Interpretation of perceptron weights as constructed time series for EEG classification

Wong, Dik Kin; Uy, E. Timothy; Guimaraes, Marcos Perreau; W, Yang; Suppes, Patrick

doi:10.1016/j.neucom.2006.01.020

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Multiple oscillators with different natural frequencies will be required for such similarity recognition. For examples of such computational models applied to EEG-recorded brain data, see Suppes et al (1997Suppes et al ( , 1998Suppes et al ( , 1999a for Fourier methods, de Barros et al (2006) for a Laplacian model, Suppes et al (2009) for congruence between brain and perceptual feature of language, and Wong et al (2006) for perceptron models with regularization and independent component analysis.…”

Section: Conclusion and Final Remarksmentioning

confidence: 99%

Phase-oscillator computations as neural models of stimulus–response conditioning and response selection

Suppes

Barros

Oas

2012

Journal of Mathematical Psychology

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The activity of collections of synchronizing neurons can be represented by weakly coupled nonlinear phase oscillators satisfying Kuramoto's equations. In this article, we build such neural-oscillator models, partly based on neurophysiological evidence, to represent approximately the learning behavior predicted and confirmed in three experiments by well-known stochastic learning models of behavioral stimulus-response theory. We use three Kuramoto oscillators to model a continuum of responses, and we provide detailed numerical simulations and analysis of the three-oscillator Kuramoto problem, including an analysis of the stability points for different coupling conditions. We show that the oscillator simulation data are well-matched to the behavioral data of the three experiments.

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Section: Conclusion and Final Remarksmentioning

confidence: 99%

Phase-oscillator computations as neural models of stimulus–response conditioning and response selection

Suppes

Barros

Oas

2012

Journal of Mathematical Psychology

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“…That is, the following question was answered affirmatively for EEG and, in one instance, magnetoencephalographic (MEG) data. Can we train a classifier of brain data so that we can predict which word or sentence or word within a sentence the participant is seeing or hearing [6], [7], [8], [9], [10], [11], [12]? Similarly, the following questions were answered.…”

Section: Introductionmentioning

confidence: 99%

Structural Similarities between Brain and Linguistic Data Provide Evidence of Semantic Relations in the Brain

2013

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This paper presents a new method of analysis by which structural similarities between brain data and linguistic data can be assessed at the semantic level. It shows how to measure the strength of these structural similarities and so determine the relatively better fit of the brain data with one semantic model over another. The first model is derived from WordNet, a lexical database of English compiled by language experts. The second is given by the corpus-based statistical technique of latent semantic analysis (LSA), which detects relations between words that are latent or hidden in text. The brain data are drawn from experiments in which statements about the geography of Europe were presented auditorily to participants who were asked to determine their truth or falsity while electroencephalographic (EEG) recordings were made. The theoretical framework for the analysis of the brain and semantic data derives from axiomatizations of theories such as the theory of differences in utility preference. Using brain-data samples from individual trials time-locked to the presentation of each word, ordinal relations of similarity differences are computed for the brain data and for the linguistic data. In each case those relations that are invariant with respect to the brain and linguistic data, and are correlated with sufficient statistical strength, amount to structural similarities between the brain and linguistic data. Results show that many more statistically significant structural similarities can be found between the brain data and the WordNet-derived data than the LSA-derived data. The work reported here is placed within the context of other recent studies of semantics and the brain. The main contribution of this paper is the new method it presents for the study of semantics and the brain and the focus it permits on networks of relations detected in brain data and represented by a semantic model.

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Interpretation of perceptron weights as constructed time series for EEG classification

Cited by 2 publications

References 15 publications

Phase-oscillator computations as neural models of stimulus–response conditioning and response selection

Phase-oscillator computations as neural models of stimulus–response conditioning and response selection

Structural Similarities between Brain and Linguistic Data Provide Evidence of Semantic Relations in the Brain

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