Decoding and Cortical Source Localization for Intended Movement Direction With MEG

Abstract

Wang W, Sudre GP, Xu Y, Kass RE, Collinger JL, Degenhart AD, Bagic AI, Weber DJ. Decoding and cortical source localization for intended movement direction with MEG. J Neurophysiol 104: 2451-2461. First published August 25, 2010 doi:10.1152/jn.00239.2010. Magnetoencephalography (MEG) enables a noninvasive interface with the brain that is potentially capable of providing movement-related information similar to that obtained using more invasive neural recording techniques. Previous studies have shown that movement direction can be decoded from multichannel MEG signals recorded in humans performing wrist movements. We studied whether this information can be extracted without overt movement of the subject, because the targeted users of brain-controlled interface (BCI) technology are those with severe motor disabilities. The objectives of this study were twofold: 1) to decode intended movement direction from MEG signals recorded during the planning period before movement onset and during imagined movement and 2) to localize cortical sources modulated by intended movement direction. Ten able-bodied subjects performed both overt and imagined wrist movement while their cortical activities were recorded using a whole head MEG system. The intended movement direction was decoded using linear discriminant analysis and a Bayesian classifier. Minimum current estimation (MCE) in combination with a bootstrapping procedure enabled source-space statistical analysis, which showed that the contralateral motor cortical area was significantly modulated by intended movement direction, and this modulation was the strongest ϳ100 ms before the onset of overt movement. These results suggest that it is possible to study cortical representation of specific movement information using MEG, and such studies may aid in presurgical localization of optimal sites for implanting electrodes for BCI systems.

Accessing and Processing MEG Signals in Real-Time: Emerging Applications and Enabling Technologies

“…J. Miller et al 2007;Wang et al 2009). In addition, Wang et al (Wang, Sudre, et al 2010) demonstrated that high-gamma band activity captured by MEG showed directional modulation similar to what was observed previously using invasive recordings in humans (ECoG) (Leuthardt et al 2004) and non-human primates (local field potentials) (Heldman et al 2006) and has been used for BMI control. Recently, Mellinger et al demonstrated that real-time MEG processing could provide control over a computer cursor in one-dimension with the individuals modulating their sensorimotor rhythms (mu and beta frequency bands) using imagined hand and feet movements (Mellinger et al 2007).…”

“…It was also demonstrated that movement-related information could be decoded accurately from MEG signals (Georgopoulos et al 2005;Waldert et al 2008;Wang, Sudre, et al 2010). Figure 1 shows the time-frequency responses of a contralateral MEG sensor (a gradiometer) when a participant performed simple center-out wrist movements (Wang, Sudre, et al 2010). There is a clear decrease in power for the low frequency sensorimotor rhythm (10-30 Hz) and a distinct increase in power for the high-gamma band (60-200 Hz) during movement.…”

“…ECoG) in terms of source localization accuracy and capability to resolve cortical activity represented by amplitudes of different frequency bands (Dalal et al 2008;Korvenoja et al 2006;Gharib et al 1995). It was also demonstrated that movement-related information could be decoded accurately from MEG signals (Georgopoulos et al 2005;Waldert et al 2008;Wang, Sudre, et al 2010). Figure 1 shows the time-frequency responses of a contralateral MEG sensor (a gradiometer) when a participant performed simple center-out wrist movements (Wang, Sudre, et al 2010).…”

Decoding perceptual thresholds from MEG/EEG

“…Several groups [2]- [4] showed that movement direction can be predicted during overt and imagined movements. Another group [5] decoded object category membership for animacy, naturalness, faces versus bodies or human versus nonhuman faces/bodies.…”

Regularized logistic regression and multiobjective variable selection for classifying MEG data

“…They used a stepwise LDA fed with data relative to the first 800 ms of the signal following the visual stimulations. Wang et al (2010) performed dimension reduction and MEG data transformation using an LDA that maximized linear discrimination among different movement directions.…”