Evaluating the Electroencephalographic Signal Quality of an In-Ear Wearable Device.

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for advancing brain monitoring technologies into everyday applications. However, despite the current availability of several in-ear EEG devices in the market, there remains a critical need for robust validation against established clinical-grade systems. In this study, we carried out a detailed examination of the signal performance of a mobile in-ear EEG device from Naox Technologies. Our investigation had two main goals: firstly, evaluating the hardware circuit's reliability through simulated EEG signal experiments and, secondly, conducting a thorough comparison between the in-ear EEG device and gold-standard EEG monitoring equipment. This comparison assesses correlation coefficients with recognized physiological patterns during wakefulness and sleep, including alpha rhythms, eye artifacts, slow waves, spindles, and sleep stages. Our findings support the feasibility of using this in-ear EEG device for brain activity monitoring, particularly in scenarios requiring enhanced comfort and user-friendliness in various clinical and research settings.

Further characterisation of late somatosensory evoked potentials using electroencephalogram and magnetoencephalogram source imaging.

Beside the well-documented involvement of secondary somatosensory area, the cortical network underlying late somatosensory evoked potentials (P60/N60 and P100/N100) is still unknown. Electroencephalogram and magnetoencephalogram source imaging were performed to further investigate the origin of the brain cortical areas involved in late somatosensory evoked potentials, using sensory inputs of different strengths and by testing the correlation between cortical sources. Simultaneous high-density electroencephalograms and magnetoencephalograms were performed in 19 participants, and electrical stimulation was applied to the median nerve (wrist level) at intensity between 1.5 and 9 times the perceptual threshold. Source imaging was undertaken to map the stimulus-induced brain cortical activity according to each individual brain magnetic resonance imaging, during three windows of analysis covering early and late somatosensory evoked potentials. Results for P60/N60 and P100/N100 were compared with those for P20/N20 (early response). According to literature, maximal activity during P20/N20 was found in central sulcus contralateral to stimulation site. During P60/N60 and P100/N100, activity was observed in contralateral primary sensorimotor area, secondary somatosensory area (on both hemispheres) and premotor and multisensory associative cortices. Late responses exhibited similar characteristics but different from P20/N20, and no significant correlation was found between early and late generated activities. Specific clusters of cortical activities were activated with specific input/output relationships underlying early and late somatosensory evoked potentials. Cortical networks, partly common to and distinct from early somatosensory responses, contribute to late responses, all participating in the complex somatosensory brain processing.

Effects of midazolam on high-frequency oscillations in amygdala and hippocampus of epilepsy patients.

High-frequency oscillations (HFOs) are associated with normal brain function, but are also increasingly recognized as potential biomarkers of epileptogenic tissue. Considering the important role of interneuron activity in physiological HFO generation, we studied their modulation by midazolam (MDZ), an agonist of γ-aminobutyric acid type A (GABAA)-benzodiazepine receptors. Here, we analyzed 80 intracranial electrode contacts in amygdala and hippocampus of 13 patients with drug-refractory focal epilepsy who had received MDZ for seizure termination during presurgical monitoring. Ripples (80-250 Hz) and fast ripples (FRs; 250-400 Hz) were compared before and after seizures with MDZ application, and according to their origin either within or outside the individual seizure onset zone (SOZ). We found that MDZ distinctly suppressed all HFOs (ripples and FRs), whereas the reduction of ripples was significantly less pronounced inside the SOZ compared to non-SOZ contacts. The rate of FRs inside the SOZ was less affected, especially in hippocampal contacts. In a few cases, even a marked increase of FRs following MDZ administration was seen. Our results demonstrate, for the first time, a significant HFO modulation in amygdala and hippocampus by MDZ, thus giving insights into the malfunction of GABA-mediated inhibition within epileptogenic areas and its role in HFO generation.

Response of sleep slow oscillations to acoustic stimulation is evidenced by distinctive synchronization processes.

