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Fragments movie 200812/8/2023 ![]() The most successful single‐trial MEG classification so far was 60.1% (with cross‐validation) for nine auditorily presented words in a set of 900 trials however, the result was from a single subject only. Previously, single words or phrases have been associated with MEG/EEG responses on the basis of pattern classification either single‐trial or averaged response waveforms were matched to prototype waveforms of each class of words or phrases, created by averaging over tens or hundreds of trials in 3–48 classes. Here we introduce a novel signal‐analysis approach that attempts to extract MEG responses elicited by continuous speech and even enables us to identify, on the basis of the MEG signature, the related speech fragment. ![]() The main reason has been the lack of suitable data analysis methods for nonaveraged ongoing MEG/EEG signals. magnetoencephalographic (MEG) and electroencephalographic (EEG) signals. The speech sounds naturally leave traces to the listener's brain activity, but still it has remained highly challenging to identify perceptual correlates of natural continuous speech in e.g. © 2012 Wiley Periodicals, Inc.Īs a fundamental prerequisite for speech perception and comprehension, our brains have a remarkable ability to follow the rapidly changing sound sequence of natural speech. The applied analysis approach thus allowed identification of segments of natural speech by means of partial reconstruction of the continuous speech envelope (i.e., the intensity variations of the speech sounds) from MEG responses, provided means to empirically assess the time scales obtainable in speech decoding with the canonical variates, and it demonstrated accurate identification of the heard speech fragments from the MEG data. By splitting the test signals into equal‐length fragments from 2 to 65 s (corresponding to 703 down to 21 pieces per the total speech stimulus) we obtained better than chance‐level identification for speech fragments longer than 2–3 s, not used in the model training. We found shared signal time series (canonical variates) between the MEG signals and speech envelopes at 0.5–12 Hz. Seven healthy adults listened to news for an hour while their brain signals were recorded with whole‐scalp MEG. ![]() To relate magnetoencephalographic (MEG) brain responses to the physical properties of such speech stimuli, we applied canonical correlation analysis (CCA) and a Bayesian mixture of CCA analyzers to extract MEG features related to the speech envelope. It is a challenge for current signal analysis approaches to identify the electrophysiological brain signatures of continuous natural speech that the subject is listening to.
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