Cortical Imaging of Event-Related (de)Synchronization During Online Control of Brain-Computer Interface Using Minimum-Norm Estimates in Frequency Domain

Abstract

It is of wide interest to study the brain activity that correlates to the control of Brain-Computer Interface (BCI). In the present study, we have developed an approach to image the cortical rhythmic modulation associated with motor imagery using minimum-norm estimates in the frequency domain (MNEFD). The distribution of cortical sources of mu activity during online control of BCI was obtained with the MNEFD. Contralateral decrease (event-related desynchronization, ERD) and ipsilateral increase (event-related synchronization, ERS) are localized in the sensorimotor cortex during online control of BCI in a group of human subjects. Statistical source analysis revealed that maximum correlation with movement imagination is localized in sensorimotor cortex.

The impact of mind-body awareness training on the early learning of a brain-computer interface

“…Two additional measures based on the EEG data analysis — the average scalp R 2 value and SPD were used to quantitatively evaluate the neural correlates of subject performance. The average R 2 value is a topographical measure of the correlation between movement imagination and mu rhythm source activities 15,23,24,28 . Specifically, R 2 was calculated as the total proportion of variance in mu rhythm amplitude between the two imagination state pairings that was due to target position, and based on single-trial source estimates.…”

Brain–Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives

“…Source imaging studies of SMR [39, 40] have revealed that movement or motor imagery of different body parts were associated with decrease in SMR from regions along the primary sensorimotor cortex corresponding to different body parts, known as the Homunculus. Such characteristic changes in EEG sensorimotor rhythms can be used to classify brain states relating to the planning/imagining of different types of limb movement, which forms the basis of neural control in SMR-based BCIs [27, 41]. …”

“…Thus, in order to optimize the spatial information, the channels used for BCI control are usually a selected subset of a few channels. These can be selected by comparing discriminative features [17], by subspace decomposition method [121, 123], or based on a priori knowledge of the functional organization of the relevant cortical area(s) [27, 39]. …”

“…the degree of freedom (DOF), is a key attribute and to a large extent determines the functions that the BCI can execute. Studies have shown that BCIs can achieve independent multi-dimensional control, which can be utilized in many rehabilitation applications, such as modulating environment [75, 94], communication [25-27, 31], orthosis control [77] and navigation in two-dimensional (2D) [78] or three-dimensional (3D) space [19], etc. (Fig.…”

“…Studies have demonstrated that people can learn to increase and decrease the amplitude of sensorimotor rhythm using mental strategy of motor imagery, and thereby control physical or virtual devices (e.g. [15, 17, 22, 23, 25-27]).…”