Closed-loop acoustic stimulation (CLAS) during sleep has shown to boost slow wave (SW) amplitude and spindle power. Moreover, sleep SW have been classified based on different processes of neuronal synchronization. Thus, different types of SW events may have distinct functional roles and be differentially affected by external stimuli. However, the SW synchronization processes affected by CLAS are not well understood. Here, we studied the effect of CLAS on the dissociation of SW events based on two features of neuronal synchronization in the electroencephalogram (topological spread and wave slope). We evaluated and classified individual SW events of 14 healthy subjects during a CLAS stimulated (STM) and a control night (CNT). Three main categories of SW events were found denoting (C1) steep slope SW with global spread, (C2) flat-slope waves with localized spread and homeostatic decline, and (C3) multipeaked flat-slope events with global spread. Comparing between conditions, we found a consistent increase of event proportion and trough amplitudes for C1 events during the time of stimulation. Furthermore, we found similar increases in post-stimulus spectral power in θ, ß, and σ frequencies for CNT vs STIM condition independently of sleep stage or SW categories. However, topological analysis showed differentiated spatial dynamics in N2 and N3 for SW categories and the co-occurrence with spindle events. Our findings support the existence of multiple types of SW with differential response to external stimuli and possible distinct neuronal mechanisms.

Interictal epileptiform discharges show distinct spatiotemporal and morphological patterns across wake and sleep.

Presurgical evaluation of mesial temporal and neocortical focal pharmacoresistant epilepsy patients using intracranial EEG recordings has led to the generation of extensive data on interictal epileptiform discharges, located within or remotely from seizure onset zones. In this study, we used this data to investigate how interictal epileptiform discharges are modulated and how their spatial distribution changes during wake and sleep and analysed the relationship between these discharge events and seizure onset zones. Preoperative evaluation data from 11 adult patients with focal pharmacoresistant epilepsy were extracted from the Epilepsiae database. Interictal epileptiform discharges were automatically detected during wakefulness and over several hours of continuous seizure-free sleep (total duration of EEG recordings:106.7 h; mean per patient: 9.7 h), and analysed across four brain areas (mesial temporal, lateral neocortical, basal cortical and the temporal pole). Sleep stages were classified manually from scalp EEG. Discharge events were characterized according to their rate and morphology (amplitude, sharpness and duration). Eight patients had a seizure onset zone over mesial areas and three patients over lateral neocortical areas. Overall, discharge rates varied across brain areas during wakefulness and sleep [wake/sleep stages × brain areas interaction; Wald χ 2(df = 6) = 31.1, P < 0.0001]. N2-N3 non-rapid eye movement sleep increased interictal epileptiform discharges in mesial areas compared with wakefulness and rapid eye movement sleep (P < 0.0001), and to other areas (P < 0.0001 for all comparisons). This mesial pattern was observed both within and outside of seizure onset zones. During wakefulness, the rate of interictal epileptiform discharges was significantly higher than during N2-N3 non-rapid eye movement sleep (P = 0.04), and rapid eye movement sleep (P = 0.01) in lateral neocortical areas (referred to as lateral neocortical pattern), a finding that was more pronounced in seizures onset zones (P = 0.004). The morphological characteristics of the discharge events were modulated during wakefulness and sleep stages across brain areas. The effect of seizure onset zones on discharge morphology was conditioned by brain area and was particularly marked in temporal pole areas. Our analysis of discharge patterns in relation to cerebral localization, vigilance state and the anatomical affiliation of seizure onset zones revealed the global and local aspects of the complex relationship between interictal discharges, sleep and seizure onset zones. This novel approach may lead to a better understanding of cognitive decline and responses to therapy, as well as to adaptation of surgical interventions for epileptic patients.

Face-induced gamma oscillations and event-related potentials in patients with epilepsy: an intracranial EEG study.

BACKGROUND: To examine the pathological effect of a mesial temporal seizure onset zone (SOZ) on local and inter-regional response to faces in the amygdala and other structures of the temporal lobe. METHODS: Intracranial EEG data was obtained from the amygdala, hippocampus, fusiform gyrus and parahippocampal gyrus of nine patients with drug-refractory epilepsy during visual stimulation with faces and mosaics. We analyzed event-related potentials (ERP), gamma frequency power, phase-amplitude coupling and phase-slope-index and compared the results between patients with versus without a mesial temporal SOZ. RESULTS: In the amygdala and fusiform gyrus, faces triggered higher ERP amplitudes compared to mosaics in both patient groups and higher gamma power in patients without a mesial temporal SOZ. In the hippocampus, famous faces triggered higher gamma power for both groups combined but did not affect ERPs in either group. The differentiated ERP response to famous faces in the parahippocampal gyrus was more pronounced in patients without a mesial temporal SOZ. Phase-amplitude coupling and phase-slope-index results yielded bidirectional modulation between amygdala and fusiform gyrus, and predominately unidirectional modulation between parahippocampal gyrus and hippocampus. CONCLUSIONS: A mesial temporal SOZ was associated with an impaired response to faces in the amygdala, fusiform gyrus and parahippocampal gyrus in our patients. Compared to this, the response to faces in the hippocampus was impaired in patients with, as well as without, a mesial temporal SOZ. Our results support existing evidence for face processing deficits in patients with a mesial temporal SOZ and suggest the pathological effect of a mesial temporal SOZ on the amygdala to play a pivotal role in this matter in particular.

