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.

BOARD-FTD-PACC: a graphical user interface for the synaptic and cross-frequency analysis derived from neural signals.

In order to understand the link between brain functional states and behavioral/cognitive processes, the information carried in neural oscillations can be retrieved using different analytic techniques. Processing these different bio-signals is a complex, time-consuming, and often non-automatized process that requires customization, due to the type of signal acquired, acquisition method implemented, and the objectives of each individual research group. To this end, a new graphical user interface (GUI), named BOARD-FTD-PACC, was developed and designed to facilitate the visualization, quantification, and analysis of neurophysiological recordings. BOARD-FTD-PACC provides different and customizable tools that facilitate the task of analyzing post-synaptic activity and complex neural oscillatory data, mainly cross-frequency analysis. It is a flexible and user-friendly software that can be used by a wide range of users to extract valuable information from neurophysiological signals such as phase-amplitude coupling and relative power spectral density, among others. BOARD-FTD-PACC allows researchers to select, in the same open-source GUI, different approaches and techniques that will help promote a better understanding of synaptic and oscillatory activity in specific brain structures with or without stimulation.

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.

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.

Music therapy and music medicine interventions with adult burn patients: A systematic review and meta-analysis.

BACKGROUND: Pain is one of the most common and most difficult symptoms to manage in adult burn patients in the Intensive Care Unit (ICU). Insufficient or unsuccessful pain management can negatively affect physiological, psychological, and social health in burn patients, both during and after hospitalization. Music therapy and music medicine interventions have been shown to positively affect pain and mental health in this population. This systematic review and meta-analysis provide an update of Randomized Controlled Trials (RCTs) using music therapy or music medicine interventions in adult burn patients. METHODS: A variety of databases were searched from their beginning to June 2020, including PsycINFO and PsycArticles (via APAsycNET), PubMed and MEDLINE (via OvidSP), Scopus, Web of Science, and the Cochrane Library. Data of all articles meeting the inclusion criteria were extracted, organized, and processed according to the PRISMA guidelines. Statistical analysis was performed using Q-test and I2 statistics. RESULTS: 10 RCTs with a total of 1061 participants were included. The results of the meta-analysis showed a statistically significant reduction of pain (I2 = 96.03%, P < 0.001), anxiety (I2 = 98.85%, P < 0.002), and improved relaxation (I2 = 87.19%, P < 0.001) favoring music interventions compared to control groups. CONCLUSIONS: This review provides preliminary evidence for the effectiveness of music interventions for adult burn patients. However, more high-quality RCTs are needed to safely establish guidelines for music therapists and other health care professionals in using music for health purposes with this population.

The Effect of Music Therapy on Perceived Pain, Mental Health, Vital Signs, and Medication Usage of Burn Patients Hospitalized in the Intensive Care Unit: A Randomized Controlled Feasibility Study Protocol.

Background: Burn patients experience major physiological and psychological stressors during treatment and rehabilitation, including elevated levels of pain, anxiety, stress, or depression. Music interventions inclusive of music therapy (MT) have been shown to improve such symptoms, but rigorous clinical trials investigating specific music therapy methods in adult burn patients are scarce. Methods: This is a single center Randomized Controlled Trial (RCT) protocol with two parallel arms. Participants are 81 adult burn patients admitted to the Intensive Care Unit (ICU) of the University Hospital Fundación Santa Fe de Bogotá in Colombia. The intervention consists of a Music Assisted Relaxation (MAR) protocol, a music therapy technique composed of entrained live music combined with a guided relaxation and/or the use of imagery. The effects of the MAR will be compared to a control group (treatment as usual) over a period of maximum 2 weeks or six interventions. The primary outcome measure is perceived background pain, as measured with a Visual Analog Scale (VAS) before and after each intervention. Secondary outcomes are anxiety and depression levels; vital signs; and the use of pain medication. Additionally, some patients in the intervention group will be invited to participate in electroencephalography, electromyography, and electrocardiography recordings during the MAR. Discussion: This study protocol follows the SPIRIT guidelines for defining items of clinical trials and is the first study in Colombia to evaluate the effects of music therapy for adult burn patients. With this RCT it is hoped to gather new knowledge about the potential of music therapy to help critical care patients cope and recover from their injuries during the hospitalization in the ICU. Trial registration: www.clinicaltrials.gov, Identifier: NCT04571255. Protocol version: V1.0, May 24th 2021.

Acute Effects of Two Different Species of Amyloid-ß on Oscillatory Activity and Synaptic Plasticity in the Commissural CA3-CA1 Circuit of the Hippocampus.

