Real-time estimation of perceptual thresholds based on the electroencephalogram using a deep neural network

Background: Perceptual thresholds are measured in scientific and clinical setting to evaluate performance of the nervous system in essential tasks such as vision, hearing, touch, and registration of pain. Current procedures for estimating perceptual thresholds depend on the analysis of pairs of stimuli and participant responses, relying on the commitment and cognitive ability of subjects to respond accurately and consistently to stimulation. Here, we demonstrate that it is possible to measure the threshold for the perception of nociceptive stimuli based on non-invasively recorded brain activity alone using a deep neural network.

New method: For each stimulus, a trained deep neural network performed a 2-interval forced choice procedure, in which the network had to choose which of two time intervals in the electroencephalogram represented post-stimulus brain activity. Network responses were used to estimate the perceptual threshold in real-time using a psychophysical method of limits.

Comparison with existing methods: Network classification was able to match participants in reporting stimulus perception, resulting in average network-estimated perceptual thresholds that matched perceptual thresholds based on participant reports.

Results: The neural network successfully separated trials containing brain responses from trials without and could consistently estimate perceptual thresholds in real-time during a Go-/No-Go procedure and a counting task.

Conclusion: Deep neural networks monitoring non-invasively recorded brain activity are now able to accurately predict stimulus perception and estimate the perceptual threshold in real-time without any verbal or motor response from the participant.