Auditory closed-loop stimulation on sleep slow oscillations using in-ear EEG sensors.

Acoustic stimulation synchronized to slow oscillations in scalp electroencephalograms has been shown to enhance sleep features, which makes it promising in boosting cognitive functions as well as in the treatment of some sleep disturbances. Nevertheless, scalp electrode sensors are resource intensive and poorly tolerated by sleeping patients. The aim of this study was to investigate the potential usability of in-the-ear electroencephalography to implement auditory closed-loop stimulation during sleep. For this, we evaluated the agreement between slow oscillation recordings obtained through the in-ear sensor and those obtained simultaneously from standard scalp electrodes during naps of 13 healthy subjects. We found that in-ear activity provided enough information to automatically detect sleep slow oscillations in real-time. Based on this, we successfully enhanced scalp slow oscillations using auditory single-cycle closed-loop brain-state-dependent stimulation based on in-ear signals acquired in 11 further subjects. We conclude that in-ear sensors provide a feasible technology for the enhancement of sleep patterns, and could pave the way for new clinical applications in the near future.

Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG.

Clinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.

Automatic seizure detection based on imaged-EEG signals through fully convolutional networks.

Seizure detection is a routine process in epilepsy units requiring manual intervention of well-trained specialists. This process could be extensive, inefficient and time-consuming, especially for long term recordings. We proposed an automatic method to detect epileptic seizures using an imaged-EEG representation of brain signals. To accomplish this, we analyzed EEG signals from two different datasets: the CHB-MIT Scalp EEG database and the EPILEPSIAE project that includes scalp and intracranial recordings. We used fully convolutional neural networks to automatically detect seizures. For our best model, we reached average accuracy and specificity values of 99.3% and 99.6%, respectively, for the CHB-MIT dataset, and corresponding values of 98.0% and 98.3% for the EPILEPSIAE patients. For these patients, the inclusion of intracranial electrodes together with scalp ones increased the average accuracy and specificity values to 99.6% and 58.3%, respectively. Regarding the other metrics, our best model reached average precision of 62.7%, recall of 58.3%, F-measure of 59.0% and AP of 54.5% on the CHB-MIT recordings, and comparatively lowers performances for the EPILEPSIAE dataset. For both databases, the number of false alarms per hour reached values less than 0.5/h for 92% of the CHB-MIT patients and less than 1.0/h for 80% of the EPILEPSIAE patients. Compared to recent studies, our lightweight approach does not need any estimation of pre-selected features and demonstrates high performances with promising possibilities for the introduction of such automatic methods in the clinical practice.

Entrainment and synchronization of brain oscillations to auditory stimulations.

Oscillations of neural excitability shape sensory, motor or cognitive processes. Furthermore, a large body of research demonstrates that intrinsic oscillations are entrained by external rhythms, allowing a simple and efficient way to enhance human brain functions. As an external stimulation source, repeating acoustic stimuli have been shown to provide a possible pacing signal for modulating the electrical activity recorded by the electroencephalogram (EEG). In this review, we discuss recent advances in understanding how rhythmic auditory stimulation can selectively modulate EEG oscillations. Despite growing evidence, recent evidence suggests that standard methods of data analysis are often insufficient for a definite proof of entrainment in some instances. In particular, we stressed that the complexity of the elicited modulations, often varying in phase and frequency on a short timescale, requires time-frequency measures that are better appropriate to analyze driven brain phenomena. Once entrainment is clearly established, one can assess the specificity of its expression, thus providing a better understanding of the physiology underlying brain modulation and a faster translation to treatment programs in various psychopathologic conditions.

Single-unit activities during the transition to seizures in deep mesial structures.

