BACKGROUND:Esophagectomy with gastric conduit reconstruction is associated with long-term morbidity; however, the mechanisms underlying postoperative gastric dysfunction remain incompletely understood. This study evaluated gastric myoelectrical activity in the reconstructed intrathoracic stomach using noninvasive body surface gastric mapping (BSGM) and examined its relationship with symptoms and delayed gastric conduit emptying (DGCE). METHODS:A total of 30 adults at a median of 50.4 months postesophagectomy were recruited and compared with 30 matched controls. All participants underwent BSGM. Patient-reported outcomes were assessed using the Patient Assessment of Upper Gastrointestinal Disorders-Symptom Severity Index (PAGI-SYM), Patient Assessment of Upper Gastrointestinal Disorders-Quality of Life (PAGI-QoL), total symptom burden score, and Konradsson's DGCE score. Electrophysiological metrics were compared with those of matched controls. Pearson correlations and analysis of variance with correlation ratio were used to assess associations among electrophysiology, symptoms, and DGCE. RESULTS:Postesophagectomy patients demonstrated significantly impaired gastric electrophysiology, including reduced principal gastric frequency (2.65 ± 0.37 vs controls, 3.11 ± 0.24 cycles per minute, P <.001) and body mass index (BMI)-adjusted amplitude (27.1 ± 11.1 vs 40.5 ± 17.3 µV, P <.001). Notably, 23 (76.7%) patients exhibited abnormal BSGM phenotypes, most commonly low-frequency activity (n = 13). DGCE was present in 23.3% of patients and was associated with a higher symptom burden and reduced quality of life (PAGI-SYM, r = 0.37, P <.05; PAGI-QOL, r = -0.41, P <.05). Principal gastric frequency was found to correlate with nausea (r = 0.49, P <.05), and BMI-adjusted amplitude correlated with symptoms of excessive fullness (r = 0.49, P <.05) and upper gut pain (r = 0.48, P <.05). CONCLUSION:The gastric conduit exhibits persistent electrophysiological abnormalities years after esophagectomy, characterized by low-frequency slow-wave activity and reduced amplitude. Although these abnormalities showed some association with symptom burden, they did not correlate with DGCE, indicating that this condition is multifactorial. BSGM provides insight into postesophagectomy gastric function and could inform therapeutic strategies.

Objective. Electroencephalography (EEG) data is derived by sampling continuous neurological time series signals. In order to prepare EEG signals for machine learning, the signal must be divided into manageable segments. The current naive approach uses arbitrary fixed time slices, which may have limited biological relevance because brain states are not confined to fixed intervals. We investigate whether adaptive segmentation methods are beneficial for machine learning EEG analysis. Approach. We introduce a novel adaptive segmentation method, CTXSEG, that creates variable-length segments based on statistical differences in the EEG data and propose ways to use them with modern machine learning approaches that typically require fixed-length input. We assess CTXSEG using controllable synthetic data generated by our novel signal generator CTXGEN. We validate on a real-world use case by replacing fixed-length segmentation in the preprocessing step of a typical EEG machine learning pipeline for seizure detection, and offer guidance on how application may be extended to other problem domains. Main results. We found that using CTXSEG to prepare EEG data improves seizure detection performance compared to fixed-length approaches when evaluated using a standardized framework, without modifying the machine learning method, and requires fewer segments. Significance. This work demonstrates that adaptive segmentation with CTXSEG can be readily applied to modern machine learning approaches, with potential to improve performance. It is a promising alternative to fixed-length segmentation for signal preprocessing and should be considered as part of the standard preprocessing repertoire in EEG machine learning applications.

