Partial Orders of Similarity Differences Invariant Between EEG-Recorded Brain and Perceptual Representations of Language

Suppes, Patrick; Perreau-Guimaraes, Marcos; Wong, Dik Kin

doi:10.1162/neco.2009.04-08-764

Cited by 13 publications

(16 citation statements)

References 27 publications

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“…Representing the signal in the frequency domain makes possible the use of filtering techniques to remove the oscillation components that are unrelated to phoneme perception. We have shown in previous work that we can usually improve the recognition rate by finding an optimal frequency range over a grid of low-and highfrequency cutoffs (20). Using the phase-only model, we first looked for the approximate optimal frequency range for recognizing the eight initial consonants through a 10-fold cross-validation using only the trials from the training set.…”

Section: Results Of Recognizing Initial Consonants Using Temporal Andmentioning

confidence: 99%

“…The normalized confusion matrix of recognition results provided empirical evidence by which to order the similarity-differences of the brain wave representations of phonemes. Our previous work (20) proposed a method to derive invariant partial orders of similarity-differences of brain and perceptual representations and corresponding similarity trees using the estimated conditional probability matrices. The same technique was used to analyze the recognition results in this article.…”

Section: Methodsmentioning

confidence: 99%

“…More specifically, in previous work (20) we investigated the EEG-recorded brain waves of language constituents using timedomain signals. Partial orders of similarity-difference were shown to be invariant between perceptual and brain representations of phonemes, words, and sentences.…”

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confidence: 99%

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Using phase to recognize English phonemes and their distinctive features in the brain

Wang

Perreau-Guimaraes

Carvalhaes

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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The neural mechanisms used by the human brain to identify phonemes remain unclear. We recorded the EEG signals evoked by repeated presentation of 12 American English phonemes. A support vector machine model correctly recognized a high percentage of the EEG brain wave recordings represented by their phases, which were expressed in discrete Fourier transform coefficients. We show that phases of the oscillations restricted to the frequency range of 2-9 Hz can be used to successfully recognize brain processing of these phonemes. The recognition rates can be further improved using the scalp tangential electric field and the surface Laplacian around the auditory cortical area, which were derived from the original potential signal. The best rate for the eight initial consonants was 66.7%. Moreover, we found a distinctive phase pattern in the brain for each of these consonants. We then used these phase patterns to recognize the consonants, with a correct rate of 48.7%. In addition, in the analysis of the confusion matrices, we found significant similarity-differences were invariant between brain and perceptual representations of phonemes. These latter results supported the importance of phonological distinctive features in the neural representation of phonemes. phase synchronization | EEG classification H ow the human brain processes phonemes has been a subject of interest for linguists and neuroscientists for a long time. From the beginning of the 20th century, behavioral experiments were carried out to explore the perceptual discrimination of phonemes under various conditions (1-5). Since 1978, Mismatch Negativity in EEG (6) has been used extensively to measure the neural discrimination of phonemes (7). These results suggest the existence of a language-specific phoneme representation (8). More recent findings (9-11) using magnetoencephalograph recordings support the brain's encoding of phonological features before the lexical information is retrieved. Invasive recordings of animal neural responses to human speech also exhibited temporal and spatial characteristics, reflecting the distinctive features of phonemes (12,13). Related research has shown that animal recordings from the auditory cortex match animals' behavioral discrimination of phonemes (14) as well as the pattern of human psychological confusions (15). Functional MRI (fMRI) provides a noninvasive method to locate cortical activities of phoneme perception in healthy human brains (16). Success in recognizing fMRI evoked by isolated vowels has also been reported (17), but this work is difficult to extend because of the limited temporal resolution of fMRI.In most EEG studies of phoneme perception, the interest has been in the temporal information rather than its spectral structure. However, it has been shown that EEG rhythms may be related to a specific cognitive state of the brain (18,19). In the present study, we show that we can recognize, with some success, brain representations of specific phonemes using only the phase properties of brain waves in a narrow...

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Section: Results Of Recognizing Initial Consonants Using Temporal Andmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Using phase to recognize English phonemes and their distinctive features in the brain

Wang

Perreau-Guimaraes

Carvalhaes

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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“…BR can even be applied to detect different conscious states of the human, i.e., in his conscious perception [32]. However, more functional questions like the decoding of visual, auditory, perceptual or cognitive patterns are addressed as well [33]–[37]. For our purpose, we define BR as the passive decoding of brain activity, i.e., detection of certain brain patterns that are related to specific functional, cognitive or intentional (but not necessarily conscious) processes, which are evoked by internal or external events during human-machine interaction.…”

Section: Introductionmentioning

confidence: 99%

On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics

et al. 2013

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The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

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“…Theses about structural isomorphism between mental or neural images and objects in the world will not be examined here, because the subject is complicated and still controversial. I could not say much that would be useful in a short space (For details, see Suppes et al, 2009). I will just stipulate that I think in terms of such images and very much believe they are the "meaning" we often properly attach to a given word or phrase.…”

Section: Amentioning

confidence: 99%

Three Kinds of Meanings

Suppes¹

2012

Prospects for Meaning

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It is argued that there are at least three distinct kinds of meaning that have wide currency across many different kinds of language use. The fi rst kind consists of formal defi nitions of terms in mathematics and science. These definitions are usually clearly distinguished, as such, in the discourse context in which they occur. The second kind consists of dictionary defi nitions, familiar to all of us. The third kind, that of associative meanings, is not as widely recognized as the fi rst two, but associative meanings are at the center of our cognitive and emotional experience. Baldly stated, the thesis defended is that associations provide the computational method of computing meaning as we speak, listen, read or write about our thoughts and feelings. This claim is supported by a variety of research in psychology and neuroscience. For much of the use of this third kind of meaning, the familiar analytic-synthetic philosophical distinction is artifi cial and awkward.1. Meaning given by formal defi nition I fi rst consider defi nitions formalized within a theory in the ordinary mathematical sense. This means the language itself is not described formally but that the primitive concepts of the theory are given and any use of mathematics other than that of intuitive logical inference is explicitly stated. For simplicity here, I shall mainly refer only to theories that in principle could be formalized within fi rst-order logic, but that degree of formalization is not itself considered. This means that the only formal terms in the theory to begin with are the primitive symbols of the theoryno other mathematical notation being used. Within this framework, we then say this about the formal defi nitions within a given theory.The fi rst defi nition of the theory is a sentence of a certain form which establishes the meaning of a new term of the theory using only the primitive terms of the theory. The second defi nition in a theory is a sentence of a certain form which establishes the meaning of a second new term of the theory by using only the primitive terms and the fi rst defi ned term. And

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Partial Orders of Similarity Differences Invariant Between EEG-Recorded Brain and Perceptual Representations of Language

Cited by 13 publications

References 27 publications

Using phase to recognize English phonemes and their distinctive features in the brain

Using phase to recognize English phonemes and their distinctive features in the brain

On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics

Three Kinds of Meanings

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