Focal seizures are assumed to arise from a hypersynchronous activity affecting a circumscribed brain region. Using microelectrodes in seizure-generating deep mesial regions of 9 patients, we investigated the firing of hundreds of single neurons before, during, and after ictal electroencephalogram (EEG) discharges. Neuronal spiking activity at seizure initiation was highly heterogeneous and not hypersynchronous. Furthermore, groups of neurons showed significant changes in activity minutes before the seizure with no concomitant changes in the corresponding macroscopic EEG recordings. Altogether, our findings suggest that only limited subsets of neurons in epileptic depth regions initiate the seizure-onset and that ictogenic mechanisms operate in submillimeter-scale microdomains. Ann Neurol 2017 Ann Neurol 2017;82:1022-1028.

How to record high-frequency oscillations in epilepsy: A practical guideline.

OBJECTIVE: Technology for localizing epileptogenic brain regions plays a central role in surgical planning. Recent improvements in acquisition and electrode technology have revealed that high-frequency oscillations (HFOs) within the 80-500 Hz frequency range provide the neurophysiologist with new information about the extent of the epileptogenic tissue in addition to ictal and interictal lower frequency events. Nevertheless, two decades after their discovery there remain questions about HFOs as biomarkers of epileptogenic brain and there use in clinical practice. METHODS: In this review, we provide practical, technical guidance for epileptologists and clinical researchers on recording, evaluation, and interpretation of ripples, fast ripples, and very high-frequency oscillations. RESULTS: We emphasize the importance of low noise recording to minimize artifacts. HFO analysis, either visual or with automatic detection methods, of high fidelity recordings can still be challenging because of various artifacts including muscle, movement, and filtering. Magnetoencephalography and intracranial electroencephalography (iEEG) recordings are subject to the same artifacts. SIGNIFICANCE: High-frequency oscillations are promising new biomarkers in epilepsy. This review provides interested researchers and clinicians with a review of current state of the art of recording and identification and potential challenges to clinical translation.

Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex.

The local field potential (LFP) is generated by large populations of neurons, but unitary contribution of spiking neurons to LFP is not well characterised. We investigated this contribution in multi-electrode array recordings from human and monkey neocortex by examining the spike-triggered LFP average (st-LFP). The resulting st-LFPs were dominated by broad spatio-temporal components due to ongoing activity, synaptic inputs and recurrent connectivity. To reduce the spatial reach of the st-LFP and observe the local field related to a single spike we applied a spatial filter, whose weights were adapted to the covariance of ongoing LFP. The filtered st-LFPs were limited to the perimeter of 800 µm around the neuron, and propagated at axonal speed, which is consistent with their unitary nature. In addition, we discriminated between putative inhibitory and excitatory neurons and found that the inhibitory st-LFP peaked at shorter latencies, consistently with previous findings in hippocampal slices. Thus, in human and monkey neocortex, the LFP reflects primarily inhibitory neuron activity.

Self-induced intracerebral gamma oscillations in the human cortex.

Gamma oscillations play a pivotal role in multiple cognitive functions. They enable coordinated activity and communication of local assemblies, while abnormalities in gamma oscillations exist in different neurological and psychiatric diseases. Thus, a specific rectification of gamma synchronization could potentially compensate the deficits in pathological conditions. Previous experiments have shown that animals can voluntarily modulate their gamma power through operant conditioning. Using a closed-loop experimental setup, we show in six intracerebrally recorded epileptic patients undergoing presurgical evaluation that intracerebral power spectrum can be increased in the gamma frequency range (30-80 Hz) at different fronto-temporal cortical sites in human subjects. Successful gamma training was accompanied by increased gamma power at other cortical locations and progressively enhanced cross-frequency coupling between gamma and slow oscillations (3-12 Hz). Finally, using microelectrode targets in two subjects, we report that upregulation of gamma activities is possible also in spatial micro-domains, without the spread to macroelectrodes. Overall, our findings indicate that intracerebral gamma modulation can be achieved rapidly, beyond the motor system and with high spatial specificity, when using micro targets. These results are especially significant because they pave the way for use of high-resolution therapeutic approaches for future clinical applications.

Voluntary control of intracortical oscillations for reconfiguration of network activity.

