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Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
Published on: July 5, 2015
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An Interpretable Performance Metric for Auditory Attention Decoding Algorithms in a Context of Neuro-Steered Gain
Summary
Hearing aids struggle to identify attended speakers. This study introduces a new metric, minimal expected switch duration (MESD), to objectively evaluate auditory attention decoding (AAD) algorithms for better neuro-steered hearing aid control.
Area of Science:
- Neuroscience
- Signal Processing
- Biomedical Engineering
Background:
- Hearing aids in multi-speaker environments struggle to identify user's intended speaker.
- Brain activity, via electroencephalography (EEG), can decode attended speakers.
- Current auditory attention decoding (AAD) algorithms lack uniform performance evaluation and objective benchmarking.
Purpose of the Study:
- To introduce an interpretable performance metric for evaluating AAD algorithms.
- To resolve the trade-off between AAD accuracy and decision time in neuro-steered hearing aids.
- To provide a method for optimizing AAD-based adaptive gain control systems.
Main Methods:
- Modeled an adaptive gain control system steered by AAD decisions as a Markov chain.
- Calculated the minimal expected switch duration (MESD) as a performance metric.
- Demonstrated MESD's ability to optimize gain levels and decision time.
Main Results:
- The MESD metric provides an objective evaluation of AAD algorithms.
- MESD quantifies the expected time to switch hearing aid operation after user attention shifts.
- MESD facilitates automatic and theoretically founded optimization of AAD system parameters.
Conclusions:
- MESD offers a novel, interpretable metric for AAD algorithm benchmarking.
- This metric addresses the critical trade-off between AAD accuracy and decision latency.
- The MESD calculation enables optimized neuro-steered hearing aid gain control.
