Dheeraj Rathee

Previous studies have demonstrated that musical deviants (syntactically irregular chords) elicit event related potentials/fields with negative polarity; specifically, the early right anterior negativity and the right anterior temporal negativity responses with peak latencies at ~200 ms and ~350 ms, respectively, post stimulus onset. Here, we investigated differences in the neural dynamics of the auditory perceptual system of individuals with music training compared to those with no music training. Magnetoencephalography was used to examine the neural response to a deviant sound when the auditory system was primed using stimulus entrainment to evoke an auditory gamma-band response between 31 Hz and 39 Hz, in 2 Hz steps. Participants responded to the harmonic relationship between the entrainment stimulus and the subsequent target stimulus. Gamma frequencies carry stimulus information; thus, the paradigm primed the auditory system with a known gamma frequency and evaluated any improvement in the brains response to a deviant stimulus. The entrainment stimuli did not elicit an early right anterior negativity response. Furthermore, the source location of the event-related field difference during the later right anterior temporal negativity response time-window varied depending on group and entrainment condition. In support of previous findings from research using this, and a functionally similar visual-priming paradigm, a 7 Hz phase modulation of gamma amplitude was found for non-musicians following 33 Hz stimulus entrainment. Overall, significant effects of gamma entrainment were found more frequently in the non-music brain. By contrast, musicians demonstrated a greater range of interactions with slower brain rhythms, indicative of increased top-down control. Conclusion Musicians’ auditory cognition relies more on top-down processes, while non-musicians rely on bottom-up processing, and therefore, their auditory cognition is facilitated by the entrained gamma-band response. First neural evidence in support of faster reaction-time responses due to an interaction in phase of an entrained gamma-band response of 33 Hz and a slower endogenous theta rhythm (~7 Hz) - consistently reported in previous research.

Brain-machine interface (BMI) driven robot-assisted neurorehabilitation intervention has demonstrated improvement in upper-limb (UL) motor function, specifically, with post-stroke hemiparetic patients. However, neurophysiological patterns related to such interventions are not well understood. This study examined the longitudinal changes in band-limited resting-state (RS) functional connectivity (FC) networks in association with post-stroke UL functional recovery achieved by a multimodal intervention involving motor attempt (MA) based BMI and robotic hand-exoskeleton. Four adults were rehabilitated with the intervention for a period lasting upto 6 weeks. RS magnetoencephalography (MEG) signals, Action Research Arm Test (ARAT), and grip strength (GS) measures were recorded at five equispaced sessions over the intervention period. An average post-interventional increase of 100.0% (p=0.00028) and 88.0% were attained for ARAT and GS, respectively. A cluster-based statistical test involving correlation estimates between beta-band (15–26 Hz) RS-MEG FCs and UL functional recovery provided positively correlated sub-networks in both contralesional and ipsilesional motor cortices. The fronto-parietal FC exhibited hemispheric lateralisation wherein majority of the positively and negatively correlated connections were found in contralesional and ipsilesional hemispheres, respectively. Our findings are consistent with the theory of bilateral motor cortical association with UL recovery and predict novel FC patterns that can be important for higher level cognitive functions.

Objective . The majority of the current approaches of connectivity based brain–computer interface (BCI) systems focus on distinguishing between different motor imagery (MI) tasks. Brain regions associated with MI are anatomically close to each other, hence these BCI systems suffer from low performances. Our objective is to introduce single-trial connectivity feature based BCI system for cognition imagery (CI) based tasks wherein the associated brain regions are located relatively far away as compared to those for MI. Approach . We implemented time-domain partial Granger causality (PGC) for the estimation of the connectivity features in a BCI setting. The proposed hypothesis has been verified with two publically available datasets involving MI and CI tasks. Main results . The results support the conclusion that connectivity based features can provide a better performance than a classical signal processing framework based on bandpass features coupled with spatial filtering for CI tasks, including word generation, subtraction, and spatial navigation. These results show for the first time that connectivity features can provide a reliable performance for imagery-based BCI system. Significance . We show that single-trial connectivity features for mixed imagery tasks (i.e. combination of CI and MI) can outperform the features obtained by current state-of-the-art method and hence can be successfully applied for BCI applications.

The objective is to evaluate the impact of EEG referencing schemes and spherical surface Laplacian (SSL) methods on the classification performance of motor-imagery (MI)-related brain-computer interface systems. Two EEG referencing schemes: common referencing and common average referencing and three surface Laplacian methods: current source density (CSD), finite difference method, and SSL using realistic head model were implemented separately for pre-processing of the EEG signals recorded at the scalp. A combination of filter bank common spatial filter for features extraction and support vector machine for classification was used for both pairwise binary classifications and four-class classification of MI tasks. The study provides three major outcomes: 1) the CSD method performs better than CR, providing a significant improvement of 3.02% and 5.59% across six binary classification tasks and four-class classification task, respectively; 2) the combination of a greater number of channels at the pre-processing stage as compared with the feature extraction stage yields better classification accuracies for all the Laplacian methods; and 3) the efficiency of all the surface Laplacian methods reduced significantly in the case of a fewer number of channels considered during the pre-processing.