Movement planning involves transforming the sensory goal representation into a command in motor coordinates. Surprisingly, the real-time dynamics of sensorimotor transformations at the whole brain level remain unknown, in part due to the spatiotemporal limitations of fMRI and neurophysiological recordings. Here, we used magnetoencephalography (MEG) during pro-/anti-wrist pointing to determine (1) the cortical areas involved in transforming visual signals into appropriate hand motor commands, and (2) how this transformation occurs in real time, both within and across the regions involved. We computed sensory, motor, and sensorimotor indices in 16 bilateral brain regions for direction coding based on hemispherically lateralized de/synchronization in the α (7-15Hz) and β (15-35Hz) bands. We found a visuomotor progression, from pure sensory codes in 'early' occipital-parietal areas, to a temporal transition from sensory to motor coding in the majority of parietal-frontal sensorimotor areas, to a pure motor code, in both the α and β bands. Further, the timing of these transformations revealed a top-down pro/anti cue influence that propagated 'backwards' from frontal through posterior cortical areas. These data directly demonstrate a progressive, real-time transformation both within and across the entire occipital-parietalfrontal network that follows specific rules of spatial distribution and temporal order..

Reference frame transformations are usually considered to be deterministic. However, translations, scaling or rotation angles could be stochastic. Indeed, variability of these entities often originates from noisy estimation processes. The impact of transformation noise on the statistics of the transformed signals is unknown and a quantification of these effects is the goal of this study. We first quantify analytically and numerically how stochastic reference frame transformations (SRFT) alter the posterior distribution of the transformed signals. We then propose an new empirical measure to quantify deviations from a given distribution when only limited data is available. We apply this empirical measure to an example in sensory-motor neuroscience to quantify how different head roll angles change the distribution of reach endpoints away from the normal distribution.

In this paper we propose an agglomerative hierarchical clustering Ward's algorithm in tandem with the Affinity Propagation algorithm to reliably localize active brain regions from magnetoencephalography (MEG) brain signals. Reliable localization of brain areas with MEG has been difficult due to variations in signal strength, and the spatial extent of the reconstructed activity. The proposed approach to resolve this difficulty is based on adaptive clustering on reconstructed beamformer images to find locations that are consistently active across different participants and experimental conditions with high spatial resolution. Using data from a human reaching task, we show that the method allows more accurate and reliable localization from MEG data alone without using functional magnetic resonance imaging (fMRI) or any other imaging techniques.

Planning an accurate reach involves the transformation of the neural representation of target location in sensory coordinates into a command for hand motion in motor coordinates. Although imaging techniques such as fMRI reveal the cortical topography of such transformations, and neurophysiological recordings provide local dynamics, we do not yet know the real-time dynamics of sensorimotor transformations at the whole brain level. We used high spatiotemporal resolution magnetoencephalography (MEG) during a pro-/anti-reaching task to determine (1) which brain areas are involved in transforming visual signals into appropriate motor commands for the arm, and (2) how this transformation occurs on a millisecond time scale, both within and across the regions involved. We performed time-frequency response analysis and identified 16 bilateral brain regions using adaptive hierarchical clustering (Alikhanian et al. 2013). We then computed sensory, motor, and sensorimotor indices for direction coding based on hemispherically lateralized de/synchronization in the α (7-15Hz) and β (15-35Hz) bands.

In this paper, a two phase algorithm is proposed for both blind synchronization and data sequence estimation of all users without any prior knowledge about spreading sequences in asynchronous unequal power multi-user direct sequence spread spectrum (DS-SS) systems. In the first phase, for blind synchronization, an eigenvalue variation (EV) based method is proposed, which uses all estimated eigenvalues related to signal, which are discriminated from noise eigenvalues by a threshold. In this paper, is shown EV to be a powerful tool for blind synchronization in eavesdropping scenarios in which unequal power signals are received from users. In the second phase, for blind data sequence estimation of all users, a variable step-size independent component analysis (ICA) algorithm based on negentropy maximization of active users is proposed using subspace as a preprocessing step. There is no need to know any spreading sequences for data estimation of users. Computer simulations confirm much better performance by the proposed algorithm at the cost of some more complexity compared with that of using only a pure subspace algorithm. Moreover, we compare the performance of the proposed blind synchronization with that of a successive blind synchronization, and we show that the proposed method is much faster.