OBJECTIVE:The circadian rhythm synchronizes physiological and behavioral patterns with the 24-h light-dark cycle. Disruption to the circadian rhythm is linked to various health conditions, although optimal methods to describe these disruptions remain unclear. An emerging approach is to examine the intraindividual variability in measurable properties of the circadian rhythm over extended periods. Epileptic seizures are modulated by circadian rhythms, but the relevance of circadian rhythm disruption in epilepsy remains unexplored. Our study investigates intraindividual circadian variability in epilepsy and its relationship with seizures. METHODS:We retrospectively analyzed >70 000 h of wearable smartwatch data (Fitbit) from 143 people with epilepsy (PWE) and 31 healthy controls. Circadian oscillations in heart rate time series were extracted, daily estimates of circadian period, acrophase, and amplitude properties were produced, and estimates of the intraindividual variability of these properties over an entire recording were calculated. RESULTS:PWE exhibited greater intraindividual variability in period (76 vs. 57 min, d = .66, p < .001) and acrophase (64 vs. 48 min, d = .49, p = .004) compared to controls, but not in amplitude (2 beats per minute, d = -.15, p = .49). Variability in circadian properties showed no correlation with seizure frequency nor any differences between weeks with and without seizures. SIGNIFICANCE:For the first time, we show that heart rate circadian rhythms are more variable in PWE, detectable via consumer wearable devices. However, no association with seizure frequency or occurrence was found, suggesting that this variability might be underpinned by the epilepsy etiology rather than being a seizure-driven effect.

OBJECTIVE:Epileptic seizures occurring in cyclical patterns is increasingly recognized as a significant opportunity to advance epilepsy management. Current methods for detecting seizure cycles rely on intrusive techniques or specialized biomarkers, thereby limiting their accessibility. This study evaluates a non-invasive seizure cycle detection method using seizure diaries and compares its accuracy with cycles identified from intracranial electroencephalography (iEEG) seizures and interictal epileptiform discharges (IEDs). METHODS:Using data from a previously published first in-human iEEG device trial (n = 10), we analyzed seizure cycles identified through diary reports, iEEG seizures, and IEDs. Cycle similarities across diary reports, iEEG seizures, and IEDs were evaluated at periods of 1 to 45 days using spectral coherence, accuracy, precision, recall, and the false-positive rate. RESULTS:A spectral coherence analysis of the raw signals showed moderately similar periodic components between diary seizures/day and iEEG seizures/day (median = .43, IQR = .68). In contrast, there was low coherence between diary seizures/day and IEDs/day (median = .11, IQR = .18) and iEEG seizures/day and IEDs/day (median = .12, IQR = .19). Accuracy, precision, recall scores, and false-positive rates of iEEG seizure cycles from diary seizure cycles were significantly higher than chance across all participants (accuracy (mean ± standard deviation): .95 ± .02; precision: .56 ± .19; recall: .56 ± .19; false-positive rate: .02 ± .01). However, accuracy, precision, and recall scores of IED cycles from both diary and iEEG cycles did not perform above chance, on average. Recall scores were compared across good diary reporters, under-reporters, and over-reporters, with recall scores generally performing better in good reporters and under-reporters compared to over-reporters. SIGNIFICANCE:These findings suggest that iEEG seizure cycles can be identified with diary reports, even in individuals who under- and over-report seizures. This approach offers an accessible alternative for monitoring seizure cycles compared to more invasive methods.

