

Researchers discover method to detect motor-related brain activity

The left side of the illustration shows the group's experimental study and task. Participants squeeze their hand into a fist after an audio command (zero seconds) and hold it squeezed until given a second audio command (approximately five seconds). Electroencephalography (EEG) and electromyography (EMG) signals are recorded to relate the brain and muscle activity. The right side of the illustration shows the results of the executed movements classification.

Motor-related brain activity, particularly its accurate detection, quantification and classification capabilities, is of great interest to researchers. They are searching for a better way to help patients with cognitive or motor impairments or to improve neurorehabilitation for patients with nervous system injuries. There is a close relationship between motor and cognitive activity of the human brain, and suppression of the specific rhythmic activity of neurons within the sensorimotor cortex of the brain—known as the mu-rhythm (8 to 14 hertz)—is a biological marker of motor-related brain activity. Studies indicate this feature of motor-related brain activity suffers from intra- and inter-subject variability when using traditional methods to explore it, such as time-frequency analysis, spatial filtering and machine learning.


The group's work confirms, for the first time, the neuronal dynamics within the sensorimotor brain area underlying the motor functions of the human brain can be simplified. "We demonstrated this using the RQA toolbox, which is fundamentally different from traditional methods for quantification of motor-related brain activity," Frolov said. "We also demonstrated that RQA measures of complexity are well suited to detect and classify motor tasks." These results show potential for developing efficient methods for classifying brain states.



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