Recent evidence indicates that soluble amyloid-ß (Aß) species induce imbalances in excitatory and inhibitory transmission, resulting in neural network functional impairment and cognitive deficits during early stages of Alzheimer's disease (AD). To evaluate the in vivo effects of two soluble Aß species (Aß 25-35 and Aß 1-40) on commissural CA3-to-CA1 (cCA3-to-CA1) synaptic transmission and plasticity, and CA1 oscillatory activity, we used acute intrahippocampal microinjections in adult anaesthetized male Wistar rats. Soluble Aß microinjection increased cCA3-to-CA1 synaptic variability without significant changes in synaptic efficiency. High-frequency CA3 stimulation was rendered inefficient by soluble Aß intrahippocampal injection to induce long-term potentiation and to enhance synaptic variability in CA1, contrasting with what was observed in vehicle-injected subjects. Although soluble Aß microinjection significantly increased the relative power of γ-band and ripple oscillations and significantly shifted the average vector of θ-to-γ phase-amplitude coupling (PAC) in CA1, it prevented θ-to-γ PAC shift induced by high-frequency CA3 stimulation, opposite to what was observed in vehicle-injected animals. These results provide further evidence that soluble Aß species induce synaptic dysfunction causing abnormal synaptic variability, impaired long-term plasticity, and deviant oscillatory activity, leading to network activity derailment in the hippocampus.

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.

The unique social sense of puerperium: Increased empathy and Schadenfreude in parents of newborns.

Pregnancy and puerperium are typified by marked biobehavioral changes. These changes, which are traceable in both mothers and fathers, play an important role in parenthood and may modulate social cognition abilities. However, the latter effects remain notably unexplored in parents of newborns (PNs). To bridge this gap, we assessed empathy and social emotions (envy and Schadenfreude) in 55 PNs and 60 controls (childless healthy participants without a romantic relationship or sexual intercourse in the previous 48 hours). We used facial electromyography to detect physiological signatures of social emotion processing. Results revealed higher levels of affective empathy and Schadenfreude in PNs, the latter pattern being accompanied by increased activity of the corrugator suppercilii region. These effects were not explained by potential confounding variables (educational level, executive functioning, depression, stress levels, hours of sleep). Our novel findings suggest that PNs might show social cognition changes crucial for parental bonding and newborn care.

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.

Examining the optimal timing for closed-loop auditory stimulation of slow-wave sleep in young and older adults.

STUDY OBJECTIVES: Closed-loop auditory stimulation (CLAS) is a method for enhancing slow oscillations (SOs) through the presentation of auditory clicks during sleep. CLAS boosts SOs amplitude and sleep spindle power, but the optimal timing for click delivery remains unclear. Here, we determine the optimal time to present auditory clicks to maximize the enhancement of SO amplitude and spindle likelihood. METHODS: We examined the main factors predicting SO amplitude and sleep spindles in a dataset of 21 young and 17 older subjects. The participants received CLAS during slow-wave-sleep in two experimental conditions: sham and auditory stimulation. Post-stimulus SOs and spindles were evaluated according to the click phase on the SOs and compared between and within conditions. RESULTS: We revealed that auditory clicks applied anywhere on the positive portion of the SO increased SO amplitudes and spindle likelihood, although the interval of opportunity was shorter in the older group. For both groups, analyses showed that the optimal timing for click delivery is close to the SO peak phase. Click phase on the SO wave was the main factor determining the impact of auditory stimulation on spindle likelihood for young subjects, whereas for older participants, the temporal lag since the last spindle was a better predictor of spindle likelihood. CONCLUSIONS: Our data suggest that CLAS can more effectively boost SOs during specific phase windows, and these differ between young and older participants. It is possible that this is due to the fluctuation of sensory inputs modulated by the thalamocortical networks during the SO.

Altered Oscillatory Dynamics of CA1 Parvalbumin Basket Cells during Theta-Gamma Rhythmopathies of Temporal Lobe Epilepsy.

Recent reports in human demonstrate a role of theta-gamma coupling in memory for spatial episodes and a lack of coupling in people experiencing temporal lobe epilepsy, but the mechanisms are unknown. Using multisite silicon probe recordings of epileptic rats engaged in episodic-like object recognition tasks, we sought to evaluate the role of theta-gamma coupling in the absence of epileptiform activities. Our data reveal a specific association between theta-gamma (30-60 Hz) coupling at the proximal stratum radiatum of CA1 and spatial memory deficits. We targeted the microcircuit mechanisms with a novel approach to identify putative interneuronal types in tetrode recordings (parvalbumin basket cells in particular) and validated classification criteria in the epileptic context with neurochemical identification of intracellularly recorded cells. In epileptic rats, putative parvalbumin basket cells fired poorly modulated at the falling theta phase, consistent with weaker inputs from Schaffer collaterals and attenuated gamma oscillations, as evaluated by theta-phase decomposition of current-source density signals. We propose that theta-gamma interneuronal rhythmopathies of the temporal lobe are intimately related to episodic memory dysfunction in this condition.

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.

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.

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.

Automated detection of high-frequency oscillations in electrophysiological signals: Methodological advances.