An Oculomotor Brain-Computer Interface (BCI) records neural activity from regions of the brain involved in planning eye movements and translates this activity into control commands. While previous successful oculomotor BCI studies primarily relied on invasive microelectrode implants in non-human primates, this study investigates the feasibility of an oculomotor BCI using a minimally invasive endovascular Stentrode device implanted near the supplementary motor area in a patient with amyotrophic lateral sclerosis (ALS). To achieve this, self-paced visually-guided and free-viewing saccade tasks were designed, in which the participant performed saccades in four directions (left, right, up, down), with simultaneous recording of endovascular EEG and eye gaze. The visually guided saccades were cued with visual stimuli, whereas the free-viewing saccades were self-directed without explicit cues. The results showed that while the neural responses of visually guided saccades overlapped with the cue-evoked potentials, the free-viewing saccades exhibited distinct saccade-related potentials that began shortly before eye movement, peaked approximately 50 ms after saccade onset, and persisted for around 200 ms. In the frequency domain, these responses appeared as a low-frequency synchronisation below 15 Hz. Classification of 'fixation vs. saccade' was robust, achieving mean area under the receiver operating characteristic curve (AUC) scores of 0.88 within sessions and 0.86 between sessions. In contrast, classifying saccade direction proved more challenging, yielding within-session AUC scores of 0.67 for four-class decoding and up to 0.75 for the best-performing binary comparisons (left vs. up and left vs. down). This proof-of-concept study demonstrates the feasibility of an endovascular oculomotor BCI in an ALS patient, establishing a foundation for future oculomotor BCI studies in human subjects.

Individuals with severe physical disabilities often experience diminished quality of life stemming from limited ability to engage with their surroundings. Brain-Computer Interface (BCI) technology aims to bridge this gap by enabling direct technology interaction. However, current BCI systems require invasive procedures, such as craniotomy or implantation of electrodes through blood vessels, posing significant risks to patients. Sub-scalp electroencephalography (EEG) offers a lower risk alternative. This study investigates the signal quality of sub-scalp EEG recordings from various depths in a sheep model, and compares results with other methods: ECoG and endovascular arrays. A computational model was also constructed to investigate the factors underlying variations in electrode performance. We demonstrate that peg electrodes placed within the sub-scalp space can achieve visual evoked potential signal-to-noise ratios (SNRs) approaching that of ECoG. Endovascular arrays exhibited SNR comparable to electrodes positioned on the periosteum. Furthermore, sub-scalp recordings captured high gamma neural activity, with maximum bandwidth ranging from 120 Hz to 180 Hz depending on electrode depth. These findings support the use of sub-scalp EEG for BCI applications, and provide valuable insights for future sub-scalp electrode design. This data lays the groundwork for human trials, ultimately paving the way for chronic, in-home BCIs that empower individuals with physical disabilities.

Objective. Common spatial patterns (CSPs) has been established as a powerful feature extraction method in EEG signal processing with machine learning, but it has shortcomings including sensitivity to noise and rigidity in the value of the weights. Our goal was to transform CSP into a trainable machine learning model that can learn from data, be regularized, and be integrated into end-to-end classification networks.Approach. We developed a novel parameterised and orthogonally-constrained neural network layer for learning CSPs (POC-CSP) that maintains CSP's mathematical properties while allowing trainable weights. The layer uses parameterisation based on Lie Group theory to convert constrained optimisation into unconstrained optimisation, enabling integration with standard neural network (NN) training methods. We evaluated the approach on two public motor imagery datasets, focusing on both subject-specific and multi-subject paradigms.Main results. POC-CSP outperformed both conventional CSP and existing NN implementations in subject-specific classification tasks. In a novel multi-subject paradigm, POC-CSP achieved superior generalisation. When fine-tuned with just 50% of a new subject's data, POC-CSP achieved 0.95 average accuracy across subjects, substantially outperforming subject-specific models trained with more data.Significance. These findings demonstrate that combining CSP's proven effectiveness with NNs' flexibility can significantly improve EEG signal processing performance. The ability to generalize across subjects and achieve high accuracy with minimal subject-specific training data makes POC-CSP particularly valuable for practical brain-computer interface applications, where collecting large amounts of training data from each new user is often impractical or unfeasible.

Epilepsy is characterized by recurrent, unpredictable seizures that impose significant challenges in dailymanagement and treatment. One emerging area of interest is the identification of seizure cycles, includingmultiday patterns, which may offer insights into seizure prediction and treatment optimization. This studyinvestigated multiday seizure cycles in a Tetanus Toxin (TT) rat model of epilepsy. Six TT-injected rats wereobserved over a 40-day period, with continuous EEG monitoring to record seizure events. Wavelet transformanalysis revealed significant multiday cycles in seizure occurrences, with periods ranging from 4 to 7 daysacross different rats. Synchronization Index (SI) analysis demonstrated variable phase locking, with somerats showing strong synchronization of seizures with specific phases of the cycle. Importantly, the studyrevealed that these seizure cycles are dynamic and evolve over time, with some rats exhibiting shifts in cycleperiods during the recording period. This suggests that the underlying neural mechanisms driving these cyclesmay change as the epileptic state progresses. The identification of stable and evolving multiday rhythms inseizure activity, independent of external factors, highlights a potential intrinsic biological basis for seizuretiming. These findings offer promising avenues for improving seizure forecasting and designing personalized,timing-based therapeutic interventions in epilepsy. Future research should explore the underlying neuralmechanisms and clinical applications of multiday seizure cycles.

