maintain proper joint impedance. However, the neural structures contributing to such sensorimotor control under unconstrained and naturalistic conditions are not fully characterized. To elucidate which brain regions are implicated in generating and coordinating voluntary movements, we employed a physiologically inspired, two-stage method to decode relaxation and three patterns of contraction in unilateral finger muscles (i.e., extension, flexion, and co-contraction) from high-density scalp electroencephalograms (EEG). The decoder consisted of two parts employed in series. The first discriminated between relaxation and contraction. If the EEG data were discriminated as contraction, the second stage then discriminated among the three contraction patterns. Despite the difficulty in dissociating detailed contraction patterns of muscles within a limb from scalp EEG signals, the decoder performance was higher than chance-level by 2-fold in the four-class classification. Moreover, weighted features in the trained decoders revealed EEG features differentially contributing to decoding performance. During the first stage, consistent with previous reports, weighted features were localized around sensorimotor cortex (SM1) contralateral to the activated fingers, while those during the second stage were localized around ipsilateral SM1. The loci of these weighted features suggested that the coordination of unilateral finger muscles induced different signaling patterns in ipsilateral SM1 contributing to motor control. Weighted EEG features enabled a deeper understanding of human sensorimotor processing as well as of a more naturalistic control of brain-computer interfaces.

Wireless User-Generic Ear EEG.

In the past few years it has been demonstrated that electroencephalography (EEG) can be recorded from inside the ear (in-ear EEG). To open the door to low-profile earpieces as wearable brain-computer interfaces (BCIs), this work presents a practical in-ear EEG device based on multiple dry electrodes, a user-generic design, and a lightweight wireless interface for streaming data and device programming. The earpiece is designed for improved ear canal contact across a wide population of users and is fabricated in a low-cost and scalable manufacturing process based on standard techniques such as vacuum forming, plasma-treatment, and spray coating. A 2.5 × 2.5 cm2 wireless recording module is designed to record and stream data wirelessly to a host computer. Performance was evaluated on three human subjects over three months and compared with clinical-grade wet scalp EEG recordings. Recordings of spontaneous and evoked physiological signals, eye-blinks, alpha rhythm, and the auditory steady-state response (ASSR), are presented. This is the first wireless in-ear EEG to our knowledge to incorporate a dry multielectrode, user-generic design. The user-generic ear EEG recorded a mean alpha modulation of 2.17, outperforming the state-of-the-art in dry electrode in-ear EEG systems.

Brain-scale cortico-cortical functional connectivity in the delta-theta band is a robust signature of conscious states: an intracranial and scalp EEG study.

Long-range cortico-cortical functional connectivity has long been theorized to be necessary for conscious states. In the present work, we estimate long-range cortical connectivity in a series of intracranial and scalp EEG recordings experiments. In the two first experiments intracranial-EEG (iEEG) was recorded during four distinct states within the same individuals: conscious wakefulness (CW), rapid-eye-movement sleep (REM), stable periods of slow-wave sleep (SWS) and deep propofol anaesthesia (PA). We estimated functional connectivity using the following two methods: weighted Symbolic-Mutual-Information (wSMI) and phase-locked value (PLV). Our results showed that long-range functional connectivity in the delta-theta frequency band specifically discriminated CW and REM from SWS and PA. In the third experiment, we generalized this original finding on a large cohort of brain-injured patients. FC in the delta-theta band was significantly higher in patients being in a minimally conscious state (MCS) than in those being in a vegetative state (or unresponsive wakefulness syndrome). Taken together the present results suggest that FC of cortical activity in this slow frequency band is a new and robust signature of conscious states.

Neurofeedback of scalp bi-hemispheric EEG sensorimotor rhythm guides hemispheric activation of sensorimotor cortex in the targeted hemisphere.

Oscillatory electroencephalographic (EEG) activity is associated with the excitability of cortical regions. Visual feedback of EEG-oscillations may promote sensorimotor cortical activation, but its spatial specificity is not truly guaranteed due to signal interaction among interhemispheric brain regions. Guiding spatially specific activation is important for facilitating neural rehabilitation processes. Here, we tested whether users could explicitly guide sensorimotor cortical activity to the contralateral or ipsilateral hemisphere using a spatially bivariate EEG-based neurofeedback that monitors bi-hemispheric sensorimotor cortical activities for healthy participants. Two different motor imageries (shoulder and hand MIs) were selected to see how differences in intrinsic corticomuscular projection patterns might influence activity lateralization. We showed sensorimotor cortical activities during shoulder, but not hand MI, can be brought under ipsilateral control with guided EEG-based neurofeedback. These results are compatible with neuroanatomy; shoulder muscles are innervated bihemispherically, whereas hand muscles are mostly innervated contralaterally. We demonstrate the neuroanatomically-inspired approach enables us to investigate potent neural remodeling functions that underlie EEG-based neurofeedback via a BCI.