Gripping-force identification using EEG and phase-demodulation approach

Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task

We propose a framework for building electrophysiological predictors of single-trial motor performance variations, exemplified for SVIPT, a sequential isometric force control task suitable for hand motor rehabilitation after stroke. Electroencephalogram (EEG) data of 20 subjects with mean age of 53 years was recorded prior to and during 400 trials of SVIPT. They were executed within a single session with the non-dominant left hand, while receiving continuous visual feedback of the produced force trajectories. The behavioral data showed strong trial-by-trial performance variations for five clinically relevant metrics, which accounted for reaction time as well as for the smoothness and precision of the produced force trajectory. 18 out of 20 tested subjects remained after preprocessing and entered offline analysis. Source Power Comodulation (SPoC) was applied on EEG data of a short time interval prior to the start of each SVIPT trial. For 11 subjects, SPoC revealed robust oscillatory EEG subspace components, whose bandpower activity are predictive for the performance of the upcoming trial. Since SPoC may overfit to non-informative subspaces, we propose to apply three selection criteria accounting for the meaningfulness of the features. Across all subjects, the obtained components were spread along the frequency spectrum and showed a variety of spatial activity patterns. Those containing the highest level of predictive information resided in and close to the alpha band. Their spatial patterns resemble topologies reported for visual attention processes as well as those of imagined or executed hand motor tasks. In summary, we identified subject-specific single predictors that explain up to 36% of the performance fluctuations and may serve for enhancing neuroergonomics of motor rehabilitation scenarios.

Nonlinear EEG decoding based on a particle filter model

While the world is stepping into the aging society, rehabilitation robots play a more and more important role in terms of both rehabilitation treatment and nursing of the patients with neurological diseases. Benefiting from the abundant contents of movement information, electroencephalography (EEG) has become a promising information source for rehabilitation robots control. Although the multiple linear regression model was used as the decoding model of EEG signals in some researches, it has been considered that it cannot reflect the nonlinear components of EEG signals. In order to overcome this shortcoming, we propose a nonlinear decoding model, the particle filter model. Two- and three-dimensional decoding experiments were performed to test the validity of this model. In decoding accuracy, the results are comparable to those of the multiple linear regression model and previous EEG studies. In addition, the particle filter model uses less training data and more frequency information than the multiple linear regression model, which shows the potential of nonlinear decoding models. Overall, the findings hold promise for the furtherance of EEG-based rehabilitation robots.

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.