Objective Individuals with migraine exhibit heightened sensitivity to visual input that continues beyond their migraine episodes. However, the contribution of color to visual sensitivity, and how it relates to neural activity, has largely been unexplored in these individuals. Background Previously, it has been shown that, in non‐migraine individuals, patterns with greater chromaticity separation evoked greater cortical activity, regardless of hue, even when colors were isoluminant. Therefore, to investigate whether individuals with migraine experienced increased visual sensitivity, we compared the behavioral and neural responses to chromatic patterns of increasing separation in migraine and non‐migraine individuals. Methods Seventeen individuals with migraine (12 with aura) and 18 headache‐free controls viewed pairs of colored horizontal grating patterns that varied in chromaticity separation. Color pairs were either blue‐green, red‐green, or red‐blue. Participants rated the discomfort of the gratings and electroencephalogram was recorded simultaneously. Results Both groups showed increased discomfort ratings and larger N1/N2 event‐related potentials (ERPs) with greater chromaticity separation, which is consistent with increased cortical excitability. However, individuals with migraine rated gratings as being disproportionately uncomfortable and exhibited greater effects of chromaticity separation in ERP amplitude across occipital and parietal electrodes. Ratings of discomfort and ERPs were smaller in response to the blue‐green color pairs than the red‐green and red‐blue gratings, but this was to an equivalent degree across the 2 groups. Conclusions Together, these findings indicate that greater chromaticity separation increases neural excitation, and that this effect is heightened in migraine, consistent with the theory that hyper‐excitability of the visual system is a key signature of migraine.

We present a novel signal processing algorithm for automated, noninvasive detection of Cortical Spreading Depolarizations (CSDs) using electroencephalography (EEG) signals and validate the algorithm on simulated EEG signals. CSDs are waves of neurochemical changes that suppress neuronal activity as they propagate across the brain's cortical surface. CSDs are believed to mediate secondary brain damage after brain trauma and cerebrovascular diseases like stroke. We address key challenges in detecting CSDs from EEG signals: (i) decay of high spatial frequencies as they travel from the cortical surface to the scalp surface; and (ii) presence of sulci and gyri, which makes it difficult to track the CSD waves as they travel across the cortex. Our algorithm detects and tracks "wavefronts" of the CSD wave, and stitches together data across space and time to decide on the presence of a CSD wave. To test our algorithm, we provide different models and complex patterns of CSD waves, including different widths of CSD suppressions, and use these models to simulate scalp EEG signals using head models of 4 subjects from the OASIS dataset. Our results suggest that the average width of suppression that a low-density EEG grid of 40 electrodes can detect is 1.1 cm, which includes a vast majority of CSD suppressions, but not all. A higher density EEG grid having 340 electrodes can detect complex CSD patterns as thin as 0.43 cm (less than minimum widths reported in prior works), among which single-gyrus propagation is the hardest to detect because of its small suppression area.

Cell detection and segmentation is fundamental for all downstream analysis of digital pathology images. However, obtaining the pixel-level ground truth for single cell segmentation is extremely labor intensive. To overcome this challenge, we developed an end-to-end deep learning algorithm to perform both single cell detection and segmentation using only point labels. This is achieved through the combination of different task orientated point label encoding methods and a multi-task scheduler for training. We apply and validate our algorithm on PMS2 stained colon rectal cancer and tonsil tissue images. Compared to the state-of-the-art, our algorithm shows significant improvement in cell detection and segmentation without increasing the annotation efforts.

Abstract. Brain Computer Interface (BCI) systems, which are based on motor imagery, enable humans to command arti cial peripherals by merely thinking about the task. There is a tremendous interest in implementing BCIs on portable platforms, such as Field Programmable Gate Arrays (FPGAs) due to their low-cost, low-power and portability characteristics. This article presents the design and implementation of a Brain Computer Interface (BCI) system based on motor imagery on a Virtex-6 FPGA. In order to design an accurate algorithm, the proposed method avails statistical learning methods such as Mutual Information (MI), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM). It also uses Separable Common Spatio Spectral Pattern (SCSSP) method in order to extract features. Simulation results prove achieved performances of 73.54% for BCI competition III-dataset V, 67.2% for BCI competition IV-dataset 2a with all four classes, 80.55% for BCI competition IV-dataset 2a with the rst two classes, and 81.9% for captured signals. Moreover, the nal reported hardware resources determine its e ciency as a result of using retiming and folding techniques from the VLSI architecture' perspective. The complete proposed BCI system achieves not only excellent recognition accuracy, but also remarkable implementation e ciency in terms of portability, power, time, and cost.

Individuals with migraine generally experience photophobia and/or phonophobia during and between migraine attacks. Many different mechanisms have been postulated to explain these migraine phenomena including abnormal patterns of connectivity across the cortex. The results, however, remain contradictory and there is no clear consensus on the nature of the cortical abnormalities in migraine. Here, we uncover alterations in cortical patterns of coherence (connectivity) in interictal migraineurs during the presentation of visual and auditory stimuli, and during rest. We used a high-density EEG system, with 128 customized electrode locations, to compare inter- and intra-hemispheric coherence in the interictal period from 17 individuals with migraine (12 female) and 18 age- and gender-matched healthy control subjects. During presentations of visual (vertical grating pattern) and auditory (modulated tone) stimulation which varied in temporal frequency (4 and 6 Hz), and during rest, participants performed a color detection task at fixation. Analyses included characterizing the inter- and intra-hemisphere coherence between the scalp EEG channels over 2-second time intervals and over different frequency bands at different spatial distances and spatial clusters. Pearson’s correlation coefficients were estimated at zero-lag. Repeated measures analyses-of-variance revealed that, relative to controls, migraineurs exhibited significantly (i) faster color detection performance, and (ii) lower spatial coherence of alpha-band activity, for both inter- and intra-hemisphere connections, and (iii) the reduced coherence occurred predominantly in frontal clusters during both sensory conditions, regardless of the stimulation frequency, as well as during the resting-state. The abnormal patterns of EEG coherence in interictal migraineurs during visual and auditory stimuli, as well as at rest (eyes open), may be associated with the cortical hyper-responsivity that is characteristic of abnormal sensory processing in migraineurs.