Evaluating effectiveness of anti-seizure medications in epilepsy often relies on seizure frequency, reported through seizure diaries before and after treatment initiation. Measuring efficacy with seizure frequency can be challenging and unreliable as seizures tend to occur in cyclical patterns - seizure cycles - making it difficult to distinguish drug effects from natural fluctuations. Incorporating cycle information could aid treatment evaluation, but antiseizure medications (ASMs) may alter seizure cycles, warranting further study. We conducted an observational study using seizure and ASM tracking app data (Feb. 2023) from 86 individuals with epilepsy. Participants were grouped based on ASM regimen and ≥50% seizure rate reduction at 4 months after a drug change (drug-switching-responders, n = 7/45; drug-switching-non-responders, n = 38/45) or random timepoint (drug-sustained-responders, n = 8/41; drug-sustained-non-responders, n = 33/41). We compared groups on three seizure cycle variables detected via diaries: 1. how strongly seizures synchronise with a cycle, measured by the Synchronisation Index (SI), 2. cycle period, and 3. number of detected cycles. Permutation tests (alpha = 0.05, p < 0.004 with Bonferroni correction) assessed significance, and regression models examined correlations with seizure rate. Across an average 612-day study period, 22,976 seizures were reported. Following an ASM change, the SI of the seizure cycle was more likely to change (p < 0.004). This was pronounced in drug-switching-responders (median absolute SI difference: 0.37 [IQR = 0.26] vs. 0.11 [IQR = 0.11] in the drug-sustained-responders, p<0.004, permutation test). Changes in cycle length and number of detected cycles were similar across groups, possibly due to a non-linear relationship between seizure rate and cycles, suggested by weak linear correlations and poorly fitting models. These findings suggest ASMs may influence how strongly seizures synchronise with diary-detected seizure cycles. However, this relationship is complex and not yet well understood, complicating clinical interpretation. Ongoing research into real-time seizure cycle tracking may support the use of seizure cycles in aiding treatment monitoring. ### Competing Interest Statement Seer Medical was not involved in the study design. M.J.C. is an employee and has financial interests in Epi-Minder a company that is developing a sub-scalp EEG device. 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 Committee of St. Vincents Hospital, Melbourne approved the use of deidentified mobile application data for this research (LRR 165.19). 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 All data produced in the present study are available upon reasonable request to the authors

Recent work has shown the ability to record neural behaviour in pre-clinical studies from an endovascular location for over a year. Previous work on stimulating neural tissue from an endovascular location has also shown motor-evoked responses in sheep. However, endovascular stimulation requires high currents and can result in electrode degradation. This study aimed to modify an endovascular electrode to increase its charge injection capacity for efficacious neural stimulation. The platinum endovascular electrode was modified with platinum black and characterised by electrochemical and microscopic techniques. The stability of the electrode coating was assessed after a 7-day continuous stimulation paradigm. Modelling of the neural activating function was performed for central and peripheral neural anatomy with both electrode materials. Platinum black coatings had a substantially larger electroactive area than uncoated platinum. This resulted in increased electrode admittance, charge storage capacity and charge injection capacity while reducing the total impedance at 10 Hz and polarisation voltage. The coated electrode was comparatively more electrochemically stable than uncoated platinum following the 7-day continuous stimulation protocol. Modelling of the neural activating function indicated a substantial increase in the electrode-neuron distance which could be safely stimulated using platinum black coated electrodes. By comparison of electrochemical response with neural modelling, we have demonstrated the feasibility of safe stimulation of neural tissue using an endovascular neural interface, opening the possibility of a new, minimally invasive neural stimulation paradigm.

Objective.Sensory prostheses use arrays of electrodes to stimulate neural tissue and restore a sense of vision or hearing. At perceptible levels of stimulation, the current from each electrode spreads and causes overlapping regions of neural activation. This lack of specificity results in perceptual deficits. Methods to overcome this reduced specificity, such as a closed loop stimulation approach require measurement of the neural response to stimulation. This investigation tests the possibility of using the large stimulating electrodes such as those required by some subretinal or suprachoroidal retinal implants to measure the neural response to stimulation, an approach similar to Evoked Compound Action Potentials measurements used in cochlear implants.Approach. Ex vivotissue samples from Long Evans rats with healthy retinas and Royal College of Surgeon rats with retinal degeneration were used to investigate both stimulating and recording from electrodes of the same array. A hexagonal array was used with 20 platinum electrodes with 500μm diameter and 700μm pitch. Post-stimulus voltage decay was reduced with appropriate tuning of a triphasic stimulation pulse and in post-analysis with a high-pass filter. A method using alternating polarities of biphasic pulses was also trialed. A cocktail of synaptic and ion channel blockers was used to block all neural response including action potentials and thus confirm the biological origin of the signal.Main Results.It was found that a neural signal was observable on electrode that were sufficiently distant from the stimulating electrodes. The signal appeared to be due to direct activation of ganglion cells or possibly mediated via inner retinal neurons.Significance.This result confirms that recording usable neural signals from large electrodes is possible, which is an essential step in implementing a closed loop stimulation strategy for a subretinal or suprachoroidal retinal prosthesis.

This study introduces a neurobiologically inspired computational model based on the predictive coding algorithm, providing insights into coherent motion detection processes. The model is designed to reflect key principles observed in the visual system, particularly MT neurons and their surround suppression mechanisms, which play a critical role in detecting global motion. By integrating these principles, the model simulates how motion structures are decomposed into individual and shared sources, mirroring the brain's strategy for extracting coherent motion patterns. The results obtained from random dot stimuli underscore the delicate balance between sensory data and prior knowledge in motion detection. Model testing across varying noise levels reveals that, as noise increases, the model takes longer to stabilize its motion estimates, consistent with psychophysical experiments showing that response duration (e.g., reaction time or decision-making time) also increases under higher noise conditions. The model suggests that an excessive emphasis on prior knowledge prolongs the stabilization time for motion detection, whereas an optimal integration of prior expectations enhances detection accuracy and efficiency by preventing excessive disturbances due to noise. These findings contribute to potential explanations for motion detection deficiencies observed in schizophrenia.

Objective.Seizure detection algorithms enable clinicians to accurately assess seizure burden for epilepsy diagnosis and long-term management. State-of-the-art algorithms rely on electroencephalography (EEG) data to identify electrographic seizures. Previous research that used non-EEG signals, such as electrocardiography (ECG) and wristband data, were collected in epilepsy monitoring units. We aimed to investigate the feasibility of ECG seizure detection in ambulatory settings.Approach.We developed a patient-independent, machine learning-based seizure detector using ambulatory long-term ECG monitoring data. The model was trained on long-term studies of 47 patients and evaluated pseudoprospectively using event detection on a hold-out test set of 18 patients.Main results.In the hold-out test set, the seizure detector performed better than chance for 14 out of 18 patients. The average sensitivity was 72% and the average specificity was 68% for the whole test cohort. Overall, across training and test sets, the performance was better for patients diagnosed with focal epilepsy and for patients who were identified as responders (had substantial heart rate changes during seizures).Significance.Key contributions of this study include the development of a patient-independent seizure detector using ambulatory data and the introduction of a pseudoprospective evaluation framework, which can benefit chronic ambulatory seizure monitoring.

Objective.There is limited work investigating brain-computer interface (BCI) technology in people with multiple sclerosis (pwMS), a neurodegenerative disorder of the central nervous system. Present work is limited to recordings at the scalp, which may be significantly altered by changes within the cortex due to volume conduction. The recordings obtained from the sensors, therefore, combine disease-related alterations and task-relevant neural signals, as well as signals from other regions of the brain that are not relevant. The current study aims to unmix signals affected by multiple sclerosis (MS) progression and BCI task-relevant signals using estimated source activity to improve classification accuracy.Approach.Data was collected from eight participants with a range of MS severity and ten neurotypical participants. This dataset was used to report the classification accuracy of imagined movements of the hands and feet at the sensor-level and the source-level in the current study.K-means clustering of equivalent current dipoles was conducted to unmix temporally independent signals. The location of these dipoles was compared between MS and control groups and used for classification of imagined movement. Linear discriminant analysis classification was performed at each time-frequency point to highlight differences in frequency band delay.Main Results.Source-level signal acquisition significantly improved decoding accuracy of imagined movement vs rest and movement vs movement classification in pwMS and controls. There was no significant difference found in alpha (7-13 Hz) and beta (13-30 Hz) band classification delay between the neurotypical control and MS group, including imagery of limbs with weakness or paralysis.Significance.This study is the first to demonstrate the advantages of source-level analysis for BCI applications in pwMS. The results highlight the potential for enhanced clinical outcomes and emphasize the need for longitudinal studies to assess the impact of MS progression on BCI performance, which is crucial for effective clinical translation of BCI technology.

