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Predicting Premature Video Skipping and Viewer Interest from EEG Recordings

ABSTRACT: Brain–computer interfacing has enjoyed growing attention, not only due to the stunning demonstrations with severely disabled patients, but also the advent of economically viable solutions in areas such as neuromarketing, mental state monitoring, and future human–machine interaction. An interesting case, at least for neuromarketers, is to monitor the customer’s mental state in response to watching a commercial. In this paper, as a novelty, we propose a method to predict from electroencephalography (EEG) recordings whether individuals decide to skip watching a video trailer. Based on multiscale sample entropy and signal power, indices were computed that gauge the viewer’s engagement and emotional affect. We then trained a support vector machine (SVM), a k-nearest neighbor (kNN), and a random forest (RF) classifier to predict whether the viewer declares interest in watching the video and whether he/she decides to skip it prematurely. Our model achieved an average single-subject classification accuracy of 75.803% for skipping and 73.3% for viewer interest for the SVM, 82.223% for skipping and 78.333% for viewer interest for the kNN, and 80.003% for skipping and 75.555% for interest for the RF. We conclude that EEG can provide indications of viewer interest and skipping behavior and provide directions for future research.

SUBMITTER: Libert A

PROVIDER: S-EPMC7514236 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Project description:Using measures of reflex impairment and injury to quantify an aquatic organism's vitality have gained popularity as survival predictors of discarded non-target fisheries catch. To evaluate the robustness of this method with respect to 'rater' subjectivity, we tested inter- and intra-rater repeatability and the role of 'expectation bias'. From video clips, multiple raters determined impairment levels of four reflexes of beam-trawled common sole (Solea solea) intended for discard. Raters had a range of technical experience, including veterinary students, practicing veterinarians, and fisheries scientists. Expectation bias was evaluated by first assessing a rater's assumption about the effect of air exposure on vitality, then comparing their reflex ratings of the same fish, once when the true air exposure duration was indicated and once when the time was exaggerated (by either 15 or 30 min). Inter-rater repeatability was assessed by having multiple raters evaluate those clips with true air exposure information; and intra- and inter-rater repeatability was determined by having individual raters evaluate a series of duplicated clips, all with true air exposure. Results indicate that inter- and intra-rater repeatability were high (intra-class correlation coefficients of 74% for both), and were not significantly affected by background type nor expectation bias related to assumed impact from prolonged air exposure. This suggests that reflex impairment as a metric for predicting fish survival is robust to involving multiple raters with diverse backgrounds. Bias is potentially more likely to be introduced through subjective reflexes than raters, given that consistency in scoring differed for some reflexes based on rater experience type. This study highlights the need to provide ample training for raters, and that no prior experience is needed to become a reliable rater. Moreover, before implementing reflexes in a vitality study, it is important to evaluate whether the determination of presence/absence is subjective.

Project description:Objective A recent multicenter prospective study (DECIDE trial) examined the use of Ceribell Rapid Response EEG (Rapid-EEG) in the emergent evaluation and management of critically ill patients suspected to have non-convulsive seizures. We present a detailed, patient-level examination of seizures detected either on initial Rapid-EEG or subsequent conventional EEG within 24 h to investigate whether seizures were missed on Rapid-EEG due to the exclusion of midline/parasagittal coverage. Methods We identified from 164 patients studied in the DECIDE trial those who had seizures detected on Rapid-EEG but not conventional EEG (n = 6), conventional EEG but not Rapid-EEG (n = 4), or both Rapid-EEG and conventional EEG (n = 9). We examined the electrographic characteristics of ictal and interictal findings on both devices, especially their detection in lateral or midline/parasagittal chains, and patient clinical histories to identify contributors toward discordant seizure detection. Results Seizures detected on both EEG systems had similar electrographic appearance and laterality. Seizures detected only on conventional EEG (within 24 h following Rapid-EEG) were visible in the temporal chains, and external clinical factors (e.g., treatment with anti-seizure medications, sedation, and duration of recordings) explained the delayed presentation of seizures. Patients with seizures detected only by Rapid-EEG were treated with anti-seizure medications, and subsequent conventional EEG detected interictal highly epileptiform patterns with similar laterality. Conclusions Our case series demonstrates that electrographic data obtained from initial Rapid-EEG and subsequent conventional EEG monitoring are largely concordant relative to morphology and laterality. These findings are valuable to inform future investigation of abbreviated EEG systems to optimize management of suspected non-convulsive seizures and status epilepticus. Future, larger studies could further investigate the value of Rapid-EEG findings for forecasting and predicting seizures in long-term EEG recordings.

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Predicting Premature Video Skipping and Viewer Interest from EEG Recordings

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