The Dream Catcher experiment: blinded analyses failed to detect markers of dreaming consciousness in EEG spectral power.

In a test of whether brain activity alone can reveal when someone is dreaming, researchers used an unsupervised machine learning classifier to distinguish dreamful from dreamless sleep based on EEG spectral power and electrode location. Nine participants contributed 54 one-minute polysomnograms from non-rapid eye movement sleep—27 with dreams and 27 without. A blinded Analysis Team attempted to classify each recording over five iterations with gradually reduced blindness. At no stage did the classifier perform significantly better than chance, indicating that EEG spectral power features could not reliably detect signatures of phenomenal consciousness in this dataset.

Study at a glance

Abstract

The Dream Catcher test defines the criteria for a genuine discovery of the neural constituents of phenomenal consciousness. Passing the test implies that some patterns of purely brain-based data directly correspond to the subjective features of phenomenal experience, which would help to bridge the explanatory gap between consciousness and brain. Here, we conducted the Dream Catcher test for the first time in a step-wise and simplified form, capturing its core idea. The Dream Catcher experiment involved a Data Team, which measured participants' brain activity during sleep and collected dream reports, and a blinded Analysis Team, which was challenged to predict, based solely on brain measurements, whether or not a participant had a dream experience. Using a serial-awakening paradigm, the Data Team prepared 54 1-min polysomnograms of non-rapid eye movement sleep-27 of dreamful sleep and 27 of dreamless sleep (three of each condition from each of the nine participants)-redacting from them all associated participant and dream information. The Analysis Team attempted to classify each recording as either dreamless or dreamful using an unsupervised machine learning classifier, based on hypothesis-driven, extracted features of electroencephalography (EEG) spectral power and electrode location. The procedure was repeated over five iterations with a gradual removal of blindness. At no level of blindness did the Analysis Team perform significantly better than chance, suggesting that EEG spectral power could not be utilized to detect signatures specific to phenomenal consciousness in these data. This study marks the first step towards realizing the Dream Catcher test in practice.