TicTorque: diagnosing effects of blink tics through mobile EEG headsets
07 Sep 2015-pp 25-28
TL;DR: There is a case for applications involving the use of EEG headsets to monitor and aid students and workers with blink tics to ensure equitable time conditions and to demonstrate that time taken can increase up to 300% depending on the type of work, due to excessive blinking.
Abstract: In this work, we aim to explore the use of non-invasive, wearable EEG headsets for detecting and diagnosing the effects of blink tics. First, we demonstrate the use of the headset in the accurate detection of blink tics as compared to more expensive and cumbersome technology. Comparing against invasive facial monitoring techniques using Smartphones, EEG driven detection is able to achieve 80% accuracy in correctly detected events, with a detection delay of less than 1 second. Next, we use these headsets to draw a positive correlation between excessive blinking and deterioration in attention and performance to support past research, and demonstrate that time taken for the same work can increase up to 300% depending on the type of work, due to excessive blinking. Finally, we make a case for applications involving the use of EEG headsets to monitor and aid students and workers with blink tics to ensure equitable time conditions.
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TL;DR: This study identified three normal behavioral BR patterns and showed that BR is more influenced by cognitive processes than by age, eye color, or local factors, and provides a normal reference for the analysis of BR in movement disorders such as dystonia or tics.
Abstract: The present study measured the normal blink rate (BR) variations in relation to behavioral tasks in 150 healthy volunteers (70 males and 80 females; aged 35.9 +/- 17.9 years, range 5-87 years). The subjects were videotaped in a standard setting while performing three different tasks: resting quietly, reading a short passage, talking freely. The mean BR was computed during each task; the data were compared by means of analysis of variance and Student's t tests. Mean BR at rest was 17 blinks/min, during conversation it increased to 26, and it was as low as 4.5 while reading. As compared with rest, BR decreased by -55.08% while reading (p rest > reading, which occurred in 101 subjects (67.3%); 34 subjects (22.7%) had the pattern rest > conversation > reading; 12 (8.0%) had the pattern conversation > reading > rest. This study identified three normal behavioral BR patterns and showed that BR is more influenced by cognitive processes than by age, eye color, or local factors. The present findings provide a normal reference for the analysis of BR in movement disorders such as dystonia or tics.
442 citations
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TL;DR: Dysfunction of basal ganglia-thalamo-cortical projections affects sensorimotor, language and limbic cortical circuits, and may explain why patients with Tourette syndrome have difficulty in inhibiting unwanted behaviors and impulses.
Abstract: Tics are involuntary movements that can affect one or more muscles producing simple or complex movements. Blink reflex and startle reflex studies disclose an increased excitability of brainstem interneurons. Analysis of voluntary movement shows that when advance visual information is reduced, patients with tics and Tourette syndrome become progressively slower in completing motor sequences. Sensorimotor integration is abnormally processed. Studies of the contingent negative variation demonstrate abnormalities of movement preparation and the investigation of premotor potentials shows that in some patients tics are not preceded by a normal premotor potential. Magnetic stimulation studies demonstrate an increased excitability of cortical motor cortex. Functional MRI, PET and SPECT studies show abnormal activation of cortical and subcortical areas. Dysfunction of basal ganglia-thalamo-cortical projections affects sensorimotor, language and limbic cortical circuits, and may explain why patients with Tourette syndrome have difficulty in inhibiting unwanted behaviors and impulses.
94 citations
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30 May 2008TL;DR: The main objective of this study was to develop a neural network classifier specialized in the identification of eye blinks in EEG signals that can be erroneously interpreted as epileptiform activity.
Abstract: The electroencephalogram is a noninvasive record of the brain activity using electrodes placed on the scalp. The electroencephalographic signal can be contaminated by other signal sources, called artifacts. Among the several artifact sources, eye blink is one of the main sources of interference in the EEG exam, and can be erroneously interpreted as epileptiform activity. This study analyzed eye blink signals acquired by EEG electrodes. The main objective of this study was to develop a neural network classifier specialized in the identification of eye blinks in EEG signals. The statistical study of the eye blinks in EEG signals, the methodology and the results of the identification of this event are presented.
33 citations