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Valeriani, D.

Publications and source records attributed to Valeriani, D..

2 recordsLinked to original sources

EEG-Based Focus Estimation Using Neurable's Enten Headphones and Analytics Platform

We introduce Neurables research on focus using our recently developed Enten EEG headphones. First we quantify Entens performance on standard EEG protocols, including eyes-closed alpha rhythms, auditory evoked response and the P300 event-related potential paradigm. We show that Entens performance is on-par with established industry-standard hardware. We then introduce a series of experimental tasks designed to mimic how focus might be maintained or disrupted in a real-world office setting. We show that (A) these tasks induce behavioral changes that reflect underlying changes in focus levels and (B) our proprietary algorithm detects these changes across a large number of sessions without needing to adjust the model per participant or recording session. Through manipulation of our experimental protocol, we show that our algorithm is not dependent on gross EMG artifacts and it is driven by changes in EEG. Finally, we evaluated the models performance on the same subject across several days, and show that performance remained consistent over time. Our model correctly captured 80% {+/-} 4.1% of distractions present in our experiments with statistical significance. This indicates that our model generalizes across subjects, time points, and conditions. Our findings are based on EEG data collected from 132 participants across 337 sessions and 45 different experiments.

neuroscience↗

Cyborg Groups Enhance Face Recognition in Crowded Environments

Recognizing a person in a crowded environment is a challenging, yet critical, visual-search task for both humans and machine-vision algorithms. This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interfaces (BCIs) and human participants to create \"cyborgs\" that improve decision making. Human participants and a ResNet undertook the same face-recognition experiment. BCIs were used to decode the decision confidence of humans from their EEG signals. Different types of cyborg groups were created, including either only humans (with or without the BCI) or groups of humans and the ResNet. Cyborg groups decisions were obtained weighing individual decisions by confidence estimates. Results show that groups of cyborgs are significantly more accurate (up to 35%) than the ResNet, the average participant, and equally-sized groups of humans not assisted by technology. These results suggest that melding humans, BCI, and machine-vision technology could significantly improve decision-making in realistic scenarios.

neuroscience↗