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EEG decoding of spoken words in bilingual listeners: from words to language invariant semantic-conceptual representations.

Correia JM, Jansma B, Hausfeld L, Kikkert S, Bonte M - Front Psychol (2015)

Bottom Line: Furthermore, employing two EEG feature selection approaches, we assessed the contribution of temporal and oscillatory EEG features to our classification results.Most interestingly, significant across-language generalization was possible around 550-600 ms, suggesting the activation of common semantic-conceptual representations from the Dutch and English nouns.We discuss how this method and results could be relevant to track the neural mechanisms underlying conceptual encoding in comprehension and production.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht Brain Imaging Center (M-BIC), Maastricht University Maastricht, Netherlands.

ABSTRACT
Spoken word recognition and production require fast transformations between acoustic, phonological, and conceptual neural representations. Bilinguals perform these transformations in native and non-native languages, deriving unified semantic concepts from equivalent, but acoustically different words. Here we exploit this capacity of bilinguals to investigate input invariant semantic representations in the brain. We acquired EEG data while Dutch subjects, highly proficient in English listened to four monosyllabic and acoustically distinct animal words in both languages (e.g., "paard"-"horse"). Multivariate pattern analysis (MVPA) was applied to identify EEG response patterns that discriminate between individual words within one language (within-language discrimination) and generalize meaning across two languages (across-language generalization). Furthermore, employing two EEG feature selection approaches, we assessed the contribution of temporal and oscillatory EEG features to our classification results. MVPA revealed that within-language discrimination was possible in a broad time-window (~50-620 ms) after word onset probably reflecting acoustic-phonetic and semantic-conceptual differences between the words. Most interestingly, significant across-language generalization was possible around 550-600 ms, suggesting the activation of common semantic-conceptual representations from the Dutch and English nouns. Both types of classification, showed a strong contribution of oscillations below 12 Hz, indicating the importance of low frequency oscillations in the neural representation of individual words and concepts. This study demonstrates the feasibility of MVPA to decode individual spoken words from EEG responses and to assess the spectro-temporal dynamics of their language invariant semantic-conceptual representations. We discuss how this method and results could be relevant to track the neural mechanisms underlying conceptual encoding in comprehension and production.

No MeSH data available.


Related in: MedlinePlus

Univariate results. (A) ERP in respect to baseline of each word over the channel FCz. The ERPs for English and Dutch words are plotted separately. Group level statistics of all words with respect to baseline (Wilcoxon's test, FDR corrected < 0.05) is depicted in black bars during the time course of the ERP responses. (B) ERP scalp maps for time-intervals characteristic of the ERP components (N1: 90–160; P2: 220–300; N400: 550–670). (C) ERSP (dB) with respect to baseline for all words. The ERSP time-frequency plot includes a statistical threshold for group level significance (Wilcoxon's test in respect to baseline period, FDR correction, alpha = 0.05).
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Figure 3: Univariate results. (A) ERP in respect to baseline of each word over the channel FCz. The ERPs for English and Dutch words are plotted separately. Group level statistics of all words with respect to baseline (Wilcoxon's test, FDR corrected < 0.05) is depicted in black bars during the time course of the ERP responses. (B) ERP scalp maps for time-intervals characteristic of the ERP components (N1: 90–160; P2: 220–300; N400: 550–670). (C) ERSP (dB) with respect to baseline for all words. The ERSP time-frequency plot includes a statistical threshold for group level significance (Wilcoxon's test in respect to baseline period, FDR correction, alpha = 0.05).

Mentions: We first conducted univariate analyses of ERP and time-frequency changes relatively to stimulus baseline in order to assess the overall spectro-temporal characteristics of EEG responses evoked by the animal words. Figure 3 illustrates the averaged ERP responses elicited by the different animal words, including the expected ERP peaks (channel Fcz, Figure 3A) and their corresponding topographies (Figure 3B), in the N1 window (120–160 ms), the P2 window (230–390 ms) and the N400 window (550–800 ms). To assess univariate differences between the ERP responses we conducted all possible word-to-word contrasts within the same language (e.g., horse vs. duck), as well as all possible concept-to-concept contrasts (e.g., horse + paard vs. duck + eend). None of the possible contrasts yielded significant differences within or across participants.