ObjectiveOver recent years, there has been a growing interest in exploring the utility of seizure risk forecasting, particularly how it could improve quality of life for people living with epilepsy. This study reports on user experiences and perspectives of a seizure risk forecaster app, as well as the potential impact on mood and adjustment to epilepsy.MethodsActive app users were asked to complete a survey (baseline and 3-month follow-up) to assess perspectives on the forecast feature as well as mood and adjustment. Post-hoc, nine neutral forecast users (neither agreed nor disagreed it was useful) completed semi-structured interviews, to gain further insight into their perspectives of epilepsy management and seizure forecasting. Non-parametric statistical tests and inductive thematic analyses were used to analyse the quantitative and qualitative data, respectively.ResultsSurveys were completed by 111 users. Responders consisted of “app users” (n = 58), and “app and forecast users” (n = 53). Of the “app and forecast users”, 40 % believed the forecast was accurate enough to be useful in monitoring for seizure risk, and 60 % adopted it for purposes like scheduling activities and helping mental state. Feeling more in control was the most common response to both high and low risk forecasted states. In-depth interviews revealed five broad themes, of which ‘frustrations with lack of direction’ (regarding their current epilepsy management approach), ‘benefits of increased self-knowledge’ and ‘current and anticipated usefulness of forecasting’ were the most common.SignificancePreliminary results suggest that seizure risk forecasting can be a useful tool for people with epilepsy to make lifestyle changes, such as scheduling daily events, and experience greater feelings of control. These improvements may be attributed, at least partly, to the improvements in self-knowledge experienced through forecast use.

Objective: The periodicity of seizures, ranging from circadian to circannual cycles, is increasingly recognized as a significant opportunity to advance epilepsy management. Current methods for detecting seizure cycles rely on intrusive techniques or specialised biomarkers, limiting their accessibility. Approach: This study evaluates a non-invasive seizure cycle detection method using seizure diaries and compares its accuracy with cycles identified from intracranial electroencephalography (iEEG) seizures and interictal epileptiform discharges (IEDs). Using data from a previously published first in-human iEEG device trial (n=10), we analysed seizure cycles identified through diary reports, iEEG seizures and IEDs. Cycle similarities across diary reports, iEEG seizures and iEDs were evaluated at periods of 1 to 45 days using spectral coherence, accuracy, precision and recall scores. Main results: Spectral coherence of the raw signals averaged over frequencies and participants indicated moderately similar frequency components between diary seizures/day and iEEG seizures/day (Mean=0.62, SD=0.61,95% CI [0.59, 0.95]). In contrast, there was low coherence between diary seizures/day and IEDs/day (Mean=0.17, SD=0.17, 95% CI [0.18, 0.18]) and iEEG seizures/day and IEDs/day (Mean=0.18, SD=0.18, 95% CI [0.17, 0.19]). Mean accuracy, precision and recall of iEEG seizure cycles from diary seizure cycles was significantly higher than chance across all participants (Accuracy: Mean=0.95, SD=0.02; Precision: Mean=0.56, SD=0.19; Recall: Mean=0.56, SD=0.19). Accuracy, precision and recall scores between seizures cycles using diary or iEEG compared to IED cycles did not perform above chance, on average. Recall scores were compared across good diary reporters, under-reporters and over-reporters, with recall scores generally performing better in good reporters and under-reporters compared to over-reporters. Significance: These findings suggest that iEEG seizure cycles can be accurately identified with diary reports, even in both under- and over-reporters. This approach offers a practical, accessible alternative for monitoring seizure cycles compared to more invasive methods. ### Competing Interest Statement M.J.C. is an employee and has financial interests in Epi-Minder a company that is developing a sub-scalp EEG device and Seer Medical a company that undertakes ambulatory EEG monitoring and launched an epilepsy health management mobile application. E.N. is an employee and has financial interest in Seer Medical. The remaining authors have no conflicts of interests. ### Funding Statement A.R. receives funding from the Australian Government Research Training Program Scholarship from the University of Melbourne. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Human Research Ethics Committees of Austin Health, the Royal Melbourne Hospital and St. Vincent's Hospital gave ethical approval for the original study and subsequent analysis, which includes this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Deidentified NeuroVista data are available upon reasonable request to co-author Prof. Mark J. Cook (markcook@unimelb.edu.au).

