EEG study of the neural representation and classification of semantic categories of animals vs tools in young and elderly participants

Gu, Yong; Cazzolli, Giulia; Murphy, Brian; Miceli, Gabriele; Poesio, Massimo

doi:10.1186/1471-2202-14-s1-p318

Cited by 2 publications

(2 citation statements)

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“…Both EEG and MEG come with their own advantages, however. EEG is particularly cheap and portable, two reasons which make it also the preferred choice for brain-computer interfaces (Allison et al, 2020 ) and for working with elderly patients (Gu et al, 2013 ); and, provided one uses enough channels, it can be used as a sort of brain imaging tool (Michel and Murray, 2012 ). MEG, despite being expensive, provides much better signal and spatial resolution, and more channels by default.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics

Bruera

Poesio

2022

Front. Artif. Intell.

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Semantic knowledge about individual entities (i.e., the referents of proper names such as Jacinta Ardern) is fine-grained, episodic, and strongly social in nature, when compared with knowledge about generic entities (the referents of common nouns such as politician). We investigate the semantic representations of individual entities in the brain; and for the first time we approach this question using both neural data, in the form of newly-acquired EEG data, and distributional models of word meaning, employing them to isolate semantic information regarding individual entities in the brain. We ran two sets of analyses. The first set of analyses is only concerned with the evoked responses to individual entities and their categories. We find that it is possible to classify them according to both their coarse and their fine-grained category at appropriate timepoints, but that it is hard to map representational information learned from individuals to their categories. In the second set of analyses, we learn to decode from evoked responses to distributional word vectors. These results indicate that such a mapping can be learnt successfully: this counts not only as a demonstration that representations of individuals can be discriminated in EEG responses, but also as a first brain-based validation of distributional semantic models as representations of individual entities. Finally, in-depth analyses of the decoder performance provide additional evidence that the referents of proper names and categories have little in common when it comes to their representation in the brain.

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Section: Introductionmentioning

confidence: 99%

Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics

Bruera

Poesio

2022

Front. Artif. Intell.

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“…Using event-related EEG and multivariate pattern analysis, Simanova et al, 2010 studied the conceptual representation and classification of object categories in different modalities. In other work, we have used EEG and machine learning to decode the semantic categories of animals vs tools in younger and elderly subjects during a covert image naming task (Murphy et al, 2011;Gu et al, 2013). In this work, we apply this approach to the decoding of the emotional valence of written words, and propose a novel paradigm for using such decoding techniques for sentiment analysis.…”

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confidence: 99%

Using Brain Data for Sentiment Analysis

Gu¹,

Celli²,

Shinar³

et al. 2014

JLCL

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We present the results of exploratory experiments using lexical valence extracted from brain using electroencephalography (EEG) for sentiment analysis. We selected 78 English words (36 for training and 42 for testing), presented as stimuli to 3 English native speakers. EEG signals were recorded from the subjects while they performed a mental imaging task for each word stimulus. Wavelet decomposition was employed to extract EEG features from the time-frequency domain. The extracted features were used as inputs to a sparse multinomial logistic regression (SMLR) classifier for valence classification, after univariate ANOVA feature selection. After mapping EEG signals to sentiment valences, we exploited the lexical polarity extracted from brain data for the prediction of the valence of 12 sentences taken from the SemEval-2007 shared task, and compared it against existing lexical resources.

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EEG study of the neural representation and classification of semantic categories of animals vs tools in young and elderly participants

Cited by 2 publications

References 1 publication

Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics

Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics

Using Brain Data for Sentiment Analysis

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