Combining Different Tools for EEG Analysis to Study the Distributed Character of Language Processing

Recent studies on language processing indicate that language cognition is better understood if assumed to be supported by a distributed intelligent processing system enrolling neurons located all over the cortex, in contrast to reductionism that proposes to localize cognitive functions to specific cortical structures. Here, brain activity was recorded using electroencephalogram while volunteers were listening or reading small texts and had to select pictures that translate meaning of these texts. Several techniques for EEG analysis were used to show this distributed character of neuronal enrollment associated with the comprehension of oral and written descriptive texts. Low Resolution Tomography identified the many different sets (s_i) of neurons activated in several distinct cortical areas by text understanding. Linear correlation was used to calculate the information H(e_i) provided by each electrode of the 10/20 system about the identified s_i. H(e_i) Principal Component Analysis (PCA) was used to study the temporal and spatial activation of these sources s_i. This analysis evidenced 4 different patterns of H(e_i) covariation that are generated by neurons located at different cortical locations. These results clearly show that the distributed character of language processing is clearly evidenced by combining available EEG technologies.

The brain as a distributed intelligent processing system: an EEG study

Background

Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS.

Methodology and Principal Findings

In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence.

Conclusion

The present results support these claims and the neural efficiency hypothesis.

Integrating face and gait for human recognition at a distance in video

This paper introduces a new video-based recognition method to recognize noncooperating individuals at a distance in video who expose side views to the camera. Information from two biometrics sources, side face and gait, is utilized and integrated for recognition. For side face, an enhanced side-face image (ESFI), a higher resolution image compared with the image directly obtained from a single video frame, is constructed, which integrates face information from multiple video frames. For gait, the gait energy image (GEI), a spatio-temporal compact representation of gait in video, is used to characterize human-walking properties. The features of face and gait are obtained separately using the principal component analysis and multiple discriminant analysis combined method from ESFI and GEI, respectively. They are then integrated at the match score level by using different fusion strategies. The approach is tested on a database of video sequences, corresponding to 45 people, which are collected over seven months. The different fusion methods are compared and analyzed. The experimental results show that: 1) the idea of constructing ESFI from multiple frames is promising for human recognition in video, and better face features are extracted from ESFI compared to those from the original side-face images (OSFIs); 2) the synchronization of face and gait is not necessary for face template ESFI and gait template GEI; the synthetic match scores combine information from them; and 3) an integrated information from side face and gait is effective for human recognition in video.

Language plasticity revealed by electroencephalogram mapping

Reasoning is the result of the computations made by intelligent systems, for instance those in the brain. It is not an abstract concept because calculations performed by computations are very concrete transactions among the different central processing unit components. Entropy measurements are proposed here to disclose the plasticity of the cerebral processing associated with language comprehension in video game playing. It is also assumed that entropy may be evaluated from the correlation coefficients obtained for the game event-related activity calculated for the different electroencephalogram derivations in the 10/20 system. The brain mapping derived from these entropy measurements clearly demonstrates the reallocation of speech functions to right brain areas when the classic left language circuits are damaged during prenatal life.

N-methyl-D-aspartate channel and consciousness: from signal coincidence detection to quantum computing

Research on Blindsight, Neglect/Extinction and Phantom limb syndromes, as well as electrical measurements of mammalian brain activity, have suggested the dependence of vivid perception on both incoming sensory information at primary sensory cortex and reentrant information from associative cortex. Coherence between incoming and reentrant signals seems to be a necessary condition for (conscious) perception. General reticular activating system and local electrical synchronization are some of the tools used by the brain to establish coarse coherence at the sensory cortex, upon which biochemical processes are coordinated. Besides electrical synchrony and chemical modulation at the synapse, a central mechanism supporting such a coherence is the N-methyl-D-aspartate channel, working as a 'coincidence detector' for an incoming signal causing the depolarization necessary to remove Mg(2+), and reentrant information releasing the glutamate that finally prompts Ca(2+) entry. We propose that a signal transduction pathway activated by Ca(2+) entry into cortical neurons is in charge of triggering a quantum computational process that accelerates inter-neuronal communication, thus solving systemic conflict and supporting the unity of consciousness.

The brain as a symbol-processing machine

The knowledge accumulated about the biochemistry of the synapsis in the last decades completely changes the notion of brain processing founded exclusively over an electrical mechanism, toward that supported by a complex chemical message exchange occurring both locally, at the synaptic site, as well as at other localities, depending on the solubility of the involved chemical substances in the extracellular compartment. These biochemical transactions support a rich symbolic processing of the information both encoded by the genes and provided by actual data collected from the surrounding environment, by means of either special molecular or cellular receptor systems. In this processing, molecules play the role of symbols and chemical affinity shared by them specifies the syntax for symbol manipulation in order to process and to produce chemical messages. In this context, neurons are conceived as message-exchanging agents. Chemical strings are produced and stored at defined places, and ionic currents are used to speed up message delivery. Synaptic transactions can no longer be assumed to correspond to a simple process of propagating numbers powered by a factor measuring the presynaptic capacity to influence the postsynaptic electrical activity, but they must be modeled by more powerful formal tools supporting both numerical and symbolic calculations. It is proposed here that formal language theory is the adequate mathematical tool to handle such symbolic processing. The purpose of the present review is therefore: (a) to discuss the relevant and recent literature about trophic factors, signal transduction mechanisms, neuromodulators and neurotransmitters in order (b) to point out the common features of these correlated processes; and (c) to show how they may be organized into a formal model supported by the theory of fuzzy formal languages (d) to model the brain as a distributed intelligent problem solver.