Basal ganglia-cortical connectivity underlies self-regulation of brain oscillations in humans.

Brain-computer interfaces provide an artificial link by which the brain can directly interact with the environment. To achieve fine brain-computer interface control, participants must modulate the patterns of the cortical oscillations generated from the motor and somatosensory cortices. However, it remains unclear how humans regulate cortical oscillations, the controllability of which substantially varies across individuals. Here, we performed simultaneous electroencephalography (to assess brain-computer interface control) and functional magnetic resonance imaging (to measure brain activity) in healthy participants. Self-regulation of cortical oscillations induced activity in the basal ganglia-cortical network and the neurofeedback control network. Successful self-regulation correlated with striatal activity in the basal ganglia-cortical network, through which patterns of cortical oscillations were likely modulated. Moreover, basal ganglia-cortical network and neurofeedback control network connectivity correlated with strong and weak self-regulation, respectively. The findings indicate that the basal ganglia-cortical network is important for self-regulation, the understanding of which should help advance brain-computer interface technology.

Reconstruction of flexor and extensor muscle activities from electroencephalography cortical currents.

The ability to reconstruct muscle activity time series from electroencephalography (EEG) may lead to drastic improvements in brain-machine interfaces (BMIs) by providing a means for realistic continuous reproduction of dexterous movements in human beings. However, it is considered difficult to isolate signals related to individual muscle activities from EEG because EEG sensors record a mixture of signals originating from many cortical regions. Here, we challenge this assumption by reconstructing agonist and antagonist muscle activities (i.e. filtered electromyography (EMG) signals) from EEG cortical currents estimated using a hierarchical Bayesian EEG inverse method. Results of 5 volunteer subjects performing isometric right wrist flexion and extension tasks showed that individual muscle activity time series, as well as muscle activities at different force levels, were well reconstructed using EEG cortical currents and with significantly higher accuracy than when directly reconstructing from EEG sensor signals. Moreover, spatial distribution of weight values for reconstruction models revealed that highly contributing cortical sources to flexion and extension tasks were mutually exclusive, even though they were mapped onto the same cortical region. These results suggest that EEG sensor signals were reasonably isolated into cortical currents using the applied method and provide the first evidence that agonist and antagonist muscle activity time series can be reconstructed using EEG cortical currents.

Single-trial classification of vowel speech imagery using common spatial patterns.

With the goal of providing a speech prosthesis for individuals with severe communication impairments, we propose a control scheme for brain-computer interfaces using vowel speech imagery. Electroencephalography was recorded in three healthy subjects for three tasks, imaginary speech of the English vowels /a/ and /u/, and a no action state as control. Trial averages revealed readiness potentials at 200 ms after stimulus and speech related potentials peaking after 350 ms. Spatial filters optimized for task discrimination were designed using the common spatial patterns method, and the resultant feature vectors were classified using a nonlinear support vector machine. Overall classification accuracies ranged from 68% to 78%. Results indicate significant potential for the use of vowel speech imagery as a speech prosthesis controller.