Objective.Epilepsy affects millions globally, with a significant subset of patients suffering from drug-resistant focal seizures. Understanding the underlying neurodynamics of seizure initiation and propagation is crucial for advancing treatment and diagnostics. In this study, we present a novel, inference-based approach for analyzing the temporal evolution of cortical stability and chaos during focal epileptic seizures.Approach.Utilizing a multi-region neural mass model, we estimate time-varying synaptic connectivity from intracranial electroencephalography (iEEG) data collected from individuals with drug-resistant focal epilepsy.Main results.Our analysis reveals distinct preictal and ictal phases characterized by shifts in cortical stability, heightened chaos in the ictal phase, and highlight the critical role of inter-regional communication in driving chaotic cortical behaviour. We demonstrate that cortical dynamics are consistently destabilized prior to seizure onset, with a transient reduction in instability at seizure onset, followed by a significant increase throughout the seizure.Significance.This work provides new insights into the mechanisms of seizure generation and offers potential biomarkers for predicting seizure events. Our findings pave the way for innovative therapeutic strategies targeting cortical stability and chaos to manage epilepsy.

The development of a seizure relies on two factors. One is the existence of an overexcitable neuronal network and the other is a trigger that switches normal activity of that network into a paroxysmal state. While mechanisms of local overexcitation have been the focus of many studies, the process of triggering remains poorly understood. We suggest that, apart from the known exteroceptive sources of reflex epilepsy such as visual, auditory or olfactory signals, there is a range of interoceptive triggers, which are relevant for seizure development in Temporal Lobe Epilepsy (TLE). The hypothesis proposed here aims to explain the prevalence of epileptic activity in sleep and in drowsiness states and to provide a detailed mechanism of seizures triggered by interoceptive signals.

Understanding seizure duration is crucial for predicting seizure evolution and developing targeted therapeutic strategies. This study investigates how changes in neural connectivity patterns estimated in a Neural Mass Model (NMM) relate to seizure duration and whether these parameters can be used to forecast seizure duration. We applied a biologically plausible NMM to intracranial EEG recordings from a Tetanus Toxin rat model of epilepsy. Model parameters, representing synaptic connectivity strengths between excitatory and inhibitory neuronal populations across different cortical layers, were estimated using the Unscented Kalman Filter. A Random Forest classifier was trained to predict seizure duration (short vs. long) based on these connectivity parameters. We assessed the classification performance using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Our findings reveal that stronger excitatory-to-excitatory connectivity before seizure onset was associated with longer seizure durations. In contrast, inhibitory-to-inhibitory and excitatory-to-inhibitory connectivity strengths decreased before long seizures. The Random Forest classifier achieved an AUC of 0.91 when using both preictal (1 minute before seizure onset) and early ictal (first 5 seconds of the seizures) parameters, demonstrating excellent predictive power. Classification using only preictal parameters resulted in an AUC of 0.70. Our study highlights the critical role of preictal neural connectivity in determining seizure duration. The findings suggest that network excitability and inhibitory control before seizure onset influence how long seizures persist. These insights provide a foundation for personalized, time-based therapeutic interventions and seizure management strategies, ultimately improving the quality of life of patients with epilepsy. ### Competing Interest Statement The authors have declared no competing interest. Australian Research Council’s Training Centre in Cognitive Computing for Medical Technologies, ,

