Neurophysiological Correlates of Gait Retraining With Real-Time Visual and Auditory Feedback.

Most people acquire motor skills through feedback-based training. How the human brain processes sensory feedbacks during training, especially in a gait training, remain largely unclear. The purpose of this paper is to explore how humans adopt a new gait pattern to reduce impacts during walking-with the aid of visual and audio feedbacks. This paper demonstrates the features of underlying brain activity in incorporating the visual or auditory cues to acquire a new gait pattern. Electroencephalography (EEG) and peak positive acceleration (PPA) of the heel were collected from 23 participants during walking on a treadmill with no feedback, with visual feedback, or with audio feedback. The feedbacks were presented after each foot strike, where a sub-threshold PPA triggered a positive feedback (green/low-pitched), and a suprathreshold PPA triggered a negative feedback (red/high-pitched). The participants were instructed to voluntarily control their gait, so that low PPA could be achieved. This control was perturbed in some sessions by an additional cognitive task, and the influence of such distraction was also explored. The PPA was significantly lower in the sessions with visual or audio feedback than in sessions without feedback, showing an immediate improvement in gait pattern, when the feedback was provided. Different feedbacks modulated neural activities at different locations and/or levels during training. Alpha event-related synchronization (ERS) was particularly increased during the encoding of auditory feedback or the introduction of a distracting task. In the meantime, prominent frontal and posterior theta ERS were coupled with negative feedback, and strong beta event-related desynchronization (ERD) was observed only in sessions with feedbacks. Our results indicate that feedback effectively enhances motor planning when acquiring a new gait.

An evaluation of mental workload with frontal EEG.

Using a wireless single channel EEG device, we investigated the feasibility of using short-term frontal EEG as a means to evaluate the dynamic changes of mental workload. Frontal EEG signals were recorded from twenty healthy subjects performing four cognitive and motor tasks, including arithmetic operation, finger tapping, mental rotation and lexical decision task. Our findings revealed that theta activity is the common EEG feature that increases with difficulty across four tasks. Meanwhile, with a short-time analysis window, the level of mental workload could be classified from EEG features with 65%-75% accuracy across subjects using a SVM model. These findings suggest that frontal EEG could be used for evaluating the dynamic changes of mental workload.

Effective connectivity matrix for neural ensembles.

In this paper, we present an efficient framework to study the directional interactions within the multiple-input multiple-output (MIMO) biological neural network from spiketrain data. We used an efficient generalized linear model (GLM) with Laguerre basis functions to model a MIMO neural system, and developed an Effective Connectivity Matrix (ECM) to visualize excitatory and inhibitory connections within the neural network. A new causality representation was developed based on system dynamics. Statistical test was applied to identify the significance of the measured causality. We tested ECM on both common-input model and random networks. The results showed that ECM could (1) solve the common-input problem; (3) recover the causality among random neural networks with different connection probabilities and sizes of networks; and (3) identify the excitatory and inhibitory connections among neuronal populations accurately.

Cross-frequency information transfer from EEG to EMG in grasping.

This paper presents an investigation into the cortico-muscular relationship during a grasping task by evaluating the information transfer between EEG and EMG signals. Information transfer was computed via a non-linear model-free measure, transfer entropy (TE). To examine the cross-frequency interaction, TEs were computed after the times series were decomposed into various frequency ranges via wavelet transform. Our results demonstrate the capability of TE to capture the direct interaction between EEG and EMG. In addition, the cross-frequency analysis revealed instantaneous decrease in information transfer from EEG to the high frequency component of EMG (100-200Hz) during the onset of movement.

Reconstruction of neural network topology using spike train data: Small-world features of hippocampal network.

As the amount of experimental data made publicly accessible has gradually increased in recent years, it is now possible to reconsider many of the longstanding questions in neuroscience. In this paper, we present an efficient frame-work for reconstructing the functional connectivity from the spike train data curated from the Collaborative Research in Computational Neuroscience (CRCNS) program. We used a modified generalized linear model (GLM) framework with L1 norm penalty to investigate 10 datasets. These datasets contain spike train data collected from the hippocampal region of rats performing various tasks. Analysis of the reconstructed network showed that the neural network in the hippocampal region of well-trained rats demonstrated significant small-world features.