A significant challenge of video-electroencephalography (vEEG) in epilepsy diagnosis is timing monitoring sessions to capture epileptiform activity. Given the significant consequences of misdiagnosis or delayed diagnosis, new techniques to improve diagnostic yield of vEEG are needed. In this study, we introduce and validate “pro-ictal EEG scheduling”, a method to schedule vEEG monitoring to coincide with periods of heightened seizure probability as a low-risk approach to enhance the diagnostic yield. A database of long-term ambulatory vEEG monitoring sessions ( n =5038) of adults and children was examined. Data from linked electronic seizure diaries were extracted (minimum 10 self-reported events over 12-months) to generate cycle-based estimates of seizure risk. VEEG monitoring sessions coinciding with periods of estimated high-risk were allocated to the high-risk group (adults n =305, children n =82) and compared to remaining studies (baseline: adults n =3586, children n =1065). Test of Proportions and Risk-Ratios (RR) were used to index differences in proportions and likelihood of capturing outcome measures (abnormal report, confirmed seizure and diary event) during monitoring. The impact of clinical and demographic factors (sex, epilepsy-type, medication) was also explored. During vEEG monitoring, the high-risk group was 25% more likely to have an abnormal vEEG report (190/305:62.3% vs 1790/3586:49.9%, RR=1.25, 95% CI[1.137:1.370], p <0.001), 63% more likely to present with a confirmed seizure (56/305:18.4% vs 424/3586:11.3%, RR=1.63, 95% CI[1.265:2.101], p <0.001) and 42% more likely to report an event (153/305:50.2% vs 1267/3586:35.3%, RR=1.420, 95% CI[1.259:1.602], p <0.001). In children, the high-risk group was 93% more likely to have a confirmed seizure (21/82:25.6% vs 141/1065:13.2%, RR=1.93, 95% CI[1.297:2.885], p= 0.002). Similar effects were observed across clinical and demographic features. This study provides the first large-scale validation of pro-ictal EEG scheduling in improving the yield of vEEG. This innovative approach offers a pragmatic and low-risk strategy to enhance the diagnostic capabilities of vEEG monitoring, significantly impacting epilepsy management.

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Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Forty-four subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n=37921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n=29), sleep features were analysed for significant changes prior to seizure days. A total of 3894 reported seizures (M = 88, SD = 130) and 17078 recorded sleep nights (M = 388, SD = 351) were included in the study. Participants with epilepsy slept an average of 2 hours longer than the average sleep duration within the general population. Just 1 of 29 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 11 of 29 subjects showed significant differences between either their sleep onset (bed) or offset (wake) times prior to seizure occurrence. Conversely to existing studies, the current study found oversleeping was associated with a 20% increased seizure risk in the following 48h (p < 0.01), possibly due to nocturnal seizures occurring overnight. Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48h. We also observed that oversleeping only significantly contributed to seizure risk when the participant was in a high-risk state, according to a cycles-based forecasting algorithm. Overall, the presented results demonstrated that day-to-day changes in sleep-duration had a minimal effect on reported seizures, which bed- and wake-times were more important for identifying seizure risk the following day. Oversleeping was also linked to seizure occurrence, most likely due to nocturnal seizures driving oversleep. Wearables can be utilised to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.

The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing.

