Single-trial analysis of post-movement MEG beta synchronization using independent component analysis (ICA)

The human brain ∼20 Hz rhythm measured by electroencephalography (EEG) and magnetoencephalography (MEG) has been used as a clinical examination index of motor function which originates in the anterior bank of the central sulcus in the human brain. In human voluntary movement, it is composed of three phases, planning, execution and recovery which has been suggested that localized event-related alpha desynchronization (ERD) upon movement can be viewed as an EEG/MEG correlate of an activated cortical motor network, servicing planning and execution, while beta event-related synchronization (ERS)may reflect deactivation/inhibition during the recovery phase in the underlying cortical network. The single-trial detection of ∼20 Hz rhythm is challenged because of its low signal amplitude and its signal-to-noise ration (SNR) in EEG/MEG measured neural activities. This present study proposes a method based on independent component analysis (ICA) for extraction of the sensorimotor rhythm from magnetoencephalography (MEG) measurements of right finger lifting task in a single trail. ICA decomposes a single trial recording into a set of temporal independent components (IC) and corresponding spatial maps in which the task-related components are selected by visual inspection. Pertinent ICs are then selected by visual inspection to reconstruct task-related components beta oscillatory activity which is then subjected to beta rebound quantification and source estimation in further analyses. Since the event-related oscillatory activity of human brain is related to subject´s-related oscillatory activity of human brain is related to subject´s performance and state, the ICA-based single trial method enables the possibility of studying a single-trial, which in turn may shed light on the intricate dynamics of the brain.