Temporal dynamics of sensorimotor integration in speech perception and production: independent component analysis of EEG data

Alpha-band oscillations are the dominant oscillations in the human brain and recent evidence suggests that they have an inhibitory function. Nonetheless, there is little doubt that alpha-band oscillations also play an active role in information processing. In this article, I suggest that alpha-band oscillations have two roles (inhibition and timing) that are closely linked to two fundamental functions of attention (suppression and selection), which enable controlled knowledge access and semantic orientation (the ability to be consciously oriented in time, space, and context). As such, alpha-band oscillations reflect one of the most basic cognitive processes and can also be shown to play a key role in the coalescence of brain activity in different frequencies.

Despite intensive work on language-brain relations, and a fairly impressive accumulation of knowledge over the last several decades, there has been little progress in developing large-scale models of the functional anatomy of language that integrate neuropsychological, neuroimaging, and psycholinguistic data. Drawing on relatively recent developments in the cortical organization of vision, and on data from a variety of sources, we propose a new framework for understanding aspects of the functional anatomy of language which moves towards remedying this situation. The framework posits that early cortical stages of speech perception involve auditory fields in the superior temporal gyrus bilaterally (although asymmetrically). This cortical processing system then diverges into two broad processing streams, a ventral stream, which is involved in mapping sound onto meaning, and a dorsal stream, which is involved in mapping sound onto articulatory-based representations. The ventral stream projects ventro-laterally toward inferior posterior temporal cortex (posterior middle temporal gyrus) which serves as an interface between sound-based representations of speech in the superior temporal gyrus (again bilaterally) and widely distributed conceptual representations. The dorsal stream projects dorso-posteriorly involving a region in the posterior Sylvian fissure at the parietal-temporal boundary (area Spt), and ultimately projecting to frontal regions. This network provides a mechanism for the development and maintenance of "parity" between auditory and motor representations of speech. Although the proposed dorsal stream represents a very tight connection between processes involved in speech perception and speech production, it does not appear to be a critical component of the speech perception process under normal (ecologically natural) listening conditions, that is, when speech input is mapped onto a conceptual representation. We also propose some degree of bi-directionality in both the dorsal and ventral pathways. We discuss some recent empirical tests of this framework that utilize a range of methods. We also show how damage to different components of this framework can account for the major symptom clusters of the fluent aphasias, and discuss some recent evidence concerning how sentence-level processing might be integrated into the framework.

An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a projection pursuit index. Parameterized probability distributions that have sub- and supergaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub- and supergaussian regimes. We demonstrate that the extended infomax algorithm is able to separate 20 sources with a variety of source distributions easily. Applied to high-dimensional data from electroencephalographic recordings, it is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.