The eyes serve as a window into underlying physical and cognitive processes. Although factors such as pupil size have been studied extensively, a less explored yet potentially informative aspect is blinking. Given its novelty, blink detection techniques are far less available compared to eye-tracking and pupil size estimation tools. In this work, we present a new unsupervised machine learning blink detection strategy using existing eye-tracking technology. The method is compared to two existing techniques. All three algorithms make use of eye aspect ratio values for blink detection. Accurate and rapid blink detection complements existing eye-tracking research and may provide a new informative index of physical and mental status.

Chiari malformation type 1 (CM1) includes a series of congenital anomalies that share ectopia of the cerebellar tonsils below the foramen magnum, in some cases associated with syringomyelia or hydrocephalus. CM1 can cause dysfunction of the brainstem, spinal cord, and cranial nerves. This functional alteration of the nervous system can be detected by various modalities of neurophysiological tests, such as brainstem auditory evoked potentials, somatosensory evoked potentials, motor evoked potentials, electromyography and nerve conduction studies of the cranial nerves and spinal roots, as well as brainstem reflexes. The main goal of this study is to review the findings of multimodal neurophysiological examinations in published studies of patients with CM1 and their indication in the diagnosis, treatment, and follow-up of these patients, as well as their utility in intraoperative monitoring.