Analysis of MEG signals for selective arithmetic tasks

Peyton, Graham
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A magnetoencephalogram (MEG) is a non-invasive tool for measuring neuronal activity with millisecond temporal resolution. In this study, MEG measurements were recorded as a subject carried out a simple, repetitive, numerical task: deciding whether a number is even or odd. Signal processing techniques were applied to the MEG data so as to characterise the spatial and temporal dynamics of the brain during the decision-making process. The data is first preprocessed using Independent Component Analysis (ICA) and other semiautomated methods. The data is then segmented into trials. Evoked fields or event-related fields (ERFs), the classical measure of brain activity, are found by averaging all the trials in the time domain. These responses are typically phase locked to the stimulus. Induced potentials or oscillatory rhythms that are not necessarily phase-locked to the stimulus are found by averaging the time-frequency representations (TFRs) over all the trials. The TFRs were found using the Wavelet Transform. The results show that typical ERF components are present just after the onset of each stimulus. These waveforms indicate that the following sequence of cognitive events occur: mental matching of the stimulus with previously experienced stimuli (N100); higher-order perceptual processing modulated by attention (P200); and “Go-NoGo” control procedure which initiates or inhibits the motor response (N200). The P200 response also indicates that parity information may be retrieved directly from memory rather than being extracted by means of a mental calculation strategy. Time-frequency plots of the data show pronounced synchronisation in the beta-band as the subject is actively concentrating on the mental task. Thereafter, beta band desynchronisation occurs as the motor response is carried out. Activity is pronounced in the left general interpretive area with a latency of around 650ms. This confirms the fact that the brain is lateralised according to function. One important avenue for further research would be to explore source reconstruction using beamforming techniques. This would enable researchers to pinpoint neuronal sources with greater accuracy. Furthermore, functional connectivity analysis may be a useful means of elucidating how information is transmitted and integrated across brain networks. Overall, there is much scope for future work.
Magnetoencephalography, Signal processing