Selective Attention Measurement of Experienced Simultaneous Interpreters Using EEG Phase-Locked Response.

We quantified the electroencephalogram signals associated with the selective attention processing of experienced simultaneous interpreters and calculated the phase-locked responses evoked by a 40-Hz auditory steady-state response (40-Hz ASSR) and the values of robust inter-trial coherence (ITC) for environmental changes. Since we assumed that an interpreter's attention ability improves with an increase in the number of years of experience of simultaneous interpretation, we divided the participants into two groups based on their simultaneous interpretation experience: experts with more than 15 years of experience (E group; n = 7) and beginners with <1 year (B group; n = 15). We also compared two conditions: simultaneous interpretation (SI) and shadowing (SH). We found a significant interaction in the ITC between years of SI experience (E and B groups) and tasks (SI and SH). This result demonstrates that the number of years of SI experience influences selective attention during interpretation.

Analysis of selective attention processing on experienced simultaneous interpreters using EEG phase synchronization.

This study analyzed the selective attention processing related to cognitive load on simultaneous interpretation (SI). We tested simultaneous interpreter's brain function using EEG signals and calculated inter-trial coherence (ITC) extracted by the 40-Hz auditory steady-state response (ASSR). In this experiment, we set two conditions as Japanese-English translation and Japanese shadowing cognition. We also compared two subject groups: S rank with more than 15 years of SI experience (n=7) and C rank with less than one year experience (n=15). As a result, the ITCs for S rank in interpreting conditions were more significantly increased than C rank in the shadowing conditions (ITC: p<0.001). Our results demonstrate that 40-Hz ASSR might be a good indicator of selective attention and cognitive load during SI in ecologically valid environmental conditions. It can also be used to detect attention and cognitive control dysfunction in ADHD or schizophrenia.

Sequential Attention-based Detection of Semantic Incongruities from EEG While Listening to Speech.

We propose a method with attention-based recurrent neural networks (ARNN) for detecting the semantic incongruities in spoken sentences using single-trial electroencephalogram (EEG) signals. 19 participants listened to sentences, some of which included semantically anomalous words. We recorded their EEG signals while they listened. Although previous detection approaches used a word's explicit onset, we used the EEG signals of the whole regions of each sentence, which made it possible to classify the correctness of the sentences without the onset information of the anomalous words. ARNN achieved 63.5% classification accuracy with a statistical significance above the chance level and also above the performances which includes onset information (50.9%). Our results also demonstrated that the attention weights of the model showed that the predictions depended on the feature vectors that are temporally close to the onsets of the anomalous words.