Abstract
Mu/beta rhythms are well-studied brain activities that originate from sensorimotor cortices. These rhythms reveal spectral changes in alpha and beta bands induced by movements of different body parts, e.g., hands and limbs, in electroencephalography (EEG) signals. However, less can be revealed in them about movements of different fine body parts that activate adjacent brain regions, such as individual fingers from one hand. Several studies have reported spatial and temporal couplings of rhythmic activities at different frequency bands, suggesting the existence of well-defined spectral structures across multiple frequency bands. In the present study, spectral principal component analysis (PCA) was applied on EEG data, obtained from a finger movement task, to identify cross-frequency spectral structures. Features from identified spectral structures were examined in their spatial patterns, cross-condition pattern changes, detection capability of finger movements from resting, and decoding performance of individual finger movements in comparison to classic mu/beta rhythms. These new features reveal some similar, but more different spatial and spectral patterns as compared with classic mu/beta rhythms. Decoding results further indicate that these new features (91%) can detect finger movements much better than classic mu/beta rhythms (75.6%). More importantly, these new features reveal discriminative information about movements of different fingers (fine body-part movements), which is not available in classic mu/beta rhythms. The capability in decoding fingers (and hand gestures in the future) from EEG will contribute significantly to the development of non-invasive BCI and neuroprosthesis with intuitive and flexible controls.
Highlights
Rhythmic brain activities, biomarkers of many important brain functions, have been long studied with magnetic and electrical signals, i.e., magnetoencephalography (MEG) and electroencephalography (EEG)
We investigated spectral structures in noninvasive EEG during motor tasks of individual finger movements
Spectral Structures and Features The spectral structures in EEG decomposed by the principal component analysis (PCA) analysis present different profiles along frequency axis, yet consistency can be found across all channels, conditions, and subjects
Summary
Biomarkers of many important brain functions, have been long studied with magnetic and electrical signals, i.e., magnetoencephalography (MEG) and electroencephalography (EEG). These activities are believed due to aggregated neural oscillations, which suggest various brain states under either resting or tasked conditions (Steriade et al, 1990). EEG resolutions in finger decoding cortex related to, such as sleep (McKinney et al, 2011) and drowsiness (Lin et al, 2005) Another important rhythmic activity is the theta rhythm (i.e., 4–7 Hz) that is associated with memory processing when it appears in the frontal cortex (Urgen et al, 2013) and spatial navigation when in the parietal cortex (Snider et al, 2013).
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