EEG decoding of spoken words in bilingual listeners: from words to language invariant semantic-conceptual representations.

Correia JM, Jansma B, Hausfeld L, Kikkert S, Bonte M - Front Psychol (2015)

Univariate results. (A) ERP in respect to baseline of each word over the channel FCz. The ERPs for English and Dutch words are plotted separately. Group level statistics of all words with respect to baseline (Wilcoxon's test, FDR corrected < 0.05) is depicted in black bars during the time course of the ERP responses. (B) ERP scalp maps for time-intervals characteristic of the ERP components (N1: 90–160; P2: 220–300; N400: 550–670). (C) ERSP (dB) with respect to baseline for all words. The ERSP time-frequency plot includes a statistical threshold for group level significance (Wilcoxon's test in respect to baseline period, FDR correction, alpha = 0.05).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4319403&req=5

Figure 3: Univariate results. (A) ERP in respect to baseline of each word over the channel FCz. The ERPs for English and Dutch words are plotted separately. Group level statistics of all words with respect to baseline (Wilcoxon's test, FDR corrected < 0.05) is depicted in black bars during the time course of the ERP responses. (B) ERP scalp maps for time-intervals characteristic of the ERP components (N1: 90–160; P2: 220–300; N400: 550–670). (C) ERSP (dB) with respect to baseline for all words. The ERSP time-frequency plot includes a statistical threshold for group level significance (Wilcoxon's test in respect to baseline period, FDR correction, alpha = 0.05).
Mentions: We first conducted univariate analyses of ERP and time-frequency changes relatively to stimulus baseline in order to assess the overall spectro-temporal characteristics of EEG responses evoked by the animal words. Figure 3 illustrates the averaged ERP responses elicited by the different animal words, including the expected ERP peaks (channel Fcz, Figure 3A) and their corresponding topographies (Figure 3B), in the N1 window (120–160 ms), the P2 window (230–390 ms) and the N400 window (550–800 ms). To assess univariate differences between the ERP responses we conducted all possible word-to-word contrasts within the same language (e.g., horse vs. duck), as well as all possible concept-to-concept contrasts (e.g., horse + paard vs. duck + eend). None of the possible contrasts yielded significant differences within or across participants.

Bottom Line: Furthermore, employing two EEG feature selection approaches, we assessed the contribution of temporal and oscillatory EEG features to our classification results.Most interestingly, significant across-language generalization was possible around 550-600 ms, suggesting the activation of common semantic-conceptual representations from the Dutch and English nouns.We discuss how this method and results could be relevant to track the neural mechanisms underlying conceptual encoding in comprehension and production.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht Brain Imaging Center (M-BIC), Maastricht University Maastricht, Netherlands.

ABSTRACT
Spoken word recognition and production require fast transformations between acoustic, phonological, and conceptual neural representations. Bilinguals perform these transformations in native and non-native languages, deriving unified semantic concepts from equivalent, but acoustically different words. Here we exploit this capacity of bilinguals to investigate input invariant semantic representations in the brain. We acquired EEG data while Dutch subjects, highly proficient in English listened to four monosyllabic and acoustically distinct animal words in both languages (e.g., "paard"-"horse"). Multivariate pattern analysis (MVPA) was applied to identify EEG response patterns that discriminate between individual words within one language (within-language discrimination) and generalize meaning across two languages (across-language generalization). Furthermore, employing two EEG feature selection approaches, we assessed the contribution of temporal and oscillatory EEG features to our classification results. MVPA revealed that within-language discrimination was possible in a broad time-window (~50-620 ms) after word onset probably reflecting acoustic-phonetic and semantic-conceptual differences between the words. Most interestingly, significant across-language generalization was possible around 550-600 ms, suggesting the activation of common semantic-conceptual representations from the Dutch and English nouns. Both types of classification, showed a strong contribution of oscillations below 12 Hz, indicating the importance of low frequency oscillations in the neural representation of individual words and concepts. This study demonstrates the feasibility of MVPA to decode individual spoken words from EEG responses and to assess the spectro-temporal dynamics of their language invariant semantic-conceptual representations. We discuss how this method and results could be relevant to track the neural mechanisms underlying conceptual encoding in comprehension and production.

No MeSH data available.


Related in: MedlinePlus