In recent years, new recording technologies have advanced such that oscillations of neuronal networks can be identified from simultaneous, multisite recordings at high temporal and spatial resolutions. However, because of the deluge of multichannel data generated by these experiments, achieving the full potential of parallel neuronal recordings also depends on the development of new mathematical methods capable of extracting meaningful information related to time, frequency and space. In this review, we aim to bridge this gap by focusing on the new analysis tools developed for the automated detection of high-frequency oscillations (HFOs, >40Hz) in local field potentials. For this, we provide a revision of different aspects associated with physiological and pathological HFOs as well as the several stages involved in their automatic detection including preprocessing, selection, rejection and analysis through time-frequency processes. Beyond basic research, the automatic detection of HFOs would greatly assist diagnosis of epilepsy disorders based on the recognition of these typical pathological patterns in the electroencephalogram (EEG). Also, we emphasize how these HFO detection methods can be applied and the properties that might be inferred from neuronal signals, indicating potential future directions.

Inhibiting cholesterol degradation induces neuronal sclerosis and epileptic activity in mouse hippocampus.

Elevations in neuronal cholesterol have been associated with several degenerative diseases. An enhanced excitability and synchronous firing in surviving neurons are among the sequels of neuronal death in these diseases and also in some epileptic syndromes. Here, we attempted to increase neuronal cholesterol levels, using a short hairpin RNA to suppress expression of the enzyme cytochrome P450 family 46, subfamily A, polypeptide 1 gene (CYP46A1). This protein hydroxylates cholesterol and so facilitates transmembrane extrusion. A short hairpin RNA CYP46A1construction coupled to the adeno-associated virus type 5 was injected focally and unilaterally into mouse hippocampus. It was selectively expressed first in neurons of the cornu ammonis (hippocampus) (CA)3a region. Cytoplasmic and membrane cholesterol increased, and the neuronal soma volume increased and then decreased before pyramidal cells died. As CA3a pyramidal cells died, interictal electroencephalographic (EEG) events occurred during exploration and non-rapid eye movement sleep. With time, neuronal death spread to involve pyramidal cells and interneurons of the CA1 region. CA1 neuronal death was correlated with a delayed local expression of phosphorylated tau. Astrocytes were activated throughout the hippocampus and microglial activation was specific to regions of neuronal death. CA1 neuronal death was correlated with distinct aberrant EEG activity. During exploratory behaviour and rapid eye movement sleep, EEG oscillations at 7-10 Hz (theta) could accelerate to 14-21 Hz (beta) waves. They were accompanied by low-amplitude, high-frequency oscillations of peak power at ~300 Hz and a range of 250-350 Hz. Although episodes of EEG acceleration were not correlated with changes in exploratory behaviour, they were followed in some animals by structured seizure-like discharges. These data strengthen links between increased cholesterol, neuronal sclerosis and epileptic behaviour.

Spike avalanches in vivo suggest a driven, slightly subcritical brain state.

In self-organized critical (SOC) systems avalanche size distributions follow power-laws. Power-laws have also been observed for neural activity, and so it has been proposed that SOC underlies brain organization as well. Surprisingly, for spiking activity in vivo, evidence for SOC is still lacking. Therefore, we analyzed highly parallel spike recordings from awake rats and monkeys, anesthetized cats, and also local field potentials from humans. We compared these to spiking activity from two established critical models: the Bak-Tang-Wiesenfeld model, and a stochastic branching model. We found fundamental differences between the neural and the model activity. These differences could be overcome for both models through a combination of three modifications: (1) subsampling, (2) increasing the input to the model (this way eliminating the separation of time scales, which is fundamental to SOC and its avalanche definition), and (3) making the model slightly sub-critical. The match between the neural activity and the modified models held not only for the classical avalanche size distributions and estimated branching parameters, but also for two novel measures (mean avalanche size, and frequency of single spikes), and for the dependence of all these measures on the temporal bin size. Our results suggest that neural activity in vivo shows a mélange of avalanches, and not temporally separated ones, and that their global activity propagation can be approximated by the principle that one spike on average triggers a little less than one spike in the next step. This implies that neural activity does not reflect a SOC state but a slightly sub-critical regime without a separation of time scales. Potential advantages of this regime may be faster information processing, and a safety margin from super-criticality, which has been linked to epilepsy.

Epileptic seizure predictors based on computational intelligence techniques: a comparative study with 278 patients.

The ability of computational intelligence methods to predict epileptic seizures is evaluated in long-term EEG recordings of 278 patients suffering from pharmaco-resistant partial epilepsy, also known as refractory epilepsy. This extensive study in seizure prediction considers the 278 patients from the European Epilepsy Database, collected in three epilepsy centres: Hôpital Pitié-là-Salpêtrière, Paris, France; Universitätsklinikum Freiburg, Germany; Centro Hospitalar e Universitário de Coimbra, Portugal. For a considerable number of patients it was possible to find a patient specific predictor with an acceptable performance, as for example predictors that anticipate at least half of the seizures with a rate of false alarms of no more than 1 in 6 h (0.15 h⁻¹). We observed that the epileptic focus localization, data sampling frequency, testing duration, number of seizures in testing, type of machine learning, and preictal time influence significantly the prediction performance. The results allow to face optimistically the feasibility of a patient specific prospective alarming system, based on machine learning techniques by considering the combination of several univariate (single-channel) electroencephalogram features. We envisage that this work will serve as benchmark data that will be of valuable importance for future studies based on the European Epilepsy Database.