Brain oscillatory activity during motor imagery in EEG-fMRI coregistration

Modulation of event-related desynchronization in robot-assisted hand performance: brain oscillatory changes in active, passive and imagined movements

Background

Robot-assisted therapy in patients with neurological disease is an attempt to improve function in a moderate to severe hemiparetic arm. A better understanding of cortical modifications after robot-assisted training could aid in refining rehabilitation therapy protocols for stroke patients. Modifications of cortical activity in healthy subjects were evaluated during voluntary active movement, passive robot-assisted motor movement, and motor imagery tasks performed under unimanual and bimanual protocols.

Methods

Twenty-one channel electroencephalography (EEG) was recorded with a video EEG system in 8 subjects. The subjects performed robot-assisted tasks using the Bi-Manu Track robot-assisted arm trainer. The motor paradigm was executed during one-day experimental sessions under eleven unimanual and bimanual protocols of active, passive and imaged movements. The event-related-synchronization/desynchronization (ERS/ERD) approach to the EEG data was applied to investigate where movement-related decreases in alpha and beta power were localized.

Results

Voluntary active unilateral hand movement was observed to significantly activate the contralateral side; however, bilateral activation was noted in all subjects on both the unilateral and bilateral active tasks, as well as desynchronization of alpha and beta brain oscillations during the passive robot-assisted motor tasks. During active-passive movement when the right hand drove the left one, there was predominant activation in the contralateral side. Conversely, when the left hand drove the right one, activation was bilateral, especially in the alpha range. Finally, significant contralateral EEG desynchronization was observed during the unilateral task and bilateral ERD during the bimanual task.

Conclusions

This study suggests new perspectives for the assessment of patients with neurological disease. The findings may be relevant for defining a baseline for future studies investigating the neural correlates of behavioral changes after robot-assisted training in stroke patients.

Zur Anwendung der Elektroenzephalographie in der Sportpsychologie

Zusammenfassung: Das Elektroenzephalogramm (EEG) ist ein geeignetes Instrument, um diejenigen bioelektrischen Vorgänge zu untersuchen, die kognitiven Prozessen oder emotionalen Zuständen zugrunde liegen, welche fundamentale Prozesse im Sport darstellen. Ziel dieses Artikels ist es, die methodischen Möglichkeiten der Elektroenzephalographie in bewegungs- und sportwissenschaftlichen Studien zu beleuchten, einen Überblick über bisherige Befunde zu geben und die Verwendung des EEGs kritisch zu bewerten. Nach einer einführenden Darstellung der Grundlagen des EEGs und der wichtigsten Analysemöglichkeiten, werden drei Gruppen von Studien diskutiert, die sich mit den EEG Korrelaten 1. grundlegende Aspekte von Bewegung und Bewegungslernen, 2. aufmerksamkeitsspezifische Veränderungen während der Bewegung und 3. affektive Veränderungen im Zusammenhang mit sportlicher Bewegung beschäftigen. Es wird die Relevanz der Elektroenzephalographie aufgezeigt und abschließend auch auf die Limitationen dieses Zuganges eingegangen.

Brain-computer interface based on generation of visual images

This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier.