首页|Ohio University Researcher Has Provided New Study Findings on Machine Learning (Advancing Auditory Processing by Detecting Frequency-Following Responses Through a Specialized Machine Learning Model)
Ohio University Researcher Has Provided New Study Findings on Machine Learning (Advancing Auditory Processing by Detecting Frequency-Following Responses Through a Specialized Machine Learning Model)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Data detailed on artificial intelligence have been presented. According to news reportingout of Athens, Ohio, by NewsRx editors, research stated, “In this study, we explore the feasibility andperformance of detecting scalp-recorded frequency-following responses (FFRs) with a specialized machinelearning (ML) model.”Our news journalists obtained a quote from the research from Ohio University: “By leveraging thestrengths of feature extraction of the source separation non-negative matrix factorization (SSNMF) algorithmand its adeptness in handling limited training data, we adapted the SSNMF algorithm into aspecialized ML model with a hybrid architecture to enhance FFR detection amidst background noise. Werecruited 40 adults with normal hearing and evoked their scalp recorded FFRs using the English vowel/i/witha rising pitch contour. The model was trained on FFR-present and FFR-absent conditions, and its performancewas evaluated using sensitivity, specificity, efficiency, false-positive rate, and false-negative ratemetrics. This study revealed that the specialized SSNMF model achieved heightened sensitivity, specificity,and efficiency in detecting FFRs as the number of recording sweeps increased. Sensitivity exceeded 80%at 500 sweeps and maintained over 89% from 1000 sweeps onwards. Similarly, specificity and efficiencyalso improved rapidly with increasing sweeps.”
Ohio UniversityAthensOhioUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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