The principle of coherence in multi-level brain information processing.

Synchronisation has become one of the major scientific tools to explain biological order at many levels of organisation. In systems neuroscience, synchronised subthreshold and suprathreshold oscillatory neuronal activity within and between distributed neuronal assemblies is acknowledged as a fundamental mode of neuronal information processing. Coherent neuronal oscillations correlate with all basic cognitive functions, mediate local and long-range neuronal communication and affect synaptic plasticity. However, it remains unclear how the very fast and complex changes of functional neuronal connectivity necessary for cognition, as mediated by dynamic patterns of neuronal synchrony, could be explained exclusively based on the well-established synaptic mechanisms. A growing body of research indicates that the intraneuronal matrix, composed of cytoskeletal elements and their binding proteins, structurally and functionally connects the synapses within a neuron, modulates neurotransmission and memory consolidation, and is hypothesised to be involved in signal integration via electric signalling due to its charged surface. Theoretical modelling, as well as emerging experimental evidence indicate that neuronal cytoskeleton supports highly cooperative energy transport and information processing based on molecular coherence. We suggest that long-range coherent dynamics within the intra- and extracellular filamentous matrices could establish dynamic ordered states, capable of rapid modulations of functional neuronal connectivity via their interactions with neuronal membranes and synapses. Coherence may thus represent a common denominator of neurophysiological and biophysical approaches to brain information processing, operating at multiple levels of neuronal organisation, from which cognition may emerge as its cardinal manifestation.

Identification of the phase code in an EEG during gripping-force tasks: a possible alternative approach to the development of the brain-computer interfaces.

BACKGROUND: The subject of brain-computer interfaces (BCIs) represents a vast and still mainly undiscovered land, but perhaps the most interesting part of BCIs is trying to understand the information exchange and coding in the brain itself. According to some recent reports, the phase characteristics of the signals play an important role in the information transfer and coding. The mechanism of phase shifts, regarding the information processing, is also known as the phase coding of information. OBJECTIVE: The authors would like to show that electroencephalographic (EEG) signals, measured during the performance of different gripping-force control tasks, carry enough information for the successful prediction of the gripping force, as applied by the subjects, when using a methodology based on the phase demodulation of EEG data. Since the presented methodology is non-invasive it could be used as an alternative approach for the development of BCIs. MATERIALS AND METHODS: In order to predict the gripping force from the EEG signals we used a methodology that uses subsequent signal processing methods: simplistic filtering methods, for extracting the appropriate brain rhythm; principal component analysis, for achieving the linear independence and detecting the source of the signal; and the phase-demodulation method, for extracting the phase-coded information about the gripping force. A fuzzy inference system is then used to predict the gripping force from the processed EEG data. RESULTS: The proposed methodology has clearly demonstrated that EEG signals carry enough information for a successful prediction of the subject's performance. Moreover, a cross-validation showed that information about the gripping force is encoded in a very similar way between the subjects tested. As for the development of BCIs, considering the computational time to pre-process the data and train the fuzzy model, a real-time online analysis would be possible if the real-time non-causal limitations of the methodology could be overcome. CONCLUSION: The study has shown that phase coding in the human brain is a possible mechanism for information coding or transfer during visuo-motor tasks, while the phase-coded content about the gripping forces can be successfully extracted using the phase-demodulation approach. Since the methodology has proven to be appropriate for the case of this study it could also be used as an alternative approach for the development of BCIs for similar tasks.

Using ANNs to predict a subject's response based on EEG traces.

Numerous reports have shown that performing working-memory tasks causes an elevated rhythmic coupling in different areas of the brain; it has been suggested that this indicates information exchange. Since the information exchanged is encoded in brain waves and measurable by electroencephalography (EEG) it is reasonable to assume that it can be extracted with an appropriate method. In our study we made an attempt to extract the information using an artificial neural network (ANN), which can be considered as a stimulus-response model with a state observer. The EEG was recorded from three subjects while they performed a modified Sternberg task that required them to respond to each task with the answer "true" or "false". The study revealed that a stimulus-response model can successfully be identified by observing phase-demodulated theta-band EEG signals 1 s prior to a subject's answer. The results also showed that it was possible to predict the answers from the EEG signals with an average reliability of 75% for all the subjects. From this we concluded that it is possible to observe the system states and thus predict the correct answer using the EEG signals as inputs.

Gripping-force identification using EEG and phase-demodulation approach.

In this paper we investigate the fuzzy identification of brain-code during simple gripping-force control tasks. Since the synchronized oscillatory activity and the phase dynamics between the brain areas are two important mechanisms in the brain's function and information transfer, we decided to examine whether it is possible to extract the encoded information from the EEG signals using the phase-demodulation approach. The EEG was measured during the performance of different visuomotor tasks and the information we were trying to decode was the gripping force as applied by the subjects. The study revealed that it is possible, by using simple beta-rhythm filtering, phase demodulation, principal component analysis and a fuzzy model, to estimate the gripping-force response by using EEG signals as the inputs for the proposed model. The presented study has shown that even though EEG signals represent a superposition of all the active neurons, it is still possible to decode some information about the current activity of the brain centers. Furthermore, the cross-validation showed that the information about the gripping force is encoded in a very similar way for all the examined subjects. Thus, the phase shifts of the EEG signals seem to have a key role during activity and information transfer in the brain, while the phase-demodulation method proved to be a crucial step in the signal processing.