Alpha band functional connectivity correlates with the performance of brain–machine interfaces to decode real and imagined movements

Abstract

Brain signals recorded from the primary motor cortex (M1) are known to serve a significant role in coding the information brain–machine interfaces (BMIs) need to perform real and imagined movements, and also to form several functional networks with motor association areas. However, whether functional networks between M1 and other brain regions, such as these motor association areas, are related to the performance of BMIs is unclear. To examine the relationship between functional connectivity and performance of BMIs, we analyzed the correlation coefficient between performance of neural decoding and functional connectivity over the whole brain using magnetoencephalography. Ten healthy participants were instructed to execute or imagine three simple right upper limb movements. To decode the movement type, we extracted 40 virtual channels in the left M1 via the beam forming approach, and used them as a decoding feature. In addition, seed-based functional connectivities of activities in the alpha band during real and imagined movements were calculated using imaginary coherence. Seed voxels were set as the same virtual channels in M1. After calculating the imaginary coherence in individuals, the correlation coefficient between decoding accuracy and strength of imaginary coherence was calculated over the whole brain. The significant correlations were distributed mainly to motor association areas for both real and imagined movements. These regions largely overlapped with brain regions that had significant connectivity to M1. Our results suggest that use of the strength of functional connectivity between M1 and motor association areas has the potential to improve the performance of BMIs to perform real and imagined movements.

Common neural correlates of real and imagined movements contributing to the performance of brain–machine interfaces

“…Many previous studies have used invasive or non-invasive measurements to demonstrate the importance of cM1 activity for decoding movement types, directions, and trajectories during real and imagined movements 6 7 20 34 . Recently, we demonstrated the effect of functional connectivity between cM1 and motor association areas on BMI performance during real and imagined movements using MEG 31 . To the best of our knowledge, no previous studies have investigated the relationship between cM1 activity representing motor information in real and imagined movements with high spatiotemporal resolution using non-invasive measurements.…”

“…Some recent studies that used invasive 26 or non-invasive methods 27 28 29 30 also reported that imagined movements activate M1. In addition, we recently reported that the strength of functional connectivities between M1 and the motor association area affects the performance of BMIs in both real and imagined movements 31 . Furthermore, M1 activity during imagined movements has been recorded not only in healthy subjects but also in patients with stroke 21 , tetraplegia 6 7 32 , and ALS 33 .…”

Spatiotemporal neural correlates of brain-computer interface learning

“…anxiety) or demographical items to predict BCI performance, but the associated results seem to be contradictory (55; 56; 57). Recently, FC-based metrics have been shown to correlate with the user's performance suggesting potential strategies for improving MI-based BCI accuracy (58).…”

Neurofeedback training of alpha-band coherence enhances motor performance

“…Based on this observation, we hypothesized that a treatment capable of increasing alpha-band FC between the motor cortex and the rest of the brain should have beneficial effects on motor performance. Recent studies have indeed shown that traditional neurofeedback training of SMR modulation improves motor cortex FC as a marker of recovery (Várkuti et al, 2013;Sugata et al, 2014). Here, we developed a neurofeedback system where EEG FC is trained directly (Sacchet et al, 2012).…”