Voluntary control of oscillatory activity represents a key target in the self-regulation of brain function. Using a real-time closed-loop paradigm and simultaneous macro- and micro-electrode recordings, we studied the effects of self-induced intracortical oscillatory activity (4-8 Hz) in seven neurosurgical patients. Subjects learned to robustly and specifically induce oscillations in the target frequency, confirmed by increased oscillatory event density. We have found that the session-to-session variability in performance was explained by the functional long-range decoupling of the target area suggesting a training-induced network reorganization. Downstream effects on more local activities included progressive cross-frequency-coupling with gamma oscillations (30-120 Hz), and the dynamic modulation of neuronal firing rates and spike timing, indicating an improved temporal coordination of local circuits. These findings suggest that effects of voluntary control of intracortical oscillations can be exploited to specifically target plasticity processes to reconfigure network activity, with a particular relevance for memory function or skill acquisition.

High-frequency oscillations in human and monkey neocortex during the wake-sleep cycle.

Beta (ß)- and gamma (γ)-oscillations are present in different cortical areas and are thought to be inhibition-driven, but it is not known if these properties also apply to γ-oscillations in humans. Here, we analyze such oscillations in high-density microelectrode array recordings in human and monkey during the wake-sleep cycle. In these recordings, units were classified as excitatory and inhibitory cells. We find that γ-oscillations in human and ß-oscillations in monkey are characterized by a strong implication of inhibitory neurons, both in terms of their firing rate and their phasic firing with the oscillation cycle. The ß- and γ-waves systematically propagate across the array, with similar velocities, during both wake and sleep. However, only in slow-wave sleep (SWS) ß- and γ-oscillations are associated with highly coherent and functional interactions across several millimeters of the neocortex. This interaction is specifically pronounced between inhibitory cells. These results suggest that inhibitory cells are dominantly involved in the genesis of ß- and γ-oscillations, as well as in the organization of their large-scale coherence in the awake and sleeping brain. The highest oscillation coherence found during SWS suggests that fast oscillations implement a highly coherent reactivation of wake patterns that may support memory consolidation during SWS.

RIPPLELAB: A Comprehensive Application for the Detection, Analysis and Classification of High Frequency Oscillations in Electroencephalographic Signals.

High Frequency Oscillations (HFOs) in the brain have been associated with different physiological and pathological processes. In epilepsy, HFOs might reflect a mechanism of epileptic phenomena, serving as a biomarker of epileptogenesis and epileptogenicity. Despite the valuable information provided by HFOs, their correct identification is a challenging task. A comprehensive application, RIPPLELAB, was developed to facilitate the analysis of HFOs. RIPPLELAB provides a wide range of tools for HFOs manual and automatic detection and visual validation; all of them are accessible from an intuitive graphical user interface. Four methods for automated detection-as well as several options for visualization and validation of detected events-were implemented and integrated in the application. Analysis of multiple files and channels is possible, and new options can be added by users. All features and capabilities implemented in RIPPLELAB for automatic detection were tested through the analysis of simulated signals and intracranial EEG recordings from epileptic patients (n = 16; 3,471 analyzed hours). Visual validation was also tested, and detected events were classified into different categories. Unlike other available software packages for EEG analysis, RIPPLELAB uniquely provides the appropriate graphical and algorithmic environment for HFOs detection (visual and automatic) and validation, in such a way that the power of elaborated detection methods are available to a wide range of users (experts and non-experts) through the use of this application. We believe that this open-source tool will facilitate and promote the collaboration between clinical and research centers working on the HFOs field. The tool is available under public license and is accessible through a dedicated web site.

Dynamic Balance of Excitation and Inhibition in Human and Monkey Neocortex.

Balance of excitation and inhibition is a fundamental feature of in vivo network activity and is important for its computations. However, its presence in the neocortex of higher mammals is not well established. We investigated the dynamics of excitation and inhibition using dense multielectrode recordings in humans and monkeys. We found that in all states of the wake-sleep cycle, excitatory and inhibitory ensembles are well balanced, and co-fluctuate with slight instantaneous deviations from perfect balance, mostly in slow-wave sleep. Remarkably, these correlated fluctuations are seen for many different temporal scales. The similarity of these computational features with a network model of self-generated balanced states suggests that such balanced activity is essentially generated by recurrent activity in the local network and is not due to external inputs. Finally, we find that this balance breaks down during seizures, where the temporal correlation of excitatory and inhibitory populations is disrupted. These results show that balanced activity is a feature of normal brain activity, and break down of the balance could be an important factor to define pathological states.