BACKGROUND:Seizure risk forecasting could reduce injuries and even deaths in people with epilepsy. There is great interest in using non-invasive wearable devices to generate forecasts of seizure risk. Forecasts based on cycles of epileptic activity, seizure times or heart rate have provided promising forecasting results. This study validates a forecasting method using multimodal cycles recorded from wearable devices. METHOD:Seizure and heart rate cycles were extracted from 13 participants. The mean period of heart rate data from a smartwatch was 562 days, with a mean of 125 self-reported seizures from a smartphone app. The relationship between seizure onset time and phases of seizure and heart rate cycles was investigated. An additive regression model was used to project heart rate cycles. The results of forecasts using seizure cycles, heart rate cycles, and a combination of both were compared. Forecasting performance was evaluated in 6 of 13 participants in a prospective setting, using long-term data collected after algorithms were developed. FINDINGS:The results showed that the best forecasts achieved a mean area under the receiver-operating characteristic curve (AUC) of 0.73 for 9/13 participants showing performance above chance during retrospective validation. Subject-specific forecasts evaluated with prospective data showed a mean AUC of 0.77 with 4/6 participants showing performance above chance. INTERPRETATION:The results of this study demonstrate that cycles detected from multimodal data can be combined within a single, scalable seizure risk forecasting algorithm to provide robust performance. The presented forecasting method enabled seizure risk to be estimated for an arbitrary future period and could be generalised across a range of data types. In contrast to earlier work, the current study evaluated forecasts prospectively, in subjects blinded to their seizure risk outputs, representing a critical step towards clinical applications. FUNDING:This study was funded by an Australian Government National Health & Medical Research Council and BioMedTech Horizons grant. The study also received support from the Epilepsy Foundation of America's 'My Seizure Gauge' grant.

Multiday cyclic patterns underlying the timing of seizures are well-established in adults with epilepsy and are critical to the development of seizure risk forecasting models. As cycles underpinning these models are yet to be explored in paediatric cohorts, the current study applies methods drawn from seizure risk forecasting to identify and compare multiday seizure cycles between paediatric and adult cohorts. This is followed by the first validation of personalised forecasts of seizure likelihood in a paediatric cohort. Multiday seizure cycles were extracted retrospectively from 325 (71 paediatric) electronic seizure diary users (more than 28 days of app use) with confirmed epilepsy. Cycles were grouped (k-means clustering), and seizure cycles quantified (synchronisation index), with significant cycles identified by Rayleigh test of periodicity ( p <0.05). Wilcoxon rank-sum test assessed differences in prevalence and strength of cycle groups between paediatric and adult cohorts. 34,402 seizures (paediatric: M =101, SD =103, adult: M =107, SD =156) were analysed and seizure cycles were grouped according to circadian (0.5-1.5 days), about-weekly (2-12 days), about-fortnightly (13-22 days) and about-monthly (23-32 days) periodicities. Significant cycles were identified in each cycle group, with no differences in prevalence or cycle strength between paediatric and adult cohorts for any multiday cycle group. Similar effects were observed across clinical and demographic features (sex, epilepsy-type, medication). These multiday patterns formed the basis for cycle-based estimates of seizure likelihood. Receiver operating characteristic (area under the curve: AUC) was applied and demonstrated that these seizure forecasts performed better than chance (i.e. shuffled seizure times). Multiday seizure cycles are therefore similar in paediatric and adult cohorts, and this study provides the first validation of cycle-based seizure risk forecasting models as a promising approach for paediatric epilepsy.### Competing Interest StatementH.K., P.J.K., R.E.S., E.S.N., D.E., D.F. and M.J.C. have employment or a financial interest in Seer Medical Pty. Ltd. The remaining authors have no conflicts of interest.### Funding StatementThis research was supported by NHMRC Investigator Grant (1178220), My Seizure Gauge Grant (Epilepsy Foundation of America) and the BioMedTech Horizons 3 program (initiative of MTPConnect). Funding partners were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Ethics committee of St Vincent's Hospital Melbourne gave ethical approval for this work.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors.

