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Showing posts with the label Graph Convolution Network

2022-06-18: ADHD Prediction Through Analysis of Eye Movements With Graph Convolution Network

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Since processing speech with background noise requires appropriate parsing of the distorted auditory signal, individuals with attention deficit hyperactivity disorder (ADHD) may have difficulty processing speech with background noise due to reduced inhibitory control and working memory capacity. We conducted a study (Jayawardena et al.) by utilizing Audiovisual speech-in-noise (SIN) performance and eye-tracking measures of young adults with ADHD compared to age-matched controls for ADHD evaluation. In this study, there was five ADHD participants and six non-ADHD participants. We utilized eye tracking data recorded using a Tobii Pro X2-60 computer screen-based eye tracker. Each participant was told to watch a computer screen where a female speaks sentences out loud as levels of background noise varies and asked to repeat the sentences exactly as they heard them. The task consisted of varying six levels of background noise: 0 to 25 dB. Each participant was presented with nine senten...