Summary Objective Patients with absence epilepsy sensitivity <10% of their absences. The clinical gold standard to assess absence epilepsy is a 24‐h electroencephalographic (EEG) recording, which is expensive, obtrusive, and time‐consuming to review. We aimed to (1) investigate the performance of an unobtrusive, two‐channel behind‐the‐ear EEG‐based wearable, the Sensor Dot (SD), to detect typical absences in adults and children; and (2) develop a sensitive patient‐specific absence seizure detection algorithm to reduce the review time of the recordings. Methods We recruited 12 patients (median age = 21 years, range = 8–50; seven female) who were admitted to the epilepsy monitoring units of University Hospitals Leuven for a 24‐h 25‐channel video‐EEG recording to assess their refractory typical absences. Four additional behind‐the‐ear electrodes were attached for concomitant recording with the SD. Typical absences were defined as 3‐Hz spike‐and‐wave discharges on EEG, lasting 3 s or longer. Seizures on SD were blindly annotated on the full recording and on the algorithm‐labeled file and consequently compared to 25‐channel EEG annotations. Patients or caregivers were asked to keep a seizure diary. Performance of the SD and seizure diary were measured using the F1 score. Results We concomitantly recorded 284 absences on video‐EEG and SD. Our absence detection algorithm had a sensitivity of .983 and false positives per hour rate of .9138. Blind reading of full SD data resulted in sensitivity of .81, precision of .89, and F1 score of .73, whereas review of the algorithm‐labeled files resulted in scores of .83, .89, and .87, respectively. Patient self‐reporting gave sensitivity of .08, precision of 1.00, and F1 score of .15. Significance Using the wearable SD, epileptologists were able to reliably detect typical absence seizures. Our automated absence detection algorithm reduced the review time of a 24‐h recording from 1‐2 h to around 5–10 min.

Background: The growing interest in neuroimaging technologies generates a massive amount of biomedical data of high dimensionality. Tensor-based analysis of brain imaging data has been recognized as an effective analysis that exploits its inherent multi-way nature. In particular, the advantages of tensorial over matrix-based methods have previously been demonstrated in the context of functional magnetic resonance imaging (fMRI) source localization. However, such methods can also become ineffective in realistic challenging scenarios, involving, e.g., strong noise and/or significant overlap among the activated regions. Moreover, they commonly rely on the assumption of an underlying multilinear model generating the data.-New Method: This paper aims at investigating the possible gains from exploiting the 4-dimensional nature of the brain images, through a higher-order tensorization of the fMRI signal, and the use of less restrictive generative models. In this context, the higher-order Block Term Decomposition (BTD) and the PARAFAC2 tensor models are considered for the first time in fMRI blind source separation. A novel PARAFAC2-like extension of BTD (BTD2) is also proposed, aiming at combining the effectiveness of BTD in handling strong instances of noise and the potential of PARAFAC2 to cope with datasets that do not follow the strict multilinear assumption.-Comparison with Existing Methods: The methods were tested using both synthetic and real data and compared with state of the art methods.-Conclusions: The simulation results demonstrate the effectiveness of BTD and BTD2 for challenging scenarios (presence of noise, spatial overlap among activation regions and inter-subject variability in the Haemodynamic Response Function (HRF)).

Data fusion refers to the joint analysis of multiple datasets which provide complementary views of the same task. In this preprint, the problem of jointly analyzing electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) data is considered. Jointly analyzing EEG and fMRI measurements is highly beneficial for studying brain function because these modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The fusion methods reported so far ignore the underlying multi-way nature of the data in at least one of the modalities and/or rely on very strong assumptions about the relation of the two datasets. In this preprint, these two points are addressed by adopting for the first time tensor models in the two modalities while also exploring double coupled tensor decompositions and by following soft and flexible coupling approaches to implement the multi-modal analysis. To cope with the Event Related Potential (ERP) variability in EEG, the PARAFAC2 model is adopted. The results obtained are compared against those of parallel Independent Component Analysis (ICA) and hard coupling alternatives in both simulated and real data. Our results confirm the superiority of tensorial methods over methods based on ICA. In scenarios that do not meet the assumptions underlying hard coupling, the advantage of soft and flexible coupled decompositions is clearly demonstrated.

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The growing use of neuroimaging technologies generates a massive amount of biomedical data that exhibit high dimensionality. Tensor-based analysis of brain imaging data has been proved quite effective in exploiting their multiway nature. The advantages of tensorial methods over matrix-based approaches have also been demonstrated in the characterization of functional magnetic resonance imaging (fMRI) data, where the spatial (voxel) dimensions are commonly grouped (unfolded) as a single way/mode of the 3-rd order array, the other two ways corresponding to time and subjects. However, such methods are known to be ineffective in more demanding scenarios, such as the ones with strong noise and/or significant overlapping of activated regions. This paper aims at investigating the possible gains from a better exploitation of the spatial dimension, through a higher-(4 or 5) order tensor modeling of the fMRI signal. In this context, and in order to increase the degrees of freedom of the modeling process, a higher-order Block Term Decomposition (BTD) is applied, for the first time in fMRI analysis. Its effectiveness is demonstrated via extensive simulation results.

Abstract-Tensor-based analysis of brain imaging data, in particular functional Magnetic Resonance Imaging (fMRI), has proved to be quite effective in exploiting their inherently multidimensional nature. It commonly relies on a trilinear model generating the analyzed data. This assumption, however, may prove to be quite strict in practice; for example, due to the natural intra-subject and inter-subject variability of the Haemodynamic Response Function (HRF). This paper investigates the possible gains from the adoption of a less strict trilinear model, such as PARAFAC2, which allows a more flexible representation of the fMRI data in the temporal domain. In this context, and inspired by a recently reported successful application of the Block Term Decomposition (BTD) model to a 4-way tensorization of the fMRI signal, a PARAFAC2-like extension of BTD (called here BTD2) is proposed. Simulation results are presented, that reveal the pros and cons of these tensorial methods, demonstrating BTD2's enhanced robustness to noise.