Objective Seizure unpredictability is a major source of disability for people with epilepsy. Recent work using chronic brain recordings has established that for many individuals with epilepsy seizure risk is not random, but corresponds to circadian and multiday (multidien) cycles in brain excitability. Here, we aimed to evaluate whether multimodal wearable device recordings can characterize cycles of seizure risk, and compare wearables performance with concurrent chronic brain recordings.Methods Fourteen subjects underwent long-term ambulatory monitoring with a multimodal wrist worn device (measuring heart rate, heart rate variability, accelerometry, tonic and phasic electrodermal activity, temperature) and an implanted responsive neurostimulation system (measuring interictal epileptiform abnormalities (IEA) and electrographic seizures). Wavelet time-frequency analyses identified circadian and multiday cycles in wearable and brain recordings. Circular statistics assessed seizure phase locking to cycles in physiology.Results Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electrographic seizure detections (mean 76 seizures). Seizure phase locking to multiday cycles occurred in six (IEA), five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Seizure phase locking to residual HR multiday cycles (HR after regression of correlated physical activity (ACC)) increased to six subjects.Interpretation Long timescale cyclical changes in wearable recordings are common in epilepsy, and seizures occur at preferred phases of these cycles for many individuals. Broadly accessible wearable technology can provide new insights into the chronobiology of epilepsy with implications for seizure forecasting.### Competing Interest StatementG.A.W., and B.H.B. declare intellectual property licensed to Cadence Neuroscience. N.M.G. and G.A.W. are investigators for the Medtronic Deep Brain Stimulation Therapy for Epilepsy Post-Approval Study. E.S.N., P.J.K, M.J.C. and D.R.F. declare a financial interest in Seer Medical.### Funding StatementThis research was supported by the Epilepsy Foundation Epilepsy Innovation Institute My Seizure Gauge; NIH grants UH3-NS95495 & R01-NS09288203; American Epilepsy Society Research & Training Fellowship for Clinicians (N.M.G.); National Science Foundation grant CBET-2138378 (M.N.).### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The Institutional Review Board of Mayo Clinic gave ethical approval for this work.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesWearable data will be made available in 2023 on EpilepsyEcosystem.org. The analysis approach is described in detail above; MATLAB scripts are available from the author by reasonable request.* ACC : accelerometry ASD : amplitude spectral density BVP : blood volume pulse. EDAp : phasic electrodermal activity EDAt : tonic electrodermal activity HR : heart rate HRV : heart rate variability IBI : interbeat interval IEA : interictal epileptiform activity iEEG : intracranial EEG RNS : responsive neurostimulation TEMP : temperature

BACKGROUND:Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology, and immunology. In people with epilepsy, epileptic brain activity and seizure occurrence have been found to follow circadian, weekly, and monthly rhythms. Understanding the relationship between these cycles of brain excitability and other physiological systems can provide new insight into the causes of multiday cycles. The brain-heart link has previously been considered in epilepsy research, with potential implications for seizure forecasting, therapy, and mortality (i.e., sudden unexpected death in epilepsy).METHODS:We report the results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study sought to examine multiday cycles of heart rate and seizures in adults with diagnosed uncontrolled epilepsy (N=31) and healthy adult controls (N=15) using wearable smartwatches and mobile seizure diaries over at least four months (M=12.0, SD=5.9; control M=10.6, SD=6.4). Cycles in heart rate were detected using a continuous wavelet transform. Relationships between heart rate cycles and seizure occurrence were measured from the distributions of seizure likelihood with respect to underlying cycle phase.FINDINGS:Heart rate cycles were found in all 46 participants (people with epilepsy and healthy controls), with circadian (N=46), about-weekly (N=25) and about-monthly (N=13) rhythms being the most prevalent. Of the participants with epilepsy, 19 people had at least 20 reported seizures, and 10 of these had seizures significantly phase locked to their multiday heart rate cycles.INTERPRETATION:Heart rate cycles showed similarities to multiday epileptic rhythms and may be comodulated with seizure likelihood. The relationship between heart rate and seizures is relevant for epilepsy therapy, including seizure forecasting, and may also have implications for cardiovascular disease. More broadly, understanding the link between multiday cycles in the heart and brain can shed new light on endogenous physiological rhythms in humans.FUNDING:This research received funding from the Australian Government National Health and Medical Research Council (investigator grant 1178220), the Australian Government BioMedTech Horizons program, and the Epilepsy Foundation of America's 'My Seizure Gauge' grant.

Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder®), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilizing cycles in EA and previous seizure times. The procedures and devices were well-tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as visually confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88), which is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.