Abstract
Brain-computer interfaces (BCIs) are systems that use real-time analysis of neuroimaging data to determine the mental state of their user for purposes such as providing neurofeedback. Here, we investigate the feasibility of a BCI based on speech perception. Multivariate pattern classification methods were applied to single-trial EEG data collected during speech perception by native and non-native speakers. Two principal questions were asked: 1) Can differences in the perceived categories of pairs of phonemes be decoded at the single-trial level? 2) Can these same categorical differences be decoded across participants, within or between native-language groups? Results indicated that classification performance progressively increased with respect to the categorical status (within, boundary or across) of the stimulus contrast, and was also influenced by the native language of individual participants. Classifier performance showed strong relationships with traditional event-related potential measures and behavioral responses. The results of the cross-participant analysis indicated an overall increase in average classifier performance when trained on data from all participants (native and non-native). A second cross-participant classifier trained only on data from native speakers led to an overall improvement in performance for native speakers, but a reduction in performance for non-native speakers. We also found that the native language of a given participant could be decoded on the basis of EEG data with accuracy above 80%. These results indicate that electrophysiological responses underlying speech perception can be decoded at the single-trial level, and that decoding performance systematically reflects graded changes in the responses related to the phonological status of the stimuli. This approach could be used in extensions of the BCI paradigm to support perceptual learning during second language acquisition.
Highlights
Learning foreign languages is difficult, in part because they often make use of sounds which are unfamiliar
What if it were possible to detect these signals in single-trial EEG data? Research using multivariate pattern classification methods and brain-computer interface (BCI) paradigms has shown that this is feasible for signals such as the P3 response [2,3]
Grand averaged event-related potentials (ERPs) responses to the standard and deviant stimuli across the three measurement conditions for both the native-English and native-Dutch groups are presented in Figure 2b, along with difference waves obtained by subtracting the grand average ERP for the standard stimulus from that of the deviant stimulus in each condition
Summary
Learning foreign languages is difficult, in part because they often make use of sounds which are unfamiliar. Studies of human language perception have made use of EEG measurements to reveal differences in the processing of speech sounds by the brains of native and nonnative listeners. The results of these studies are typically based on the analysis of event-related potentials collected over hundreds of trials and using many individual participants. This is done because the signals of interest are much smaller in amplitude than the ongoing brain activity measured during single-trials [1]. If it was possible to detect the brain responses underlying speech perception, it could allow for the development of BCIs that support the learning of foreign languages through the monitoring of ongoing perception, or by providing feedback to users on their brain’s responses
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