Multiple Sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A Brain-Computer Interface (BCI) aims to restore quality of life through control of an external device, such as a wheelchair. However, the limited BCI research in people with MS is insufficient. The current study aims to expand on the current MS-BCI literature by highlighting the feasibility of decoding MS imagined movement. We collected electroencephalography (EEG) data from eight participants with various symptoms of MS and ten neurotypical control participants. Participants made imagined movements of the hands and feet as directed by a go no-go protocol. Binary regularised linear discriminant analysis was used to classify imagined movement at individual time-frequency points. The frequency bands which provided the maximal accuracy, and the associated latency, were compared. In all MS participants, the classification algorithm achieved above 70% accuracy in at least one imagined movement vs. rest classification and most movement vs. movement classifications. There was no significant difference between classification of limbs with weakness or paralysis to neurotypical controls. Both the MS and control groups possessed decodable information within the alpha (7-13 Hz) and beta (16-30 Hz) bands at similar latency. This study is the first to demonstrate the feasibility of decoding imagined movements in people with MS. As an alternative to the P300 response, motor imagery-based control of a BCI may also be combined with existing motor imagery therapy to supplement MS rehabilitation. These promising results merit further long term BCI studies to investigate the effect of MS progression on classification performance.

Current brain-computer interfaces (BCI) face limitations in signal acquisition. While sub-scalp EEG offers a potential solution, existing devices prioritize chronic seizure monitoring and lack features suited for BCI applications. This work addresses this gap by outlining key specifications for sub-scalp BCI devices, focusing on channel count, sampling rate, power efficiency, and form factor. We present the Set-And-Forget EEG (SAFE) system, a custom-built amplifier and wireless transmitter meeting these criteria. This compact (12x12 mm), six-channel device offers 1024 Hz sampling and Bluetooth Low Energy data transmission. Validation using generated sinusoids and electrocorticography recordings of visual evoked potentials in sheep models demonstrated low noise recording. Future animal studies will assess sub-scalp EEG signal quality for BCI applications. This data lays the groundwork for human trials, ultimately paving the way for chronic, in-home BCIs that empower individuals with physical disabilities.

BACKGROUND:Multiple Sclerosis (MS) can be a severely disabling condition that leads to various neurological symptoms. A Brain-Computer Interface (BCI) may substitute some lost function; however, there is a lack of BCI research in people with MS. Present BCI designs have also overlooked the unique pathological changes associated with MS and have not considered needs of users within their home environments. To progress this research area effectively and efficiently, we aimed to evaluate user needs and assess the feasibility and user-centric requirements of a BCI for people with MS. We hypothesised that (i) people with MS would be interested in adopting BCI technology and (ii) those with reduced independence would prefer a higher-performing invasive BCI. METHODS:We conducted an online survey of people with MS to describe user preferences and establish the initial steps of user-centred design. The survey aimed to understand their interest in BCI applications, bionic applications, device preferences, and development considerations and related these to symptoms and assistance needs. RESULTS:We demonstrated widespread interest for BCI applications in all stages of MS, with a preference for a non-invasive (n = 12) or minimally invasive (n = 15) BCI over carer assistance (n = 6). Descriptive analysis indicated that level of independence did not influence preference towards the higher performing but highly invasive BCI. CONCLUSIONS:The needs of end users reported in this study are crucial for efficient development of BCI systems that can be effectively translated into the home environment. Considering the potential to enhance independence and quality of life for people living with MS, the results emphasise the importance of user-centred design for future advancement of BCIs that account for the unique pathological changes associated with MS.

Seizure duration, a characteristic of epilepsy that is understudied in relation to its relationship with rhythmic cycles, provides critical insights into the severity and temporal dynamics of seizures. This study investigates the rhythmic patterns of seizure duration in the tetanus toxin rat model of epilepsy, which is a well-established platform enabling long-term, stable recordings and observation of seizure emergence and remission. Our analysis shows significant cyclical patterns in seizure durations, with periods ranging from 4 to 8 days across rats. The synchronization index and circular-linear correlations revealed phase-locked relationships between seizure durations and cycles, suggesting non-random, predictable temporal dynamics. Further analyses examined the relationship between seizure durations, inter-seizure intervals and dominant EEG power. The relationship between inter-seizure intervals and seizure duration was modest, suggesting little to no temporal dependency. In contrast, seizure duration showed stronger associations with EEG power in dominant frequency bands. The findings highlight that seizure durations exhibit predictable rhythms, which could transform seizure prediction and enable time-based intervention strategies, ultimately improving epilepsy management and patient outcomes. These insights lay the groundwork for personalized, rhythm-aware therapeutic approaches.